The ERP characteristics in the process of hazard identification

The ERP characteristics in the process of hazard identification | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The ERP characteristics in the process of hazard identification Shu Zhang, Shanhong Tang, Shufen Ye, Xiuzhi Shi, Yan Zhang, Bing Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6434600/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract The prevention of accidents is aided by having a strong ability to identify hazards. The objective and quantitative assessment of hazard identification ability through event-related potential (ERP) experiments is of significance for person-job safety matching in high-risk positions. In this study, we first designed and conducted an electroencephalogram (EEG) experiment related to the hazard identification process. Subsequently, two indicators reflecting the hazard identification process were extracted from the behavioral data obtained during the experiment: hazard identification speed and hazard identification accuracy. Finally, time-domain and frequency-domain analysis methods were employed to investigate the ERP characteristics and patterns in the hazard identification process. The results showed that: (1) the low and high hazard identification accuracy groups (L-HIA and H-HIA) demonstrated significantly different N100 and P200 components, as well as beta, theta, and alpha power; (2) the fast and slow hazard identification speed groups (F-HIS and S-HIS) demonstrated significantly different N100, P200, and P300 components and beta power; (3) the average power value of theta wave in the central frontal region (P low 1.99 µV²) can be used as the grading standard for hazard identification accuracy; (4) the average peak voltage value of the P300 component in the occipital region (U fast 5.67 µV) can be used as the grading standard for hazard identification speed. It’s conducive for enterprises and individuals to master the hazard identification ability of employee to train and improve their ability pointedly. Biological sciences/Neuroscience Biological sciences/Psychology Health sciences/Biomarkers Health sciences/Risk factors Hazard identification Event-related potential technique Hazard identification accuracy Hazard identification speed Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 introduction Amid rapid societal development, individuals encounter increasingly complex hazards and risks, the rapid and effective identification of hazards in complex environments is a crucial first step in accident prevention. An empirical study conducted in the United Kingdom revealed that 33.5% of safety hazards in workplaces remain unrecognized 1 . Similarly, field inspections in the United States have shown that over 40% of safety risks go unnoticed 2 . Furthermore, in the construction sites of Australia, more than 57% of potential hazards remain undetected 3 . A recent scaffolding collapse on October 9, 2023, in Guangdong Province, China, tragically illustrated this point: six workers engaged in concrete pouring were impacted to varying extents due to differences in their hazard recognition capacities. Regrettably, two workers who failed to identify the danger in time suffered fatal consequences. This underscores the critical role of enhancing hazard identification capabilities as a foundational strategy for accident prevention and safety management, especially in high-risk environments. Hazard identification performance is a foundational step toward effective safety management 4 . However, the objective and quantitative assessment of hazard identification capability remains an unresolved challenge. Previous studies have investigated the hazard identification process using questionnaires, interviews, or other subjective methods. In many studies, hazard identification ability is assessed and predicted based on data collected from a series of hazard identification tasks in relevant scenarios, typically by verbally describing or indicating via questionnaires the presence and number of hazards 5 – 9 . For example, Pandit conducted a hazard identification task in which participants verbally described all the hazards present in a hazardous scenario to collect hazard identification data. This was done to explore the role of safety communication modes within social networks on workers' hazard identification ability 9 . Similarly, a study obtained hazard identification data through the analysis of risk rating scales completed by the participants and the results of interviews conducted with them. The data were used to explore the impact of avalanche education on hazard identification 6 . These findings lay a crucial foundation for exploring hazard identification abilities; however, they present challenges in objectively and quantitatively assessing hazard identification capability. Additionally, since hazard identification is regarded as a visual search task, another primary method has been to equip participants with eye-tracking devices to record oculomotor features. By using Tobii eye-tracker or other devices, the research team extracted and analyzed several eye-tracking metrics for each subject, to measure cognitive processes to gain insights into the hazard identification process 10 – 12 . Unfortunately, hazard identification cannot be directly reflected by the visual search process. With the rapid development of neuroscience and technology, such as functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), and electroencephalogram (EEG), scholars have begun to explore the process of hazard identification from brain neural mechanisms 13 . It not only makes up for the shortcomings of traditional research in objective data, but also provides a neurobiological basis for revealing the psychological process of hazard identification. Studies have explored the anatomical and cognitive components of hazard identification during driving by combining fMRI and neuropsychological tests. It was found that driving hazards caused significant activation of the lateral occipital cortex and the right prefrontal cortex 14 . Liao et al. utilized near-infrared spectroscopy (NIRS) systems to conduct hazard identification tasks, revealing that different types of construction work environments elicit varying cognitive demands 15 – 17 . Compared with other physical and physiological signals, EEG has been increasingly used for hazard identification due to its unique advantages 18 – 22 . However, quantitative measurement data or standards for hazard identification need to be obtained. Therefore, this paper will adopt the method of ERP to deeply analyze the difference of ERP data of measurement indicators in the process of hazard identification and summarize the ERP characteristics of groups with varying speeds and accuracy in hazard identification. This study hopes to reveal the neural mechanism of hazard identification through ERP technology, providing a scientific basis for quantifying and improving hazard identification ability. 2 Literature review 2.1 The application of Eye-tracking and fNIRS in hazard identification 2.1.1 Conceptual research on hazard identification ability A significant portion of injuries occur because subjects are unable to predict or identify hazardous conditions 23 . Unidentified hazards could lead to underestimation of risks. Hazard identification is one of the root causes of accidents 24 . Hazard identification was defined as the accuracy in detecting whether a hazard existed and what the hazard was 5 . Therefore, several field-level hazard identification indicators were encouraged to be used to predict and estimate hazard identification skill. Some scholars used the rationality of hazard assessment and the accuracy of oral description, through the questionnaire survey, to reflect the hazard identification ability of the subjects 25 , 26 . In the field of construction or other industries, it was measured as the proportion of safety hazards successfully identified by a particular worker in a given case image 9 ; Legault et al. found that skin conductance index and heartbeat interval index can measure the change characteristics of individual hazard identification 27 . In the field of transportation, scores on tests of hazard identification skill have been linked with drivers’ crash involvement both prospectively and retrospectively 28 – 34 . The definition of hazard identification has already reached a unified consensus, but research on the ability of hazard identification is still in its early stages. The indicators for representing the ability of hazard identification and the methods for calculating it need to be clarified. 2.1.2 Eye-tracking in hazard identification Eye-tracking can provide valuable insights into cognitive strategies, prior knowledge, and experience by analyzing fixation location and duration 35 , 36 . As a result, many studies have been using eye-tracking to explore the process of hazard identification and its influencing factors. For instance, in the construction industry, scan patterns were investigated as a psychological representation of hazard identification 37 , with different types of hazards can induce different cognitive demands 15 . Bright and tidy construction sites can significantly improve the accuracy and efficiency of information processing in hazard identification 38 . Similarly, under risky driving conditions, novice drivers exhibit narrower scanning behavior compared to experienced drivers. This lack of effective hazard identification in novice drivers may explain their higher involvement in accidents 39 . Additionally, in the field of electrical safety, the eye-tracking data of experts and workers were obtained to improve the factors affecting hazard identification 40 . The results highlighted using eye-tracking to evaluate the difference among different conditions in identifying hazards, providing valuable insights into risk prevention strategies. 2.1.3 fNIRS in hazard identification As a tool for neuroergonomics, Functional Near-Infrared Spectroscopy (fNIRS) has been documented as an effective approach to understand, evaluate, and improve human performance 41 . The application of fNIRS is regarded as adequate for investigating the underlying cognitive and motor processes, which are representative indicators of workload, training, and fatigue 41 . Hence, Sun proposed a new perspective (changes in hemodynamic properties of the prefrontal cortex) in the assessment of hazard identification ability 42 . Zhou et al. carried out fNIRS experiments and found that the process of hazard identification can be described by establishing the relationship between regional hemodynamic response and corresponding brain function 43 . Lee examined the critical aspect of hazard identification in construction safety by employing fNIRS from actual construction sites to assess workers’ performance of hazard identification 44 . 2.2 The application of ERP technology on the process of hazard identification 2.2.1 The use of EEG technology in risk perception and hazard identification Compared to other physical and physiological signals, the EEG signal, recording of electrical activities in the brain, has been increasingly used for risk perception and hazard identification due to its unique advantages 18 – 22 . EEG signal reflects neurocognitive information as a result of human perception of hazards 45 – 47 . Previous studies have demonstrated the feasibility of using EEG signals to identify or confirm the existence of construction hazards by focusing on changes in the signal components (e.g., features and channels) 48 , 49 . Other studies have revealed that workers’ cognitive states (e.g., emotion, stress, and fatigue) assessed by EEG signals vary depending on the hazards placed in the surrounding environment 50 – 53 . Specially, the neurocognitive process of hazard recognition was explored using ERPs and functional magnetic resonance imaging (fMRI), revealing that the recognition of environmental hazards involves two processes: an early detection process and a late emotional experience process 13 . Similarly, Ma et al. proposed the hazard perception two-stage model (HPTS) by EEG technology 54 , 55 . Further research has explored the effect of safety sign optimization methods incorporating scenarios on hazard identification 56 . 2.2.2 ERP characteristics related to hazard identification ERP signals have been demonstrated to serve as measures for various aspects of safety indicators. Previous studies have confirmed that LPP is an evaluative indicator of emotion 53 , 57 . P200 shows the early, automatic, rapid, and low-level processing of stimuli 58 . P300 is elicited by selective attention, and a larger amplitude implies superior information processing 41 , reflects conscious awareness and attention allocation 59 . N100 reflects the processing bias of threatening information and early low-level visual processing and classification 60 , 61 . N200 plays an important role in monitoring cognitive conflict 62 . Specially, ERPs experiments have shown that N1, N2, P200 amplitudes can be used to assess the negative effects of sleep deprivation in the early stage of drivers' hazard identification 63 , 64 . Similarly, a study asserted that the high-hazard situations have a processing advantage in early processing stages through the measured N1 and P3 of different groups. Other studies found that theta wave is related to the execution of the identification process 65 , and the decrease of theta power reflects the decrease of task processing ability 66 . Alpha wave is an indicator of alertness 67 , which is related to the activation of cortical nerve matrix related to information processing 68 . The beta wave can be affected by negative emotions 69 . Therefore, the technique of ERPs has proven effective in revealing the neural mechanisms underlying hazard identification, making it highly suitable for further research in this domain. 2.3 Insufficient and objective indicators for quantifying hazard identification ability While hazard identification is a core process in safety management and considerable research has been dedicated to understanding this process, the field still lacks objective and standardized indicators for quantifying hazard identification ability. Firstly, traditional methods, such as surveys, questionnaires, and interviews, still dominate research in this area 5 – 9 . These tools, although useful, are limited in their ability to accurately represent an individual's cognitive and neural processes involved in hazard identification. For instance, a low score on a hazard identification test does not necessarily indicate poor identifying ability. In fact, it may reflect a greater complexity in the identification process, where an individual might be accessing a broader range of knowledge or engaging in more complex reasoning, which could lead to slower or less accurate responses on simple tests. Secondly, since hazard identification is regarded as a visual search task, the primary method has been to equip participants with eye-tracking devices to record oculomotor features 70 , 71 . Research has shown a strong correlation between eye movement patterns and hazard identification performance 72 . On the basis, researchers use eye-tracking equipment to obtain visual search data to measure the performance of hazard identification 37 , 71 . Such conclusions have made valuable contributions to our understanding of hazard identification through the analysis of visual search patterns, which may not encompass the full complexity of the process. Therefore, we examined it from the perspective of neurocognitive science, which provides a more comprehensive framework for understanding the underlying cognitive and neural processes involved in hazard identification. Finally, although previous and current research have utilized neuroscientific technologies, such as fNIRS and EEG, to study the hazard identification process and its influencing factors, few quantitative metrics or grading standards for hazard identification ability have been established. EEG, in particular, holds great promise due to its ability to provide real-time, direct measurements of brain activity during hazard identification tasks. Thus, by incorporating EEG-based metrics, this study provides a more nuanced, neurocognitive approach to assessing hazard identification ability, offering a clearer, more objective measure of identifying performance. 3 Method 3.1 Experimental materials To enhance validity, all stimulus materials for the hazard identification experiment were sourced from authentic risk scenarios prevalent at construction sites, focusing primarily on common hazards such as object strikes and fall risks. The experimental materials comprised both risk-free and risky stimulus images depicting standard construction scenes and safety behaviors. The stimulus images presented during the experiment had dimensions of 11.906 cm (width) × 10.054 cm (height). 3.2 Equipment The experiment employed the actiCHamp Plus EEG event-related potential (ERP) acquisition system from Brain Products, Germany, which included a 32-channel polar electrode cap conforming to international standards. Experimental protocols were designed using E-Prime 3.0 software to control the presentation of stimuli and record behavioral responses, while the Recorder software managed EEG data acquisition. In the data analysis phase, MATLAB 2021 facilitated the pre-processing and processing of EEG signals for each participant, and statistical analysis was conducted using SPSS 24.0 to ensure rigor in the evaluation of relevant metrics. 3.3 Experimental tasks and processes The primary objective of the hazard identification experiment was to detect the presence of hazardous factors in risky stimulus images. The experimental design employed a "one stimulus - two key choices (S-K1/K2)" paradigm, featuring 80 risky images and 35 standardized non-risky images, each presented twice. Subjects responded by pressing "J" if a hazard was detected and "K" if no hazard was present. The lead investigator provided a detailed overview of the purpose, procedures, and operational steps before commencing the experiment. Practice sessions were conducted to ensure participants were proficient with the task before proceeding to the formal trials. The experimental process of hazard identification is shown in Fig. 1 . The EEG experiment, implemented via E-Prime 3.0 software, consisted of practice and hazard identification phases, totaling 230 trials with each of the 115 images displayed twice in a randomized sequence. To prevent fatigue and maintain concentration, participants were given brief intermissions approximately every 40 trials, lasting 2–3 minutes. 3.4 Subjects of the survey Using G*Power for a priori sample size analysis, the statistical method applied was repeated measures ANOVA. The effect size was 0.6, the α level 0.05, and the power 0.8, with two groups and three repeated measurements. The results indicated that the total sample size required is 14. In this study, 32 subjects were recruited from a construction site for a hazard identification experiment, comprising both frontline construction workers and managers aged 20 to 50 years. Data analysis was conducted on 30 valid datasets, as two subjects exhibited excessive blinking and head movement during the experiment, leading to the exclusion of their EEG data. All subjects were right-handed and reported to be in good mental health. 3.5 ERP recording and pre-processing EEG data were recorded using the ActiCHamp Plus system made by Brain Products, featuring 32 electrodes placed according to the international 10–20 system, with a sampling frequency of 500 Hz. The electrical impedance of electrodes was maintained below 5 kΩ throughout the experiment. The pre-processing workflow for the EEG data was performed using EEGLAB. The raw data was firstly filtered by band-pass filter (0.1 ~ 40 Hz) to remove high-frequency artifacts and low-frequency drifts, and notch filter (50 Hz) was applied to attenuate electrical line noise 73 . Subsequently, Independent Component Analysis (ICA) was utilized to identify and reject artifacts. The continuous EEG signals were segmented into epochs (-200 to 800ms) based on stimulus marks. Epochs with amplitudes exceeding ± 100 µV were then excluded from further analysis. Finally, the data were re-referenced using TP9 and TP10 electrodes as reference electrodes. 4 Results 4.1 Behavioral data At the end of the hazard identification experiment, participants' behavioral data were exported, organized, and analyzed. Indicator results for each participant were computed based on predefined behavioral formulas (1) and (2), as summarized in Table 1 . Hazard Identification Accuracy( \(\:\text{H}\text{I}\text{A})=\frac{\:\text{N}\text{u}\text{m}\text{b}\text{e}\text{r}\:\text{o}\text{f}\:\text{i}\text{m}\text{a}\text{g}\text{e}\text{s}\:\text{c}\text{o}\text{r}\text{r}\text{e}\text{c}\text{t}\text{l}\text{y}\:\text{i}\text{d}\text{e}\text{n}\text{t}\text{i}\text{f}\text{i}\text{e}\text{d}}{\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{n}\text{u}\text{m}\text{b}\text{e}\text{r}\:\text{o}\text{f}\:\:\text{i}\text{m}\text{a}\text{g}\text{e}\text{s}}\) (1) Hazard Identification Speed( \(\:\text{H}\text{I}\text{S})=\frac{\text{N}\text{u}\text{m}\text{b}\text{e}\text{r}\:\text{o}\text{f}\:\text{i}\text{m}\text{a}\text{g}\text{e}\text{s}\:\text{i}\text{d}\text{e}\text{n}\text{t}\text{i}\text{f}\text{i}\text{e}\text{d}\left(\text{s}\text{h}\text{e}\text{e}\text{t}\right)}{\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{d}\text{u}\text{r}\text{a}\text{t}\text{i}\text{o}\text{n}\:\text{o}\text{f}\:\text{i}\text{d}\text{e}\text{n}\text{t}\text{i}\text{f}\text{i}\text{c}\text{a}\text{t}\text{i}\text{o}\text{n}\left(\text{s}\right)}\) (2) Table 1 Calculation results of behavioral indicators for the subjects' hazard identification task Subject HIA HIS (sheet/s) Subject HIA HIS (sheet/s) 1 0.915 0.700 16 0.670 0.405 2 0.913 0.593 17 0.948 0.777 3 0.826 0.942 18 0.939 0.642 4 0.957 0.727 19 0.904 0.545 5 0.891 0.725 20 0.948 0.784 6 0.883 0.496 21 0.961 0.519 7 0.717 0.448 22 0.952 0.679 8 0.909 0.817 23 0.987 0.860 9 0.930 0.995 24 0.909 0.812 10 0.900 0.641 25 0.839 0.697 11 0.900 0.724 26 0.930 0.740 12 0.909 0.626 27 0.778 0.529 13 0.896 0.561 28 0.874 0.681 14 0.917 0.727 29 0.854 0.432 15 0.974 0.626 30 0.885 0.415 The results of the behavioral indicators of the subjects were ranked separately, with the top ten participants in rapid hazard identification speed and high hazard identification accuracy classified as high performers. Conversely, the bottom ten participants in identification speed and accuracy were categorized as low performers, as detailed in Table 2 . Table 2 Grouping of subjects Behavioral indicators High group (fast group) subjects Low group (slow group) subjects HIS 3 4 8 9 14 17 20 23 24 26 2 6 7 13 16 19 21 27 29 30 HIA 4 9 15 17 18 20 21 22 23 26 3 5 6 7 16 25 27 28 29 30 Subjects 4, 9, 17, 20, 23, and 26 demonstrated rapid and accurate hazard identification, while subjects 6, 7, 16, 27, 29, and 30 showed slower responses and lower identification accuracy. Behavioral data from each group were statistically summarized, and the Shapiro-Wilk test (p > 0.05) confirmed normal distribution. An independent samples t-test indicated notable differences in hazard identification accuracy (t = 5.27, p < 0.01) and speed (t = 9.14, p < 0.01) between high- and low-performing groups. These findings justify a detailed comparative EEG analysis between groups differentiated by hazard identification accuracy and response speed as shown in Table 3 . Table 3 Results of descriptive statistics, normality test and independent samples t-test for each group Group Sample size Average value (statistics) Standard deviation Skewness Kurtosis Shapiro-Wilk test t p Value of statistic W p F-HIS 10 0.818 0.091 1.009 0.173 0.887 0.159 9.144 0.000** S-HIS 10 0.494 0.066 -0.052 -1.461 0.938 0.535 H-HIA 10 0.953 0.018 0.585 -0.016 0.948 0.646 5.270 0.000** L-HIA 10 0.822 0.076 -1.159 0.24 0.848 0.055 4.2 ERPs characteristics of HIA 4.2.1 Results of time-domain analysis (1) Analysis of EEG topographic maps The group-average EEG topographic maps for the H-HIA and L-HIA groups was shown in Fig. 2 . Significant early activation (0–300ms) is observed in the occipital and frontal regions, with the low-accuracy group exhibiting greater frontal activation compared to the high-accuracy group. In the later phase (400–500ms), marked activation is evident in the parieto-occipital regions. (2) Analysis of N100 and P200 Based on brain topography and component waveforms, prominent N100 components are observed in the frontal and central regions (C4, Cz, F4, Fz), while distinct P200 components are noted in the parietal-occipital regions (P3, Pz, P4, O1, Oz, O2). Therefore, the analysis focused on the N100 and P200 components to explore differences between the H-HIA and L-HIA groups in detail. As illustrated in Fig. 3 , the comparison between H-HIA and L-HIA groups at electrode sites F4, Fz, C4, and Cz shows pronounced N100 components and notable differences. Topographic maps indicate that, between 100–200 ms, the activation in corresponding brain regions is greater in L-HIA group. Electrodes O1, O2, and Oz showed significant P200 components. Further analysis of the topographic map showed greater activation in the parieto-occipital regions between 200–300 ms in the L-HIA group. Descriptive statistics of the mean amplitudes for N100 and P200 at corresponding electrode points were extracted for the hazard identification accuracy groups. Table 4 shows that the L-HIA group exhibited larger N100 and P200, consistent with topographic findings. Table 4 Hazard identification accuracy comparison group on amplitude of N100 and P200 EEG component Accuracy (high/low) Average amplitude Electrodes Cz F4 Fz C4 N100 High Average value -1.484 -1.725 -2.092 -0.64 Standard deviation 0.869 0.747 0.801 0.7 Low Average value -2.158 -2.716 -2.806 -1.528 Standard deviation 0.99 1.329 1.307 0.693 O1 Oz O2 P200 High Average value 3.466 3.159 3.243 Standard deviation 2.172 2.099 1.871 Low Aaverage value 6.452 6.143 6.877 Standard deviation 3.942 3.823 3.792 To assess differences in N100 and P200 amplitudes between groups, repeated-measures ANOVAs were conducted: 2 (groups: high, low) × 4 (electrodes: F4, Fz, C4, Cz) for N100 and 2 (groups: high, low) × 3 (electrodes: O1, O2, Oz) for P200. Table 5 revealed a significant main effect of group (N100: F = 4.599, p = 0.046; P200: F = 4.983, p = 0.040), indicating notable amplitude differences between the H- and L-HIA groups. Table 5 Inter-subject effect test Source Type III Sum of Squares Degrees of Freedom Mean Square F p Partial Eta Squared N100 Intercept 286.861 1 286.861 98.861 0.000 0.846 ACCgroup 13.346 1 13.346 4.599 0.046 0.204 Error 52.23 18 2.902 P200 Intercept 1291.283 1 1291.283 46.511 0.000 0.744 ACCgroup 138.346 1 138.346 4.983 0.040 0.237 Error 444.209 18 27.763 4.2.2 Results of frequency-domain analysis The frequency-domain analysis in this study focuses on theta, alpha, and beta wavebands. Power spectra were obtained using fast Fourier transform (FFT) at key electrode sites (F3, Fz, F4, C3, Cz, C4, P3, Pz, P4, O1, Oz, O2). Mean power values for each frequency band were extracted to represent the corresponding brain regions, followed by variance analysis to evaluate differences across conditions. Table 6 summarizes the descriptive statistics for theta, beta, and alpha wave power in the frontal, central, parietal, and occipital regions for participants with H- and L-HIA. Table 7 presents the main effect significance analysis of frequency domain indices across these brain regions. Notable differences were identified in theta power between H- and L-HIA groups in the frontal (F = 59.399, p < 0.05), central (F = 17.455, p < 0.05), and parietal regions (F = 8.132, p = 0.013), with higher theta power in the high accuracy group. Alpha power was also significantly higher in the parietal (F = 7.064, p = 0.019 < 0.05) and occipital regions (F = 5.793, p = 0.03 < 0.05) for the high accuracy group. Conversely, beta power was significantly higher in the L-HIA group across the frontal (F = 13.452, p = 0.003 < 0.05), central (F = 11.954, p = 0.004 < 0.05), parietal (F = 6.603, p = 0.019 < 0.05), and occipital regions (F = 9.923, p = 0.007 < 0.05). Table 6 Hazard identification accuracy comparison group on power values by brain region Hazard identification accuracy frontal region power Central region power Parietal power Occipital power Mean M / µV 2 Standard deviation SD Mean M / µV 2 Standard deviation SD Mean M / µV 2 Standard deviation SD Mean M / µV 2 Standard deviation SD theta High 2.159 0.207 1.733 0.211 1.52 0.296 1.332 0.344 Low 1.32 0.266 1.213 0.311 1.16 0.259 1.055 0.176 alpha High 1.669 0.488 1.426 0.406 1.445 0.335 1.27 0.262 Low 1.32 0.266 1.204 0.301 1.099 0.209 0.93 0.289 beta High 0.859 0.103 0.718 0.052 0.699 0.119 0.632 0.152 Low 1.091 0.187 0.902 0.145 0.842 0.149 0.953 0.237 Table 7 Results of variance analysis of frequency-domain data of hazard identification accuracy comparison group Encephalic region Type III sums of squares Mean square F value p theta Frontal region 8.440 8.440 59.399 0.000*** Central region 3.250 3.250 17.455 0.001*** Parietal region 1.549 1.549 8.132 0.013* Occipital region 0.923 0.923 3.972 0.066 beta Frontal region 0.643 0.643 13.452 0.003** Central region 0.408 0.408 11.954 0.004** Parietal region 0.306 0.306 6.603 0.019* Occipital region 1.233 1.233 9.923 0.007** alpha Frontal region 1.465 1.465 3.03 0.104 Central region 0.591 0.591 1.509 0.240 Parietal region 1.442 1.442 7.064 0.019* Occipital region 1.389 1.389 5.793 0.030* Notes: * p < 0.05;** p < 0.01༛*** p < 0.001 4.3 ERPs characteristics of HIS 4.3.1 Results of time-domain analysis (1) Analysis of EEG topographic maps Analyzing the group-averaged EEG topographies of the F- and S- HIS groups (Fig. 4 ), it was found that the low group had greater activation in the frontal and parieto-occipital regions between 200-400ms. (2) Analysis of N100, P200 and P300 Analysis of brain topographic maps and component waveforms revealed prominent N100 components in the frontal (F3, Fz, F4) and central (C3, Cz, C4) regions, as well as clear P200 and P300 components in the parieto-occipital region (P3, Pz, P4, O1, Oz, O2). This study thus focuses on examining the N100, P200, and P300 components elicited by the F- and S-HIS groups. The waveforms obtained from the electrodes F3, F4, Fz, C3, C4, and Cz revealed significant differences in the N100 components between the HIS groups, while electrodes O1, O2, and Oz showed prominent P200 and P300 components, as illustrated in Fig. 5 . The average amplitudes of components at the respective electrode sites were extracted for the comparison group, as summarized in Table 8 . It can be seen that S-HIS group exhibited larger N100, P200, and P300 amplitudes, aligning with topographic map and waveform findings. Table 8 The amplitude of N100,P200,P300 in the comparison group of hazard identification speed EEG component Speed (Fast/Slow) Average amplitude Electrodes Fz F3 F4 Cz C3 C4 N100 Fast Mean -1.581 -1.685 -1.445 -1.135 -1.029 -0.658 SD 0.772 0.778 0.74 0.63 0.741 0.481 Slow Mean -3.202 -2.834 -2.941 -2.627 -2.306 -1.63 SD 1.671 2.045 1.592 1.452 1.42 1.091 P200 O1 Oz O2 Fast Mean 3.311 3.071 3.464 SD 1.507 1.389 1.088 Slow Mean 5.883 5.625 5.861 SD 2.857 2.962 3.138 P300 O1 Oz O2 Fast Mean 2.272 1.948 2.326 SD 1.325 0.895 1.088 Slow Mean 4.538 4.51 4.869 SD 2.638 2.599 2.942 To investigate group differences in mean amplitudes of N100, P200, and P300 during the hazard identification task, repeated measures ANOVA was conducted: 2 (group: fast, slow) × 6 (electrodes: F3, F4, Fz, C3, C4, Cz) and 2 (group: fast, slow) × 3 (electrodes: O1, O2, Oz). The analysis, shown in Table 9 , revealed significant main effects for group comparisons (F = 7.832, p = 0.012; F = 6.103, p = 0.024; F = 6.441, p = 0.022), indicating notable differences in mean amplitudes of N100, P200, and P300 components between groups. Table 9 Inter-subject effect test Source Type III Sum of Squares Degrees of Freedom Mean Square F p Partial Eta Squared N100 Intercept 443.64 1 443.64 65.012 0 0.783 Speed group 53.443 1 53.443 7.832 0.012 0.303 Error 122.83 18 6.824 P200 Intercept 1234.4 1 1234.4 79.886 0 0.816 Speed group 94.311 1 94.311 6.103 0.024 0.253 Error 278.14 18 15.452 P300 Intercept 628.04 1 628.04 49.64 0 0.756 Speed group 81.495 1 81.495 6.441 0.022 0.287 Error 202.43 18 12.652 4.3.2 Results of frequency-domain analysis Table 10 presents descriptive statistics for theta, beta, and alpha wave power across the frontal, central, parietal, and occipital regions for the hazard identification speed groups. Table 11 summarizes the main effect analysis of frequency domain indices for each brain region. Notable differences in beta power were observed between H-HIA and L-HIA groups in the frontal (F = 8.697, p = 0.009), central (F = 10.355, p = 0.006), parietal (F = 6.603, p = 0.019), and occipital (F = 9.923, p = 0.007) regions, with greater beta power in the low accuracy group. No notable differences were found in theta or alpha power across brain regions. Table 10 Hazard identification speed comparison group power values for each brain region Speed of hazard identification Frontal region power Central region power Parietal region power Occipital power Mean M / µV2 Standard deviation SD Mean M / µV2 Standard deviation SD Mean M / µV2 Standard deviation SD Mean M / µV2 Standard deviation SD theta Fast 1.915 0.356 1.898 0.702 1.74 0.507 1.669 0.801 Slow 2.16 0.523 1.546 0.312 1.415 0.235 1.27 0.221 alpha Fast 1.345 0.259 1.453 0.468 1.413 0.423 1.341 0.525 Slow 1.535 0.207 1.235 0.284 1.175 0.253 1.04 0.203 beta Fast 0.862 0.113 0.72 0.06 0.677 0.099 0.655 0.169 Slow 1.056 0.198 0.897 0.149 0.858 0.153 0.904 0.231 Table 11 The results of variance analysis of frequency domain data of hazard identification speed comparison group Encephalic region Type III sums of squares Mean square F value p theta Frontal region 0.722 0.722 1.266 0.279 Central region 1.490 1.490 1.943 0.185 Parietal region 1.268 1.268 3.083 0.101 Occipital region 1.915 1.915 1.704 0.213 beta Frontal region 0.506 0.506 8.697 0.009** Central region 0.378 0.378 10.355 0.006** Parietal region 0.395 0.395 8.596 0.011* Occipital region 0.746 0.746 5.783 0.031* alpha Frontal region 0.435 0.435 2.754 0.119 Central region 0.573 0.573 1.364 0.262 Parietal region 0.678 0.678 1.903 0.189 Occipital region 1.086 1.086 2.132 0.166 4.4 Characteristics of the hazard identification ability The L-HIA and H-HIA groups exhibited notable differences in N100, P200 components, as well as beta, theta, and alpha power. The S-HIS group showed greater N100, P200, and P300 components, along with higher beta power, compared to the fast group. Considering both ERP feature components, theta power emerges as a distinct indicator for hazard identification accuracy, while the P300 component serves as a unique indicator for hazard identification speed. 4.4.1 The quantified indicator and grading standard for HIA The average theta power in the hazard identification accuracy group was further analyzed to derive characteristic patterns. It was concluded that the average theta power may serve as an indicator for distinguishing high versus low hazard identification accuracy. Figure 6 illustrates theta power during hazard identification for the accuracy comparison group. Comparison revealed that theta wave power in the high accuracy group was relatively consistent, with the minimum power of 1.86 µV 2 and the average power of 1.99 µV 2 . The power of theta wave in the low group was also relatively consistent, with the maximum power of 1.56 µV 2 and the average power of 1.22 µV 2 . 4.4.2 The quantified indicator and grading standard for HIS The average peak voltage of the P300 component in the speed comparison group was further analyzed to derive characteristic patterns. It was concluded that the average P300 amplitude can act as an indicator for determining fast versus slow hazard identification speed. And Fig. 7 presents the P300 peak voltage in the occipital region for the speed comparison group. It can be found that the amplitude of P300 in the group with F-HIS was relatively consistent, with the maximum peak voltage of 2.82 µV and the average peak voltage of 1.78 µV. The P300 component of the S-HIS group was also relatively consistent, with the minimum peak voltage of 3.43 µV, and the average peak voltage of 5.67 µV. 5 Discussion 5.1 Characteristics of HIA 5.1.1 Significant differences in time-domain characteristics among subjects with varying HIA The results showed that L-HIA group demonstrated larger N100 and P200 components compared to the H-HIA group (see Fig. 3 , Table 4 ). The N100 component, localized in the frontal region, reflects early attentional orientation, with larger amplitudes indicating greater resource allocation 60 , 61 . Research has identified the N100 component as a reliable marker for the biased processing of threat-related cues 74 . Differentiating within a brief window of 80–130 ms post-stimulus, the N100 underscores the rapid and intuitive nature of hazard perception 58 . In addition, the N100 component has been shown to signify an early processing bias toward risky stimuli, enhancing the rapid allocation of attention to risk-related information 75 . Our findings indicate that individuals with lower accuracy in hazard identification tasks exhibit larger N100 amplitudes, reflecting an increased allocation of attentional resources to compensate for inefficiencies in identification. Compared to their high-accuracy counterparts, these individuals face greater challenges in early information processing, such as ambiguity or uncertainty in hazard recognition, necessitating greater attentional investment and resulting in enhanced N100 amplitudes. This suggests that heightened attentional engagement does not necessarily correlate with improved identification accuracy, and the allocation of immediate attentional resources alone may not directly influence hazard identification outcomes. Instead, the accuracy of hazard identification is also influenced by more stable factors, such as knowledge and experience 76 , 77 . Conversely, individuals with higher identification accuracy exhibit lower N100 amplitudes, indicating reduced allocation of attentional resources. This suggests that individuals with higher identification accuracy utilize attentional resources more efficiently and require fewer resources for hazard identification. The P200 component is an attention-related marker indicative of early, automatic, and rapid stimulus processing 78 , 79 . Scholars widely acknowledge that the P200 component reflects attentional bias, with greater P200 amplitudes indicating increased allocation of attentional resources, irrespective of the stimulus valence 80 . Salmi et al. demonstrated that uncertain hazard stimuli elicit significantly higher P200 amplitudes compared to certain hazards, highlighting its role as an indicator of hazard perception, with increased P200 amplitude correlating with heightened perceived hazard 81 . Additionally, P200 is associated with early stimulus detection, selective attention, and the efficiency of information categorization; smaller amplitudes indicate more efficient processing 82 . For instance, sleep deprivation has been shown to significantly increase P200 amplitudes, suggesting impaired discrimination and processing speed, as well as diminished selective attention and interference resistance 83 . In this study, individuals with lower hazard identification accuracy elicited larger P200 amplitudes, suggesting that they require greater attentional resources to process hazard-related information, reflecting lower processing efficiency. Hazard information is less intuitive or more challenging for them to interpret, resulting in increased uncertainty and heightened difficulty of identification. In contrast, individuals with higher identification accuracy exhibited smaller P200 amplitudes, indicating that they require fewer attentional resources for hazard identification and demonstrate higher processing efficiency. 5.1.2 Significant differences in frequency-domain characteristics among subjects with varying HIA The H-HIA group exhibited greater theta and alpha power compared to the L-HIA group, whereas the L-HIA group showed higher beta power than the H-HIA group(see Table 6 ). Previous studies have shown that theta band oscillations, which are widely distributed throughout the brain, play a critical role in higher cognitive functions, such as event and memory encoding, motor responses, working memory, novelty detection, and top-down control. Frontal-central theta activity, in particular, has been linked to the execution of these cognitive processes 84 . Prefrontal theta power is associated with efficient cognitive processing 66 , 68 . while reduced theta power signals impaired task processing capacity 69 . In this study, individuals with high hazard identification accuracy demonstrated increased frontal theta power, reflecting their capacity to suppress irrelevant information and execute identification processes effectively, thereby enhancing identification accuracy. Conversely, the low accuracy group exhibited lower theta power, indicative of less efficient identification performance and diminished task processing abilities. Alpha wave activity over the posterior scalp reflects neural mechanisms related to attention allocation, with decreased alpha power indicating active attentional engagement 85 . In the study, the L-HIA group exhibited reduced alpha power in the parietal and occipital regions, indicating a greater allocation of attentional resources during the task. This finding aligns with the observation of significantly larger P200 amplitudes in this group, suggesting that individuals with lower identification accuracy require greater attentional resources for hazard identification. Regarding indicator beta, it has been suggested that beta activity is related to emotional states (e.g., excitement, sadness, tension, etc.), and that negative emotions caused by noise, etc., can increase beta waves 86 , 87 . The increase in beta power observed in the L-HIA group suggests that they are influenced by negative emotions during the later stages of the task 88 , impacting their identification performance. 5.2 Characteristics of HIS 5.2.1 Significant differences in time-domain characteristics among subjects with varying HIS The S-HIS group demonstrated larger N100, P200, and P300 components compared to the F-HIS group (see Fig. 5 ,Table 8 ). During the early stages of hazard information processing, individuals in the S-HIS group may face greater identification challenges, which leads to an increased focus on the hazard information. As a result, they must allocate more time and attentional resources to process the information across different contexts. This increased cognitive effort is reflected in significantly larger N100 and P200 amplitudes. Conversely, individuals in the F-HIS group demonstrated more efficient early-stage information processing and required less reliance on attentional and identification resources. Furthermore, P200 amplitude has been linked to the speed at which decision-makers identify key features of decision problems; smaller P200 amplitudes typically correspond to faster identification speeds 89 , which is consistent with present study’s findings that individuals in the F-HIS group exhibited smaller P200 amplitudes. The P300 component, a positive waveform appearing approximately 300 ms post-stimulus, is indicative of attentional resource allocation 90 , mainly reflecting the increase in attentional resource investment in emotionally or motivationally notable stimuli 91 , 92 . According to the context-updating hypothesis, P300 is elicited when new information is detected during cognitive processing, prompting the brain to update its existing mental model stored in working memory. If no significant change is detected in the stimulus attributes, the current model is maintained, and only earlier components (e.g., N100, P200, N200) are observed, if new information is detected, the brain allocates more attentional resources and updates the original contextual information (accompanied by the P300 components) to modify future coping decisions and responses 62 . In the present study, individuals in the S-HIS group exhibited a larger P300 amplitudes than the F-HIS group, suggesting that the S-HIS group experienced greater uncertainty and difficulty during hazard identification, required more attentional resources to process and identify hazards. In addition, larger P300 amplitudes indicate that individuals in the S-HIS group may undergo an information updating process during hazard identification. The P300 amplitude has also been associated with perceived hazard levels, higher perceived hazard leads to greater P300 amplitudes regardless of an individual’s impulsivity 93 , 94 . Hence, the S-HIS group with larger P300 amplitudes may process hazard information more thoroughly, potentially eliciting stronger negative emotions and resulting in longer response times. Conversely, the F-HIS group elicited lower P300 amplitude may face less uncertain hazard information, enabling efficient hazard identification without excessive attention resource allocation. Overall, individuals encountering more challenges in initial hazard processing required greater attentional and identification resources, experienced more identification conflict, and engaged in more controlled processing, resulting in slower hazard identification. In contrast, those able to process hazard information quickly displayed faster hazard identification. 5.2.2 Significant differences in frequency-domain characteristics among subjects with varying HIS The S-HIS rates demonstrated greater beta power compared to the F-HIS group (see Table 10 ). Beta activity has been linked to emotional states such as excitement, sadness, and anxiety, with negative emotions—often induced by factors like noise—resulting in heightened beta power 69 . In this study, the S-HIS group elicited greater beta power, demonstrating more obvious negative emotion. The significant P300 component observed in the time-domain analysis also verifies this point, suggesting that individuals in this group experience greater emotional interference during hazard identification. Accordingly, emotional interference in the S-HIS group may contribute to their slower hazard identification speed. 5.3 Indicators and grading standard of ERP features for hazard identification ability The results indicated that theta wave and P300 components can be used as measurement indicators of HIA and HIS, which verifies the correctness of previous research results: P300 can dynamically track the progression of cognitive impairment, positioning it as a crucial biomarker for aiding in the diagnosis of mental and neurological cognitive disorders, as well as for evaluating disease progression, therapeutic efficacy, and prognosis 95 . The decrease of theta wave power reflects the weakening of task processing ability 66 . In addition, the results proved that P300 and theta can be used to assessing hazard identification ability, providing an objective physiological basis for related research. Table 12 shows the specific grading standard for hazard identification accuracy and speed. Table 12 Grading standard of HIA and HIS Characteristic indicators H-HIA L-HIA F-HIS S-HIS theta >1.99µV 2 < 1.22µV 2 P300 5.67µV 5.4 Implications and limitation Objective data on hazard identification were obtained through ERP experiments, and after time-frequency transformation, the mean power of theta waves during hazard identification was compared against the grading standard to directly assess hazard identification accuracy. Similarly, the mean peak of the P300 component was evaluated against its grading standard to assess the speed of hazard identification. On this basis, quantifying hazard identification ability can provide a foundation for developing more scientific evaluation standards and tailored methods for vocational training and safety education. Specifically, for practitioners in high-hazard industries (such as construction, chemical, manufacturing, etc.), EEG tests can be used to assess their sensitivity and response speed to potential hazards in actual operation, to develop personalized training programs and improve the overall workplace safety level. Enterprises can leverage this method to monitor and evaluate employees' routine hazard recognition capabilities, allowing for the timely identification of those who may require additional training. This proactive approach enhances the prevention of potential incidents, optimizes safety management strategies, and minimizes workplace accident rates. In the field of education and training, the results can be used as a reference tool to evaluate the level of students' cognitive ability. For example, drivers, pilots and emergency rescue personnel who have higher requirements for response speed and accuracy can be regularly assessed by EEG tests to ensure that they continue to have good emergency response capabilities. In general, through the objective assessment and application of hazard identification capabilities, it can effectively improve the hazard management level and personnel safety awareness in various fields, and ultimately achieve a safer and more efficient production and working environment. However, this study has some limitations. Firstly, while the experimental materials consisted of authentic on-site risk stimulation images, the advancement of virtual reality technology allows for the creation of more immersive risk scenarios. These VR environments can enhance subsequent quantitative assessments of individual hazard identification ability by providing participants with a more realistic and engaging experience. Secondly, the subject selection was limited, future research should aim to increase both the sample size and industry diversity to establish a more universally applicable standard for measuring hazard identification ability. 6 Conclusion Under the theoretical background of safety science and cognitive neuroscience, this study examines the process of hazard identification, with a primary focus on assessing and quantifying the accuracy and speed of hazard identification. The research investigates individuals' hazard identification abilities through objective EEG measurements and provides a comprehensive analysis of the associated EEG characteristics. The key findings are as follows. (1) L-HIA and H-HIA groups induced significantly different N100 and P200 components as well as beta, theta, and alpha power. (2) F-HIS and S-HIS groups elicited significantly different N100, P200, and P300 components as well as beta power. (3) Theta wave and the P300 component can serve as distinct ERP features for assessing hazard identification accuracy and speed, respectively. The mean power of theta waves in the central frontal region (P low 1.99 µV²) can be utilized as a grading standard for evaluating hazard identification accuracy. Similarly, the average peak voltage of the P300 component in the occipital region (U fast 5.67 µV) can act as a grading standard for assessing the speed of hazard identification. Declarations Author Contribution S.Z. conceptualized the study, supervised the research, and reviewed/edited the manuscript. S.T. contributed to conceptualization, developed the methodology, and wrote the original draft. S.Y. conducted the investigation. X.S. provided resources and participated in manuscript revision. Y.Z. and B.W. contributed resources. All authors reviewed and approved the final manuscript. 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Fu, H., Tan, Y., Xia, Z., Feng, K. & Guo, X. Effects of construction workers' ' safety knowledge on hazard-identification performance via eye-movement modeling examples training. Saf. Sci. 180 10.1016/j.ssci.2024.106653 (2024). Ouyang, Y. & Luo, X. Differences between inexperienced and experienced safety supervisors in identifying construction hazards: Seeking insights for training the inexperienced. Adv. Eng. Inform. 52 10.1016/j.aei.2022.101602 (2022). Mercado, F., Carretié, L., Tapia, M. & Gómez-Jarabo, G. The influence of emotional context on attention in anxious subjects:: neurophysiological correlates. J. Anxiety Disord. 20 , 72–84. 10.1016/j.janxdis.2004.10.003 (2006). Poiezzi, D., Lotto, L., Daum, I., Sartori, G. & Rumiati, R. Predicting outcomes of decisions in the brain. Behav. Brain. Res. 187 , 116–122. 10.1016/j.bbr.2007.09.001 (2008). Kai, W. Research on Decision Maker's Framing Effect under Paroxysmal Events. zhejiang university (2010). Salmi, J. et al. Working memory updating training modulates a cascade of event-related potentials depending on task load. Neurobiol. Learn. Mem. 166 10.1016/j.nlm.2019.107085 (2019). Klimesch, W. EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res. Rev. 29 , 169–195. 10.1016/s0165-0173(98)00056-3 (1999). Itthipuripat, S., Wessel, J. R. & Aron, A. R. Frontal theta is a signature of successful working memory manipulation. Exp. Brain Res. 224 , 255–262. 10.1007/s00221-012-3305-3 (2013). Hsieh, L. T. & Ranganath, C. Frontal midline theta oscillations during working memory maintenance and episodic encoding and retrieval. Neuroimage 85 , 721–729. 10.1016/j.neuroimage.2013.08.003 (2014). Zuoqiu, Q., Hong, W., Xiaobing, Z. & Qiaoxiu, W. Evaluation and analysis on influence of industrial noise on brain cognition based on EEG power spectrum. China Saf. Sci. J. 30 , 178–183. DOI:10.16265/j.cnki.issn1003-3033.2021.03.025 (2021). Yuan, J. et al. Are we sensitive to valence differences in emotionally negative stimuli? Electrophysiological evidence from an ERP study. Neuropsychologia 45 , 2764–2771 (2007). Kida, T. et al. Resource allocation and somatosensory P300 amplitude during dual task: effects of tracking speed and predictability of tracking direction. Clin. Neurophysiol. 115 , 2616–2628. 10.1016/j.clinph.2004.06.013 (2004). Roshanaei, M., Norouzi, H., Onton, J., Makeig, S. & Mohammadi, A. EEG-based functional and effective connectivity patterns during emotional episodes using graph theoretical analysis. Sci. Rep. 15 , 2174–2174. 10.1038/s41598-025-86040-9 (2025). Hajcak, G. & Olvet, D. M. The persistence of attention to emotion: Brain potentials during and after picture presentation. Emotion 8 , 250–255. 10.1037/1528-3542.8.2.250 (2008). Schupp, H. T. et al. Affective picture processing: The late positive potential is modulated by motivational relevance. Psychophysiology 37 , 257–261. 10.1111/1469-8986.3720257 (2000). Martin, L. E. & Potts, G. F. Impulsivity in decision-making: An event-related potential investigation. Pers. Indiv. Differ. 46 , 303–308. 10.1016/j.paid.2008.10.019 (2009). Polich, J. Updating p300: An integrative theory of P3a and P3b. Clin. Neurophysiol. 118 , 2128–2148. 10.1016/j.clinph.2007.04.019 (2007). Peng, Z. et al. Effect of sleep deprivation on the working memory-related N2-P3 components of the event-related potential waveform. Front. NeuroSci. 14 , 469 (2020). Folstein, J. R. & Van Petten, C. Influence of cognitive control and mismatch on the N2 component of the ERP: A review. Psychophysiology 45 , 152–170. 10.1111/j.1469-8986.2007.00602.x (2008). Jing, Z., Xiaobo, L., Juan, L., Zhong, Z. & Rongjiang, J. Event-related potential for cognitive function research: a visual analysis. Chin. J. Rehabilitation Theory Pract. 28 , 69–78 (2022). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6434600","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":458610033,"identity":"e4930c0f-1d33-4378-a41d-b95049ad9d6a","order_by":0,"name":"Shu Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYDACCTB5gIEfwmUmQYtkA8laDA4Qq0V+dvOzh1/+3EncfPz4MwmGCuvEBvazB/BqYZxzzNxYtu1Z4rYzOWYSDGfSExt48hLwamGWSDCTlmw4nLjtBg+bBGPb4cQGCR4DvFrYJNK/SUv8OZy4eQb7MwnGf0Ro4ZHIMZP8wHY4cYMEg5kEYwMRWiQkcsqkge4xnnEmx9gi4Vi6cRtPDn4t8jPSt0n++HNYtr/9+MMbH2qsZfvZz+DXAgLMPDBWAsh3BNUDAeMPYlSNglEwCkbByAUAOAJE7hn7wN4AAAAASUVORK5CYII=","orcid":"","institution":"Central South University","correspondingAuthor":true,"prefix":"","firstName":"Shu","middleName":"","lastName":"Zhang","suffix":""},{"id":458610035,"identity":"262d2832-c95b-4ad2-9c09-d0e5973a8868","order_by":1,"name":"Shanhong Tang","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Shanhong","middleName":"","lastName":"Tang","suffix":""},{"id":458610036,"identity":"4e90563b-8bfd-4d4c-b138-dfeb499221f5","order_by":2,"name":"Shufen Ye","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Shufen","middleName":"","lastName":"Ye","suffix":""},{"id":458610042,"identity":"75789cd5-28a5-41ab-a46f-175d99626083","order_by":3,"name":"Xiuzhi Shi","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Xiuzhi","middleName":"","lastName":"Shi","suffix":""},{"id":458610043,"identity":"498abbad-9dcb-46fa-894f-64e965aa2858","order_by":4,"name":"Yan Zhang","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Zhang","suffix":""},{"id":458610044,"identity":"dfbba64c-6ddd-44f0-ab8b-145fe03f84b8","order_by":5,"name":"Bing Wang","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Bing","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-04-12 12:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6434600/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6434600/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-35883-x","type":"published","date":"2026-01-20T15:59:08+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83175536,"identity":"72013609-09e4-45e9-944f-f128622ad114","added_by":"auto","created_at":"2025-05-20 19:16:33","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":131775,"visible":true,"origin":"","legend":"\u003cp\u003eHazard identification experiment flowchart\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6434600/v1/632b5a28e2c4d2c564216957.jpg"},{"id":83175907,"identity":"51b81ab3-a171-4cd1-b3da-a7132ff9a7c6","added_by":"auto","created_at":"2025-05-20 19:24:33","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":37189,"visible":true,"origin":"","legend":"\u003cp\u003eBrain topography of hazard identification accuracy comparison groups\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6434600/v1/700165e6bff9ab0b8bdea51e.jpg"},{"id":83175419,"identity":"16450407-c58f-4529-9416-1a22724395d4","added_by":"auto","created_at":"2025-05-20 19:08:33","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":88827,"visible":true,"origin":"","legend":"\u003cp\u003eGroup-average waveform of the N100 and P200\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6434600/v1/3faa60f0f56007b75aac4a3b.jpg"},{"id":83175423,"identity":"055263e6-d09f-4c5b-8042-7b923bdd7a86","added_by":"auto","created_at":"2025-05-20 19:08:33","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":153231,"visible":true,"origin":"","legend":"\u003cp\u003eBrain topography of hazard recognition speed’s comparison\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6434600/v1/2212a4a118766d96bff9151b.jpg"},{"id":83175535,"identity":"480eb005-a3e3-4f1e-8c93-c102554f3e35","added_by":"auto","created_at":"2025-05-20 19:16:33","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":47212,"visible":true,"origin":"","legend":"\u003cp\u003eGroup-average waveform of the N100,P200 and P300\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6434600/v1/5c15adaa17fe1defeef008fc.jpg"},{"id":83175425,"identity":"c4fabf09-49da-45d8-8e23-681de8ba287c","added_by":"auto","created_at":"2025-05-20 19:08:33","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":76618,"visible":true,"origin":"","legend":"\u003cp\u003eIndicator’s characterization patterns of HIA\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6434600/v1/532a5def96b176e5d62f37de.jpg"},{"id":83175908,"identity":"7700789d-c527-4051-b1b4-762e8675a5d5","added_by":"auto","created_at":"2025-05-20 19:24:33","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":76918,"visible":true,"origin":"","legend":"\u003cp\u003eIndicator’s characterization pattern of HIS\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6434600/v1/b8cc254e43eab7008ec45e32.jpg"},{"id":101152831,"identity":"eb01c8df-4dad-49cd-bdae-4816de676e74","added_by":"auto","created_at":"2026-01-26 16:13:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2687373,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6434600/v1/ebe00f0b-b2be-46fa-befb-11260359a431.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The ERP characteristics in the process of hazard identification","fulltext":[{"header":"1 introduction","content":"\u003cp\u003eAmid rapid societal development, individuals encounter increasingly complex hazards and risks, the rapid and effective identification of hazards in complex environments is a crucial first step in accident prevention. An empirical study conducted in the United Kingdom revealed that 33.5% of safety hazards in workplaces remain unrecognized \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Similarly, field inspections in the United States have shown that over 40% of safety risks go unnoticed \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Furthermore, in the construction sites of Australia, more than 57% of potential hazards remain undetected \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. A recent scaffolding collapse on October 9, 2023, in Guangdong Province, China, tragically illustrated this point: six workers engaged in concrete pouring were impacted to varying extents due to differences in their hazard recognition capacities. Regrettably, two workers who failed to identify the danger in time suffered fatal consequences. This underscores the critical role of enhancing hazard identification capabilities as a foundational strategy for accident prevention and safety management, especially in high-risk environments. Hazard identification performance is a foundational step toward effective safety management \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. However, the objective and quantitative assessment of hazard identification capability remains an unresolved challenge.\u003c/p\u003e \u003cp\u003ePrevious studies have investigated the hazard identification process using questionnaires, interviews, or other subjective methods. In many studies, hazard identification ability is assessed and predicted based on data collected from a series of hazard identification tasks in relevant scenarios, typically by verbally describing or indicating via questionnaires the presence and number of hazards \u003csup\u003e\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. For example, Pandit conducted a hazard identification task in which participants verbally described all the hazards present in a hazardous scenario to collect hazard identification data. This was done to explore the role of safety communication modes within social networks on workers' hazard identification ability \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Similarly, a study obtained hazard identification data through the analysis of risk rating scales completed by the participants and the results of interviews conducted with them. The data were used to explore the impact of avalanche education on hazard identification \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. These findings lay a crucial foundation for exploring hazard identification abilities; however, they present challenges in objectively and quantitatively assessing hazard identification capability. Additionally, since hazard identification is regarded as a visual search task, another primary method has been to equip participants with eye-tracking devices to record oculomotor features. By using Tobii eye-tracker or other devices, the research team extracted and analyzed several eye-tracking metrics for each subject, to measure cognitive processes to gain insights into the hazard identification process \u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Unfortunately, hazard identification cannot be directly reflected by the visual search process.\u003c/p\u003e \u003cp\u003eWith the rapid development of neuroscience and technology, such as functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), and electroencephalogram (EEG), scholars have begun to explore the process of hazard identification from brain neural mechanisms \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. It not only makes up for the shortcomings of traditional research in objective data, but also provides a neurobiological basis for revealing the psychological process of hazard identification. Studies have explored the anatomical and cognitive components of hazard identification during driving by combining fMRI and neuropsychological tests. It was found that driving hazards caused significant activation of the lateral occipital cortex and the right prefrontal cortex \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Liao et al. utilized near-infrared spectroscopy (NIRS) systems to conduct hazard identification tasks, revealing that different types of construction work environments elicit varying cognitive demands \u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Compared with other physical and physiological signals, EEG has been increasingly used for hazard identification due to its unique advantages \u003csup\u003e\u003cspan additionalcitationids=\"CR19 CR20 CR21\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. However, quantitative measurement data or standards for hazard identification need to be obtained.\u003c/p\u003e \u003cp\u003eTherefore, this paper will adopt the method of ERP to deeply analyze the difference of ERP data of measurement indicators in the process of hazard identification and summarize the ERP characteristics of groups with varying speeds and accuracy in hazard identification. This study hopes to reveal the neural mechanism of hazard identification through ERP technology, providing a scientific basis for quantifying and improving hazard identification ability.\u003c/p\u003e"},{"header":"2 Literature review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 The application of Eye-tracking and fNIRS in hazard identification\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Conceptual research on hazard identification ability\u003c/h2\u003e \u003cp\u003eA significant portion of injuries occur because subjects are unable to predict or identify hazardous conditions \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Unidentified hazards could lead to underestimation of risks. Hazard identification is one of the root causes of accidents \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Hazard identification was defined as the accuracy in detecting whether a hazard existed and what the hazard was \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Therefore, several field-level hazard identification indicators were encouraged to be used to predict and estimate hazard identification skill. Some scholars used the rationality of hazard assessment and the accuracy of oral description, through the questionnaire survey, to reflect the hazard identification ability of the subjects \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. In the field of construction or other industries, it was measured as the proportion of safety hazards successfully identified by a particular worker in a given case image \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e; Legault et al. found that skin conductance index and heartbeat interval index can measure the change characteristics of individual hazard identification \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. In the field of transportation, scores on tests of hazard identification skill have been linked with drivers\u0026rsquo; crash involvement both prospectively and retrospectively \u003csup\u003e\u003cspan additionalcitationids=\"CR29 CR30 CR31 CR32 CR33\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. The definition of hazard identification has already reached a unified consensus, but research on the ability of hazard identification is still in its early stages. The indicators for representing the ability of hazard identification and the methods for calculating it need to be clarified.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Eye-tracking in hazard identification\u003c/h2\u003e \u003cp\u003eEye-tracking can provide valuable insights into cognitive strategies, prior knowledge, and experience by analyzing fixation location and duration \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. As a result, many studies have been using eye-tracking to explore the process of hazard identification and its influencing factors. For instance, in the construction industry, scan patterns were investigated as a psychological representation of hazard identification \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, with different types of hazards can induce different cognitive demands \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Bright and tidy construction sites can significantly improve the accuracy and efficiency of information processing in hazard identification \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Similarly, under risky driving conditions, novice drivers exhibit narrower scanning behavior compared to experienced drivers. This lack of effective hazard identification in novice drivers may explain their higher involvement in accidents \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Additionally, in the field of electrical safety, the eye-tracking data of experts and workers were obtained to improve the factors affecting hazard identification \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. The results highlighted using eye-tracking to evaluate the difference among different conditions in identifying hazards, providing valuable insights into risk prevention strategies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.1.3 fNIRS in hazard identification\u003c/h2\u003e \u003cp\u003eAs a tool for neuroergonomics, Functional Near-Infrared Spectroscopy (fNIRS) has been documented as an effective approach to understand, evaluate, and improve human performance \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. The application of fNIRS is regarded as adequate for investigating the underlying cognitive and motor processes, which are representative indicators of workload, training, and fatigue \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Hence, Sun proposed a new perspective (changes in hemodynamic properties of the prefrontal cortex) in the assessment of hazard identification ability \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Zhou et al. carried out fNIRS experiments and found that the process of hazard identification can be described by establishing the relationship between regional hemodynamic response and corresponding brain function \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Lee examined the critical aspect of hazard identification in construction safety by employing fNIRS from actual construction sites to assess workers\u0026rsquo; performance of hazard identification\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.2 The application of ERP technology on the process of hazard identification\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 The use of EEG technology in risk perception and hazard identification\u003c/h2\u003e \u003cp\u003eCompared to other physical and physiological signals, the EEG signal, recording of electrical activities in the brain, has been increasingly used for risk perception and hazard identification due to its unique advantages \u003csup\u003e\u003cspan additionalcitationids=\"CR19 CR20 CR21\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. EEG signal reflects neurocognitive information as a result of human perception of hazards \u003csup\u003e\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Previous studies have demonstrated the feasibility of using EEG signals to identify or confirm the existence of construction hazards by focusing on changes in the signal components (e.g., features and channels) \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Other studies have revealed that workers\u0026rsquo; cognitive states (e.g., emotion, stress, and fatigue) assessed by EEG signals vary depending on the hazards placed in the surrounding environment \u003csup\u003e\u003cspan additionalcitationids=\"CR51 CR52\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Specially, the neurocognitive process of hazard recognition was explored using ERPs and functional magnetic resonance imaging (fMRI), revealing that the recognition of environmental hazards involves two processes: an early detection process and a late emotional experience process \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Similarly, Ma et al. proposed the hazard perception two-stage model (HPTS) by EEG technology \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Further research has explored the effect of safety sign optimization methods incorporating scenarios on hazard identification \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 ERP characteristics related to hazard identification\u003c/h2\u003e \u003cp\u003eERP signals have been demonstrated to serve as measures for various aspects of safety indicators. Previous studies have confirmed that LPP is an evaluative indicator of emotion \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. P200 shows the early, automatic, rapid, and low-level processing of stimuli\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. P300 is elicited by selective attention, and a larger amplitude implies superior information processing\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, reflects conscious awareness and attention allocation \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. N100 reflects the processing bias of threatening information and early low-level visual processing and classification \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. N200 plays an important role in monitoring cognitive conflict \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Specially, ERPs experiments have shown that N1, N2, P200 amplitudes can be used to assess the negative effects of sleep deprivation in the early stage of drivers' hazard identification \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e,\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. Similarly, a study asserted that the high-hazard situations have a processing advantage in early processing stages through the measured N1 and P3 of different groups. Other studies found that theta wave is related to the execution of the identification process \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e, and the decrease of theta power reflects the decrease of task processing ability \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. Alpha wave is an indicator of alertness \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e, which is related to the activation of cortical nerve matrix related to information processing \u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. The beta wave can be affected by negative emotions \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. Therefore, the technique of ERPs has proven effective in revealing the neural mechanisms underlying hazard identification, making it highly suitable for further research in this domain.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Insufficient and objective indicators for quantifying hazard identification ability\u003c/h2\u003e \u003cp\u003eWhile hazard identification is a core process in safety management and considerable research has been dedicated to understanding this process, the field still lacks objective and standardized indicators for quantifying hazard identification ability. Firstly, traditional methods, such as surveys, questionnaires, and interviews, still dominate research in this area \u003csup\u003e\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. These tools, although useful, are limited in their ability to accurately represent an individual's cognitive and neural processes involved in hazard identification. For instance, a low score on a hazard identification test does not necessarily indicate poor identifying ability. In fact, it may reflect a greater complexity in the identification process, where an individual might be accessing a broader range of knowledge or engaging in more complex reasoning, which could lead to slower or less accurate responses on simple tests. Secondly, since hazard identification is regarded as a visual search task, the primary method has been to equip participants with eye-tracking devices to record oculomotor features \u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e,\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. Research has shown a strong correlation between eye movement patterns and hazard identification performance \u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. On the basis, researchers use eye-tracking equipment to obtain visual search data to measure the performance of hazard identification\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. Such conclusions have made valuable contributions to our understanding of hazard identification through the analysis of visual search patterns, which may not encompass the full complexity of the process. Therefore, we examined it from the perspective of neurocognitive science, which provides a more comprehensive framework for understanding the underlying cognitive and neural processes involved in hazard identification. Finally, although previous and current research have utilized neuroscientific technologies, such as fNIRS and EEG, to study the hazard identification process and its influencing factors, few quantitative metrics or grading standards for hazard identification ability have been established.\u003c/p\u003e \u003cp\u003eEEG, in particular, holds great promise due to its ability to provide real-time, direct measurements of brain activity during hazard identification tasks. Thus, by incorporating EEG-based metrics, this study provides a more nuanced, neurocognitive approach to assessing hazard identification ability, offering a clearer, more objective measure of identifying performance.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Method","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.1 Experimental materials\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eTo enhance validity, all stimulus materials for the hazard identification experiment were sourced from authentic risk scenarios prevalent at construction sites, focusing primarily on common hazards such as object strikes and fall risks. The experimental materials comprised both risk-free and risky stimulus images depicting standard construction scenes and safety behaviors. The stimulus images presented during the experiment had dimensions of 11.906 cm (width) \u0026times; 10.054 cm (height).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Equipment\u003c/h2\u003e \u003cp\u003eThe experiment employed the actiCHamp Plus EEG event-related potential (ERP) acquisition system from Brain Products, Germany, which included a 32-channel polar electrode cap conforming to international standards. Experimental protocols were designed using E-Prime 3.0 software to control the presentation of stimuli and record behavioral responses, while the Recorder software managed EEG data acquisition. In the data analysis phase, MATLAB 2021 facilitated the pre-processing and processing of EEG signals for each participant, and statistical analysis was conducted using SPSS 24.0 to ensure rigor in the evaluation of relevant metrics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Experimental tasks and processes\u003c/h2\u003e \u003cp\u003eThe primary objective of the hazard identification experiment was to detect the presence of hazardous factors in risky stimulus images. The experimental design employed a \"one stimulus - two key choices (S-K1/K2)\" paradigm, featuring 80 risky images and 35 standardized non-risky images, each presented twice. Subjects responded by pressing \"J\" if a hazard was detected and \"K\" if no hazard was present. The lead investigator provided a detailed overview of the purpose, procedures, and operational steps before commencing the experiment. Practice sessions were conducted to ensure participants were proficient with the task before proceeding to the formal trials. The experimental process of hazard identification is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe EEG experiment, implemented via E-Prime 3.0 software, consisted of practice and hazard identification phases, totaling 230 trials with each of the 115 images displayed twice in a randomized sequence. To prevent fatigue and maintain concentration, participants were given brief intermissions approximately every 40 trials, lasting 2\u0026ndash;3 minutes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Subjects of the survey\u003c/h2\u003e \u003cp\u003eUsing G*Power for a priori sample size analysis, the statistical method applied was repeated measures ANOVA. The effect size was 0.6, the α level 0.05, and the power 0.8, with two groups and three repeated measurements. The results indicated that the total sample size required is 14. In this study, 32 subjects were recruited from a construction site for a hazard identification experiment, comprising both frontline construction workers and managers aged 20 to 50 years. Data analysis was conducted on 30 valid datasets, as two subjects exhibited excessive blinking and head movement during the experiment, leading to the exclusion of their EEG data. All subjects were right-handed and reported to be in good mental health.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.5 ERP recording and pre-processing\u003c/h2\u003e \u003cp\u003eEEG data were recorded using the ActiCHamp Plus system made by Brain Products, featuring 32 electrodes placed according to the international 10\u0026ndash;20 system, with a sampling frequency of 500 Hz. The electrical impedance of electrodes was maintained below 5 kΩ throughout the experiment.\u003c/p\u003e \u003cp\u003eThe pre-processing workflow for the EEG data was performed using EEGLAB. The raw data was firstly filtered by band-pass filter (0.1\u0026thinsp;~\u0026thinsp;40 Hz) to remove high-frequency artifacts and low-frequency drifts, and notch filter (50 Hz) was applied to attenuate electrical line noise \u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. Subsequently, Independent Component Analysis (ICA) was utilized to identify and reject artifacts. The continuous EEG signals were segmented into epochs (-200 to 800ms) based on stimulus marks. Epochs with amplitudes exceeding\u0026thinsp;\u0026plusmn;\u0026thinsp;100 \u0026micro;V were then excluded from further analysis. Finally, the data were re-referenced using TP9 and TP10 electrodes as reference electrodes.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Behavioral data\u003c/h2\u003e \u003cp\u003eAt the end of the hazard identification experiment, participants' behavioral data were exported, organized, and analyzed. Indicator results for each participant were computed based on predefined behavioral formulas (1) and (2), as summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eHazard Identification Accuracy(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{H}\\text{I}\\text{A})=\\frac{\\:\\text{N}\\text{u}\\text{m}\\text{b}\\text{e}\\text{r}\\:\\text{o}\\text{f}\\:\\text{i}\\text{m}\\text{a}\\text{g}\\text{e}\\text{s}\\:\\text{c}\\text{o}\\text{r}\\text{r}\\text{e}\\text{c}\\text{t}\\text{l}\\text{y}\\:\\text{i}\\text{d}\\text{e}\\text{n}\\text{t}\\text{i}\\text{f}\\text{i}\\text{e}\\text{d}}{\\text{T}\\text{o}\\text{t}\\text{a}\\text{l}\\:\\text{n}\\text{u}\\text{m}\\text{b}\\text{e}\\text{r}\\:\\text{o}\\text{f}\\:\\:\\text{i}\\text{m}\\text{a}\\text{g}\\text{e}\\text{s}}\\)\u003c/span\u003e\u003c/span\u003e (1)\u003c/p\u003e \u003cp\u003eHazard Identification Speed(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{H}\\text{I}\\text{S})=\\frac{\\text{N}\\text{u}\\text{m}\\text{b}\\text{e}\\text{r}\\:\\text{o}\\text{f}\\:\\text{i}\\text{m}\\text{a}\\text{g}\\text{e}\\text{s}\\:\\text{i}\\text{d}\\text{e}\\text{n}\\text{t}\\text{i}\\text{f}\\text{i}\\text{e}\\text{d}\\left(\\text{s}\\text{h}\\text{e}\\text{e}\\text{t}\\right)}{\\text{T}\\text{o}\\text{t}\\text{a}\\text{l}\\:\\text{d}\\text{u}\\text{r}\\text{a}\\text{t}\\text{i}\\text{o}\\text{n}\\:\\text{o}\\text{f}\\:\\text{i}\\text{d}\\text{e}\\text{n}\\text{t}\\text{i}\\text{f}\\text{i}\\text{c}\\text{a}\\text{t}\\text{i}\\text{o}\\text{n}\\left(\\text{s}\\right)}\\)\u003c/span\u003e\u003c/span\u003e (2)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCalculation results of behavioral indicators for the subjects' hazard identification task\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubject\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e 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colname=\"c5\"\u003e \u003cp\u003e0.670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.519\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.860\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.812\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.697\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.740\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results of the behavioral indicators of the subjects were ranked separately, with the top ten participants in rapid hazard identification speed and high hazard identification accuracy classified as high performers. Conversely, the bottom ten participants in identification speed and accuracy were categorized as low performers, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGrouping of subjects\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavioral indicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh group (fast group) subjects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow group (slow group) subjects\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 4 8 9 14 17 20 23 24 26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 6 7 13 16 19 21 27 29 30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 9 15 17 18 20 21 22 23 26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 5 6 7 16 25 27 28 29 30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSubjects 4, 9, 17, 20, 23, and 26 demonstrated rapid and accurate hazard identification, while subjects 6, 7, 16, 27, 29, and 30 showed slower responses and lower identification accuracy. Behavioral data from each group were statistically summarized, and the Shapiro-Wilk test (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) confirmed normal distribution. An independent samples t-test indicated notable differences in hazard identification accuracy (t\u0026thinsp;=\u0026thinsp;5.27, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and speed (t\u0026thinsp;=\u0026thinsp;9.14, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) between high- and low-performing groups. These findings justify a detailed comparative EEG analysis between groups differentiated by hazard identification accuracy and response speed as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of descriptive statistics, normality test and independent samples t-test for each group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAverage value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(statistics) Standard deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eShapiro-Wilk test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eValue of statistic W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF-HIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e9.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.000**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-HIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.535\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH-HIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e5.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.000**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-HIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.2 ERPs characteristics of HIA\u003c/h2\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1 Results of time-domain analysis\u003c/h2\u003e \u003cp\u003e(1) Analysis of EEG topographic maps\u003c/p\u003e \u003cp\u003eThe group-average EEG topographic maps for the H-HIA and L-HIA groups was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Significant early activation (0\u0026ndash;300ms) is observed in the occipital and frontal regions, with the low-accuracy group exhibiting greater frontal activation compared to the high-accuracy group. In the later phase (400\u0026ndash;500ms), marked activation is evident in the parieto-occipital regions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(2) Analysis of N100 and P200\u003c/p\u003e \u003cp\u003eBased on brain topography and component waveforms, prominent N100 components are observed in the frontal and central regions (C4, Cz, F4, Fz), while distinct P200 components are noted in the parietal-occipital regions (P3, Pz, P4, O1, Oz, O2). Therefore, the analysis focused on the N100 and P200 components to explore differences between the H-HIA and L-HIA groups in detail.\u003c/p\u003e \u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the comparison between H-HIA and L-HIA groups at electrode sites F4, Fz, C4, and Cz shows pronounced N100 components and notable differences. Topographic maps indicate that, between 100\u0026ndash;200 ms, the activation in corresponding brain regions is greater in L-HIA group. Electrodes O1, O2, and Oz showed significant P200 components. Further analysis of the topographic map showed greater activation in the parieto-occipital regions between 200\u0026ndash;300 ms in the L-HIA group. Descriptive statistics of the mean amplitudes for N100 and P200 at corresponding electrode points were extracted for the hazard identification accuracy groups. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that the L-HIA group exhibited larger N100 and P200, consistent with topographic findings.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHazard identification accuracy comparison group on amplitude of N100 and P200\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEEG component\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003cp\u003e(high/low)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAverage amplitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c9\" namest=\"c4\"\u003e \u003cp\u003eElectrodes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCz\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eF4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eFz\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eC4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eN100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-1.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e-2.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-2.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e-2.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.528\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eO1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eOz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eO2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eP200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e3.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e3.243\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.871\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAaverage value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e6.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e6.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e6.877\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e3.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e3.792\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo assess differences in N100 and P200 amplitudes between groups, repeated-measures ANOVAs were conducted: 2 (groups: high, low) \u0026times; 4 (electrodes: F4, Fz, C4, Cz) for N100 and 2 (groups: high, low) \u0026times; 3 (electrodes: O1, O2, Oz) for P200. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e revealed a significant main effect of group (N100: F\u0026thinsp;=\u0026thinsp;4.599, p\u0026thinsp;=\u0026thinsp;0.046; P200: F\u0026thinsp;=\u0026thinsp;4.983, p\u0026thinsp;=\u0026thinsp;0.040), indicating notable amplitude differences between the H- and L-HIA groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInter-subject effect test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eType III Sum of Squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDegrees of Freedom\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePartial Eta Squared\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eN100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e286.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e286.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACCgroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eError\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eP200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1291.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1291.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46.511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACCgroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e138.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eError\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e444.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2 Results of frequency-domain analysis\u003c/h2\u003e \u003cp\u003eThe frequency-domain analysis in this study focuses on theta, alpha, and beta wavebands. Power spectra were obtained using fast Fourier transform (FFT) at key electrode sites (F3, Fz, F4, C3, Cz, C4, P3, Pz, P4, O1, Oz, O2). Mean power values for each frequency band were extracted to represent the corresponding brain regions, followed by variance analysis to evaluate differences across conditions.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e summarizes the descriptive statistics for theta, beta, and alpha wave power in the frontal, central, parietal, and occipital regions for participants with H- and L-HIA. Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e presents the main effect significance analysis of frequency domain indices across these brain regions.\u003c/p\u003e \u003cp\u003eNotable differences were identified in theta power between H- and L-HIA groups in the frontal (F\u0026thinsp;=\u0026thinsp;59.399, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), central (F\u0026thinsp;=\u0026thinsp;17.455, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and parietal regions (F\u0026thinsp;=\u0026thinsp;8.132, p\u0026thinsp;=\u0026thinsp;0.013), with higher theta power in the high accuracy group. Alpha power was also significantly higher in the parietal (F\u0026thinsp;=\u0026thinsp;7.064, p\u0026thinsp;=\u0026thinsp;0.019\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and occipital regions (F\u0026thinsp;=\u0026thinsp;5.793, p\u0026thinsp;=\u0026thinsp;0.03\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for the high accuracy group. Conversely, beta power was significantly higher in the L-HIA group across the frontal (F\u0026thinsp;=\u0026thinsp;13.452, p\u0026thinsp;=\u0026thinsp;0.003\u0026thinsp;\u0026lt;\u0026thinsp;0.05), central (F\u0026thinsp;=\u0026thinsp;11.954, p\u0026thinsp;=\u0026thinsp;0.004\u0026thinsp;\u0026lt;\u0026thinsp;0.05), parietal (F\u0026thinsp;=\u0026thinsp;6.603, p\u0026thinsp;=\u0026thinsp;0.019\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and occipital regions (F\u0026thinsp;=\u0026thinsp;9.923, p\u0026thinsp;=\u0026thinsp;0.007\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHazard identification accuracy comparison group on power values by brain region\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eHazard identification\u003c/p\u003e \u003cp\u003eaccuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003efrontal region power\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eCentral region power\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eParietal power\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eOccipital power\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean M / \u0026micro;V\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandard deviation SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean M / \u0026micro;V\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStandard deviation SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean M / \u0026micro;V\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eStandard deviation SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMean M / \u0026micro;V\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eStandard deviation SD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003etheta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.344\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ealpha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ebeta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of variance analysis of frequency-domain data of hazard identification accuracy comparison group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEncephalic region\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eType III sums of squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003etheta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrontal region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCentral region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParietal region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.013*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOccipital region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ebeta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrontal region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCentral region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParietal region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.019*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOccipital region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.007**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ealpha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrontal region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCentral region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParietal region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.019*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOccipital region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.030*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNotes: * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05;** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01༛*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.3 ERPs characteristics of HIS\u003c/h2\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e4.3.1 Results of time-domain analysis\u003c/h2\u003e \u003cp\u003e(1) Analysis of EEG topographic maps\u003c/p\u003e \u003cp\u003eAnalyzing the group-averaged EEG topographies of the F- and S- HIS groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), it was found that the low group had greater activation in the frontal and parieto-occipital regions between 200-400ms.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(2) Analysis of N100, P200 and P300\u003c/p\u003e \u003cp\u003eAnalysis of brain topographic maps and component waveforms revealed prominent N100 components in the frontal (F3, Fz, F4) and central (C3, Cz, C4) regions, as well as clear P200 and P300 components in the parieto-occipital region (P3, Pz, P4, O1, Oz, O2). This study thus focuses on examining the N100, P200, and P300 components elicited by the F- and S-HIS groups.\u003c/p\u003e \u003cp\u003eThe waveforms obtained from the electrodes F3, F4, Fz, C3, C4, and Cz revealed significant differences in the N100 components between the HIS groups, while electrodes O1, O2, and Oz showed prominent P200 and P300 components, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The average amplitudes of components at the respective electrode sites were extracted for the comparison group, as summarized in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. It can be seen that S-HIS group exhibited larger N100, P200, and P300 amplitudes, aligning with topographic map and waveform findings.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe amplitude of N100,P200,P300 in the comparison group of hazard identification speed\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEEG component\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSpeed\u003c/p\u003e \u003cp\u003e(Fast/Slow)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003cp\u003eamplitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c9\" namest=\"c4\"\u003e \u003cp\u003eElectrodes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eC4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eN100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.658\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSlow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-2.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eP200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eO1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eOz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eO2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e3.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e3.464\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSlow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e5.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e5.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e5.861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e3.138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eP300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eO1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eO2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e2.326\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSlow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e4.538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e4.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e4.869\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e2.942\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo investigate group differences in mean amplitudes of N100, P200, and P300 during the hazard identification task, repeated measures ANOVA was conducted: 2 (group: fast, slow) \u0026times; 6 (electrodes: F3, F4, Fz, C3, C4, Cz) and 2 (group: fast, slow) \u0026times; 3 (electrodes: O1, O2, Oz). The analysis, shown in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, revealed significant main effects for group comparisons (F\u0026thinsp;=\u0026thinsp;7.832, p\u0026thinsp;=\u0026thinsp;0.012; F\u0026thinsp;=\u0026thinsp;6.103, p\u0026thinsp;=\u0026thinsp;0.024; F\u0026thinsp;=\u0026thinsp;6.441, p\u0026thinsp;=\u0026thinsp;0.022), indicating notable differences in mean amplitudes of N100, P200, and P300 components between groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInter-subject effect test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eType III Sum of Squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDegrees of Freedom\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePartial Eta Squared\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eN100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e443.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e443.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpeed group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eError\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eP200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1234.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1234.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e79.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpeed group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eError\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e278.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eP300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e628.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e628.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpeed group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eError\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e202.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e4.3.2 Results of frequency-domain analysis\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e presents descriptive statistics for theta, beta, and alpha wave power across the frontal, central, parietal, and occipital regions for the hazard identification speed groups. Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e summarizes the main effect analysis of frequency domain indices for each brain region. Notable differences in beta power were observed between H-HIA and L-HIA groups in the frontal (F\u0026thinsp;=\u0026thinsp;8.697, p\u0026thinsp;=\u0026thinsp;0.009), central (F\u0026thinsp;=\u0026thinsp;10.355, p\u0026thinsp;=\u0026thinsp;0.006), parietal (F\u0026thinsp;=\u0026thinsp;6.603, p\u0026thinsp;=\u0026thinsp;0.019), and occipital (F\u0026thinsp;=\u0026thinsp;9.923, p\u0026thinsp;=\u0026thinsp;0.007) regions, with greater beta power in the low accuracy group. No notable differences were found in theta or alpha power across brain regions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHazard identification speed comparison group power values for each brain region\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eSpeed of hazard identification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eFrontal region power\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eCentral region power\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eParietal region power\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eOccipital power\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean M / \u0026micro;V2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandard deviation SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean M / \u0026micro;V2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStandard deviation SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean M / \u0026micro;V2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eStandard deviation SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMean M / \u0026micro;V2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eStandard deviation SD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003etheta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ealpha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ebeta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of variance analysis of frequency domain data of hazard identification speed comparison group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEncephalic region\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eType III sums of squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003etheta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrontal region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCentral region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParietal region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOccipital region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ebeta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrontal region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.009**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCentral region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParietal region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.011*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOccipital region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.031*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ealpha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrontal region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCentral region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParietal region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOccipital region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Characteristics of the hazard identification ability\u003c/h2\u003e \u003cp\u003eThe L-HIA and H-HIA groups exhibited notable differences in N100, P200 components, as well as beta, theta, and alpha power. The S-HIS group showed greater N100, P200, and P300 components, along with higher beta power, compared to the fast group. Considering both ERP feature components, theta power emerges as a distinct indicator for hazard identification accuracy, while the P300 component serves as a unique indicator for hazard identification speed.\u003c/p\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e4.4.1 The quantified indicator and grading standard for HIA\u003c/h2\u003e \u003cp\u003eThe average theta power in the hazard identification accuracy group was further analyzed to derive characteristic patterns. It was concluded that the average theta power may serve as an indicator for distinguishing high versus low hazard identification accuracy. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates theta power during hazard identification for the accuracy comparison group. Comparison revealed that theta wave power in the high accuracy group was relatively consistent, with the minimum power of 1.86 \u0026micro;V\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e and the average power of 1.99 \u0026micro;V\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The power of theta wave in the low group was also relatively consistent, with the maximum power of 1.56 \u0026micro;V\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e and the average power of 1.22 \u0026micro;V\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e4.4.2 The quantified indicator and grading standard for HIS\u003c/h2\u003e \u003cp\u003eThe average peak voltage of the P300 component in the speed comparison group was further analyzed to derive characteristic patterns. It was concluded that the average P300 amplitude can act as an indicator for determining fast versus slow hazard identification speed. And Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e presents the P300 peak voltage in the occipital region for the speed comparison group. It can be found that the amplitude of P300 in the group with F-HIS was relatively consistent, with the maximum peak voltage of 2.82 \u0026micro;V and the average peak voltage of 1.78 \u0026micro;V. The P300 component of the S-HIS group was also relatively consistent, with the minimum peak voltage of 3.43 \u0026micro;V, and the average peak voltage of 5.67 \u0026micro;V.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5 Discussion","content":"\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Characteristics of HIA\u003c/h2\u003e \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e \u003ch2\u003e5.1.1 Significant differences in time-domain characteristics among subjects with varying HIA\u003c/h2\u003e \u003cp\u003eThe results showed that L-HIA group demonstrated larger N100 and P200 components compared to the H-HIA group (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The N100 component, localized in the frontal region, reflects early attentional orientation, with larger amplitudes indicating greater resource allocation \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. Research has identified the N100 component as a reliable marker for the biased processing of threat-related cues \u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. Differentiating within a brief window of 80\u0026ndash;130 ms post-stimulus, the N100 underscores the rapid and intuitive nature of hazard perception \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. In addition, the N100 component has been shown to signify an early processing bias toward risky stimuli, enhancing the rapid allocation of attention to risk-related information \u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. Our findings indicate that individuals with lower accuracy in hazard identification tasks exhibit larger N100 amplitudes, reflecting an increased allocation of attentional resources to compensate for inefficiencies in identification. Compared to their high-accuracy counterparts, these individuals face greater challenges in early information processing, such as ambiguity or uncertainty in hazard recognition, necessitating greater attentional investment and resulting in enhanced N100 amplitudes. This suggests that heightened attentional engagement does not necessarily correlate with improved identification accuracy, and the allocation of immediate attentional resources alone may not directly influence hazard identification outcomes. Instead, the accuracy of hazard identification is also influenced by more stable factors, such as knowledge and experience \u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e,\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. Conversely, individuals with higher identification accuracy exhibit lower N100 amplitudes, indicating reduced allocation of attentional resources. This suggests that individuals with higher identification accuracy utilize attentional resources more efficiently and require fewer resources for hazard identification.\u003c/p\u003e \u003cp\u003eThe P200 component is an attention-related marker indicative of early, automatic, and rapid stimulus processing \u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e,\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e. Scholars widely acknowledge that the P200 component reflects attentional bias, with greater P200 amplitudes indicating increased allocation of attentional resources, irrespective of the stimulus valence \u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e. Salmi et al. demonstrated that uncertain hazard stimuli elicit significantly higher P200 amplitudes compared to certain hazards, highlighting its role as an indicator of hazard perception, with increased P200 amplitude correlating with heightened perceived hazard \u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. Additionally, P200 is associated with early stimulus detection, selective attention, and the efficiency of information categorization; smaller amplitudes indicate more efficient processing \u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e. For instance, sleep deprivation has been shown to significantly increase P200 amplitudes, suggesting impaired discrimination and processing speed, as well as diminished selective attention and interference resistance \u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e. In this study, individuals with lower hazard identification accuracy elicited larger P200 amplitudes, suggesting that they require greater attentional resources to process hazard-related information, reflecting lower processing efficiency. Hazard information is less intuitive or more challenging for them to interpret, resulting in increased uncertainty and heightened difficulty of identification. In contrast, individuals with higher identification accuracy exhibited smaller P200 amplitudes, indicating that they require fewer attentional resources for hazard identification and demonstrate higher processing efficiency.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section3\"\u003e \u003ch2\u003e5.1.2 Significant differences in frequency-domain characteristics among subjects with varying HIA\u003c/h2\u003e \u003cp\u003eThe H-HIA group exhibited greater theta and alpha power compared to the L-HIA group, whereas the L-HIA group showed higher beta power than the H-HIA group(see Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Previous studies have shown that theta band oscillations, which are widely distributed throughout the brain, play a critical role in higher cognitive functions, such as event and memory encoding, motor responses, working memory, novelty detection, and top-down control. Frontal-central theta activity, in particular, has been linked to the execution of these cognitive processes \u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e. Prefrontal theta power is associated with efficient cognitive processing \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e,\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. while reduced theta power signals impaired task processing capacity \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. In this study, individuals with high hazard identification accuracy demonstrated increased frontal theta power, reflecting their capacity to suppress irrelevant information and execute identification processes effectively, thereby enhancing identification accuracy. Conversely, the low accuracy group exhibited lower theta power, indicative of less efficient identification performance and diminished task processing abilities.\u003c/p\u003e \u003cp\u003eAlpha wave activity over the posterior scalp reflects neural mechanisms related to attention allocation, with decreased alpha power indicating active attentional engagement \u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e. In the study, the L-HIA group exhibited reduced alpha power in the parietal and occipital regions, indicating a greater allocation of attentional resources during the task. This finding aligns with the observation of significantly larger P200 amplitudes in this group, suggesting that individuals with lower identification accuracy require greater attentional resources for hazard identification.\u003c/p\u003e \u003cp\u003eRegarding indicator beta, it has been suggested that beta activity is related to emotional states (e.g., excitement, sadness, tension, etc.), and that negative emotions caused by noise, etc., can increase beta waves\u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e,\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e. The increase in beta power observed in the L-HIA group suggests that they are influenced by negative emotions during the later stages of the task\u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e, impacting their identification performance.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Characteristics of HIS\u003c/h2\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003e5.2.1 Significant differences in time-domain characteristics among subjects with varying HIS\u003c/h2\u003e \u003cp\u003eThe S-HIS group demonstrated larger N100, P200, and P300 components compared to the F-HIS group (see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e,Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). During the early stages of hazard information processing, individuals in the S-HIS group may face greater identification challenges, which leads to an increased focus on the hazard information. As a result, they must allocate more time and attentional resources to process the information across different contexts. This increased cognitive effort is reflected in significantly larger N100 and P200 amplitudes. Conversely, individuals in the F-HIS group demonstrated more efficient early-stage information processing and required less reliance on attentional and identification resources. Furthermore, P200 amplitude has been linked to the speed at which decision-makers identify key features of decision problems; smaller P200 amplitudes typically correspond to faster identification speeds \u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e, which is consistent with present study\u0026rsquo;s findings that individuals in the F-HIS group exhibited smaller P200 amplitudes.\u003c/p\u003e \u003cp\u003eThe P300 component, a positive waveform appearing approximately 300 ms post-stimulus, is indicative of attentional resource allocation \u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e, mainly reflecting the increase in attentional resource investment in emotionally or motivationally notable stimuli \u003csup\u003e\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e,\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e. According to the context-updating hypothesis, P300 is elicited when new information is detected during cognitive processing, prompting the brain to update its existing mental model stored in working memory. If no significant change is detected in the stimulus attributes, the current model is maintained, and only earlier components (e.g., N100, P200, N200) are observed, if new information is detected, the brain allocates more attentional resources and updates the original contextual information (accompanied by the P300 components) to modify future coping decisions and responses \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. In the present study, individuals in the S-HIS group exhibited a larger P300 amplitudes than the F-HIS group, suggesting that the S-HIS group experienced greater uncertainty and difficulty during hazard identification, required more attentional resources to process and identify hazards. In addition, larger P300 amplitudes indicate that individuals in the S-HIS group may undergo an information updating process during hazard identification. The P300 amplitude has also been associated with perceived hazard levels, higher perceived hazard leads to greater P300 amplitudes regardless of an individual\u0026rsquo;s impulsivity \u003csup\u003e\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e,\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u003c/sup\u003e. Hence, the S-HIS group with larger P300 amplitudes may process hazard information more thoroughly, potentially eliciting stronger negative emotions and resulting in longer response times. Conversely, the F-HIS group elicited lower P300 amplitude may face less uncertain hazard information, enabling efficient hazard identification without excessive attention resource allocation. Overall, individuals encountering more challenges in initial hazard processing required greater attentional and identification resources, experienced more identification conflict, and engaged in more controlled processing, resulting in slower hazard identification. In contrast, those able to process hazard information quickly displayed faster hazard identification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003e5.2.2 Significant differences in frequency-domain characteristics among subjects with varying HIS\u003c/h2\u003e \u003cp\u003eThe S-HIS rates demonstrated greater beta power compared to the F-HIS group (see Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Beta activity has been linked to emotional states such as excitement, sadness, and anxiety, with negative emotions\u0026mdash;often induced by factors like noise\u0026mdash;resulting in heightened beta power \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. In this study, the S-HIS group elicited greater beta power, demonstrating more obvious negative emotion. The significant P300 component observed in the time-domain analysis also verifies this point, suggesting that individuals in this group experience greater emotional interference during hazard identification. Accordingly, emotional interference in the S-HIS group may contribute to their slower hazard identification speed.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec35\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Indicators and grading standard of ERP features for hazard identification ability\u003c/h2\u003e \u003cp\u003eThe results indicated that theta wave and P300 components can be used as measurement indicators of HIA and HIS, which verifies the correctness of previous research results: P300 can dynamically track the progression of cognitive impairment, positioning it as a crucial biomarker for aiding in the diagnosis of mental and neurological cognitive disorders, as well as for evaluating disease progression, therapeutic efficacy, and prognosis \u003csup\u003e\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e. The decrease of theta wave power reflects the weakening of task processing ability \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. In addition, the results proved that P300 and theta can be used to assessing hazard identification ability, providing an objective physiological basis for related research. Table\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e shows the specific grading standard for hazard identification accuracy and speed.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGrading standard of HIA and HIS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic indicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eH-HIA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL-HIA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF-HIS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS-HIS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etheta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;1.99\u0026micro;V\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1.22\u0026micro;V\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1.78\u0026micro;V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;5.67\u0026micro;V\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec36\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Implications and limitation\u003c/h2\u003e \u003cp\u003eObjective data on hazard identification were obtained through ERP experiments, and after time-frequency transformation, the mean power of theta waves during hazard identification was compared against the grading standard to directly assess hazard identification accuracy. Similarly, the mean peak of the P300 component was evaluated against its grading standard to assess the speed of hazard identification.\u003c/p\u003e \u003cp\u003eOn this basis, quantifying hazard identification ability can provide a foundation for developing more scientific evaluation standards and tailored methods for vocational training and safety education. Specifically, for practitioners in high-hazard industries (such as construction, chemical, manufacturing, etc.), EEG tests can be used to assess their sensitivity and response speed to potential hazards in actual operation, to develop personalized training programs and improve the overall workplace safety level. Enterprises can leverage this method to monitor and evaluate employees' routine hazard recognition capabilities, allowing for the timely identification of those who may require additional training. This proactive approach enhances the prevention of potential incidents, optimizes safety management strategies, and minimizes workplace accident rates. In the field of education and training, the results can be used as a reference tool to evaluate the level of students' cognitive ability. For example, drivers, pilots and emergency rescue personnel who have higher requirements for response speed and accuracy can be regularly assessed by EEG tests to ensure that they continue to have good emergency response capabilities.\u003c/p\u003e \u003cp\u003eIn general, through the objective assessment and application of hazard identification capabilities, it can effectively improve the hazard management level and personnel safety awareness in various fields, and ultimately achieve a safer and more efficient production and working environment.\u003c/p\u003e \u003cp\u003eHowever, this study has some limitations. Firstly, while the experimental materials consisted of authentic on-site risk stimulation images, the advancement of virtual reality technology allows for the creation of more immersive risk scenarios. These VR environments can enhance subsequent quantitative assessments of individual hazard identification ability by providing participants with a more realistic and engaging experience. Secondly, the subject selection was limited, future research should aim to increase both the sample size and industry diversity to establish a more universally applicable standard for measuring hazard identification ability.\u003c/p\u003e \u003c/div\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003eUnder the theoretical background of safety science and cognitive neuroscience, this study examines the process of hazard identification, with a primary focus on assessing and quantifying the accuracy and speed of hazard identification. The research investigates individuals' hazard identification abilities through objective EEG measurements and provides a comprehensive analysis of the associated EEG characteristics. The key findings are as follows.\u003c/p\u003e \u003cp\u003e(1) L-HIA and H-HIA groups induced significantly different N100 and P200 components as well as beta, theta, and alpha power.\u003c/p\u003e \u003cp\u003e(2) F-HIS and S-HIS groups elicited significantly different N100, P200, and P300 components as well as beta power.\u003c/p\u003e \u003cp\u003e(3) Theta wave and the P300 component can serve as distinct ERP features for assessing hazard identification accuracy and speed, respectively. The mean power of theta waves in the central frontal region (P\u003csub\u003elow\u003c/sub\u003e \u0026lt; 1.22 \u0026micro;V\u0026sup2;, P\u003csub\u003ehigh\u003c/sub\u003e \u0026gt; 1.99 \u0026micro;V\u0026sup2;) can be utilized as a grading standard for evaluating hazard identification accuracy. Similarly, the average peak voltage of the P300 component in the occipital region (U\u003csub\u003efast\u003c/sub\u003e \u0026lt; 1.78 \u0026micro;V, U\u003csub\u003eslow\u003c/sub\u003e \u0026gt; 5.67 \u0026micro;V) can act as a grading standard for assessing the speed of hazard identification.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eS.Z. conceptualized the study, supervised the research, and reviewed/edited the manuscript. S.T. contributed to conceptualization, developed the methodology, and wrote the original draft. S.Y. conducted the investigation. X.S. provided resources and participated in manuscript revision. Y.Z. and B.W. contributed resources. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analysed during the current study are publicly available in the Zenodo repository at: https://doi.org/10.5281/zenodo.15273667.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCarter, G. \u0026amp; Smith, S. D. Safety hazard identification on construction projects. \u003cem\u003eJournal of Construction Engineering and Management\u003c/em\u003e 132, 197\u0026ndash;205, (2006). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1061/(asce)0733\u003c/span\u003e\u003cspan address=\"10.1061/(asce)0733\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e-9364(2006)132:2(197).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaslam, R. A. et al. Contributing factors in construction accidents. \u003cem\u003eAppl. 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Rehabilitation Theory Pract.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e, 69\u0026ndash;78 (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Hazard identification, Event-related potential technique, Hazard identification accuracy, Hazard identification speed","lastPublishedDoi":"10.21203/rs.3.rs-6434600/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6434600/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe prevention of accidents is aided by having a strong ability to identify hazards. The objective and quantitative assessment of hazard identification ability through event-related potential (ERP) experiments is of significance for person-job safety matching in high-risk positions. In this study, we first designed and conducted an electroencephalogram (EEG) experiment related to the hazard identification process. Subsequently, two indicators reflecting the hazard identification process were extracted from the behavioral data obtained during the experiment: hazard identification speed and hazard identification accuracy. Finally, time-domain and frequency-domain analysis methods were employed to investigate the ERP characteristics and patterns in the hazard identification process. The results showed that: (1) the low and high hazard identification accuracy groups (L-HIA and H-HIA) demonstrated significantly different N100 and P200 components, as well as beta, theta, and alpha power; (2) the fast and slow hazard identification speed groups (F-HIS and S-HIS) demonstrated significantly different N100, P200, and P300 components and beta power; (3) the average power value of theta wave in the central frontal region (P\u003csub\u003elow\u003c/sub\u003e \u0026lt; 1.22 \u0026micro;V\u0026sup2;, P\u003csub\u003ehigh\u003c/sub\u003e \u0026gt; 1.99 \u0026micro;V\u0026sup2;) can be used as the grading standard for hazard identification accuracy; (4) the average peak voltage value of the P300 component in the occipital region (U\u003csub\u003efast\u003c/sub\u003e \u0026lt; 1.78 \u0026micro;V, U\u003csub\u003eslow\u003c/sub\u003e \u0026gt; 5.67 \u0026micro;V) can be used as the grading standard for hazard identification speed. It\u0026rsquo;s conducive for enterprises and individuals to master the hazard identification ability of employee to train and improve their ability pointedly.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"The ERP characteristics in the process of hazard identification","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-20 19:08:28","doi":"10.21203/rs.3.rs-6434600/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-28T05:58:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-24T03:30:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"240380696791452437372749791408619248101","date":"2025-10-01T06:45:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-20T14:59:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"81234051360634945348187564668657111572","date":"2025-07-18T17:24:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"52206179020933842898152007033098261234","date":"2025-05-24T08:32:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"16060201119303523254368686172035262200","date":"2025-05-19T07:22:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-19T05:51:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-19T05:48:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-28T04:12:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-24T11:44:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-04-12T12:30:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5944b226-304a-4bdd-834e-082a9c22fea1","owner":[],"postedDate":"May 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":48717772,"name":"Biological sciences/Neuroscience"},{"id":48717773,"name":"Biological sciences/Psychology"},{"id":48717774,"name":"Health sciences/Biomarkers"},{"id":48717775,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-01-26T16:09:54+00:00","versionOfRecord":{"articleIdentity":"rs-6434600","link":"https://doi.org/10.1038/s41598-026-35883-x","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-01-20 15:59:08","publishedOnDateReadable":"January 20th, 2026"},"versionCreatedAt":"2025-05-20 19:08:28","video":"","vorDoi":"10.1038/s41598-026-35883-x","vorDoiUrl":"https://doi.org/10.1038/s41598-026-35883-x","workflowStages":[]},"version":"v1","identity":"rs-6434600","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6434600","identity":"rs-6434600","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}