A Validation Study of Mobile EEG for Empathy-for-Pain Research

A Validation Study of Mobile EEG for Empathy-for-Pain Research | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 27 August 2025 V1 Latest version Share on A Validation Study of Mobile EEG for Empathy-for-Pain Research Authors : Felipe Rojas-Thomas , Fiorella Macchiavello 0009-0008-9746-5379 , Vicente Soto 0000-0003-3494-4213 , Álvaro Rivera-Rei 0000-0002-3674-7291 , Daniel O’Byrne , José Bórquez , Consuelo Ruiz , David Huepe 0000-0001-8351-5314 [email protected] , and Sebastian Contreras-Huerta Authors Info & Affiliations https://doi.org/10.22541/au.175628018.85835327/v1 334 views 146 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Empathy, the ability to share and understand others’ emotional states, is essential for prosocial behavior, social cohesion, and mental health. While neuroimaging research has advanced our understanding of the neural mechanisms underlying empathy, most studies rely on stationary equipment in controlled laboratory settings, limiting access and generalizability. This study aimed to validate a mobile low-cost EEG device, to study empathy for pain. Participants viewed painful and non-painful limb scenarios while their neural responses were recorded using both research-grade stationary as well as a mobile EEG system. We compared event-related potential (ERP) responses between systems and assessed whether mobile EEG could replicate findings from a traditional empathy-for-pain paradigm. While stationary EEG (64 channels) provided superior signal quality, mobile EEG (32 channels) reliably captured key late ERP components associated with empathy, particularly the late positive potential (LPP). Interestingly, we also found that mobile EEG was sensitive to subtle variations in task design, suggesting its potential utility for validating novel paradigms within the empathy-for-pain framework. These findings support the use of mobile EEG as a viable tool for studying empathy for pain in diverse and ecologically valid settings, promoting methodological accessibility and inclusive neuroscience. 1. Introduction Empathy is a complex, multidimensional construct that allows individuals to understand and share the emotional experiences of others, making it a cornerstone of human social life (Almeida et al., 2024; Cameron et al., 2019; Coll, 2018; Toppi et al., 2022). It fosters social connectedness, supports meaningful interpersonal relationships, and plays a key role in psychological well-being and adaptive social functioning (Almeida et al., 2024; Bird & Viding, 2014; Cameron et al., 2019). Beyond its interpersonal benefits, empathy also influences the development, expression, and clinical presentation of various psychiatric conditions (Bird & Viding, 2014; Coll, 2018; Eklund & Meranius, 2020). Due to its wide-ranging impact, empathy is considered a protective factor for prosocial behavior, social cohesion, and mental health (Bernhardt & Singer, 2012; Eisenberg & Miller, 1987; Ibáñez et al., 2023; Mar, 2010). Understanding its underlying neural mechanisms may help inform strategies to promote empathy at the societal level, with potential benefits for public mental health and social well-being (Decety & Jackson, 2004; Singer & Lamm, 2009). Over the past few decades, social and cognitive neuroscience have extensively investigated the neurocognitive underpinnings of empathy (Almeida et al., 2024; Coll, 2018; Decety & Jackson, 2004; Meng et al., 2023). A prominent framework in this research is empathy for pain, which has been widely studied using neuroimaging and electrophysiological methods (Cao et al., 2015; Decety et al., 2010; Fan & Han, 2007; Singer & Lamm, 2009; Singer et al., 2004; Xiang et al., 2018). Observing others in pain reliably induces an isomorphic affective state in the observer, which can activate altruistic motivations (Batson, 1991; Brown & Brown, 2015; Contreras-Huerta et al., 2020; Contreras-Huerta, 2023; Contreras-Huerta et al., 2023; De Waal, 2007; Decety et al., 2015; Hein et al, 2015; Ibáñez et al., 2023). These processes involve the integration of higher-order cognitive mechanisms with automatic emotional responses, allowing for a vicarious connection to others’ distress (Bernhardt & Singer, 2012; Decety et al., 2015). Prior research has identified two interdependent neural pathways that underpin empathy (Decety & Jackson, 2004; Lamm et al., 2011; Singer & Lamm, 2009; Zaki & Ochsner, 2012). The first pathway, associated with affective resonance, involves regions such as the anterior cingulate cortex, amygdala, and anterior insula (Bernhardt & Singer, 2012; Contreras-Huerta et al., 2013; Craig, 2008; Fan et al., 2010). The second pathway, linked to cognitive perspective-taking, is associated with the medial prefrontal cortex and temporoparietal junction (Almeida et al., 2024; Cao et al., 2015; Lamm et al., 2010; Shamay-Tsoory et al., 2008; Shamay-Tsoory, 2010; Xiang et al., 2018). This dual-pathway model highlights the interplay between automatic, emotional components of empathy and higher-order cognitive processes. This framework aligns with findings from event-related potentials (ERPs), a methodological approach within electroencephalography (EEG), which provides insights into the temporal dynamics of empathy-related neural responses (Decety & Michalska, 2009; Fan & Han, 2007; Lavin et al., 2011; Luo & Han, 2014; Sessa et al., 2014). When participants observe others in pain, an early affective response emerges around 100–200 milliseconds post-stimulus, characterized by a negative deflection over the fronto-central area (e.g., N1 and N2). Later ERP components, linked to more deliberative cognitive aspects of empathy, appear as positive deflections around 350 milliseconds post-stimulus over the centro-parietal region (e.g., P3 and the late positive potential—LPP) (Fan & Han, 2008; Lopez-Calderon & Luck, 2014; Meng et al., 2023; Park & Donaldson, 2019; Sun et al., 2017; Xiang et al., 2018; Contreras-Huerta et al., 2014; Shen & Han, 2012; Decety et al., 2010). Early ERP components are associated with automatic attentional allocation, while later components reflect cognitive appraisal and emotion regulation, reinforcing the interaction between automatic and reflective processes in empathy (Almeida et al., 2024; Coll, 2018; Decety et al., 2010; Sun et al., 2017; Vecchio & De Pascalis, 2022; Woodman, 2010; Wu & Han, 2021). Despite substantial progress, neuroimaging research on empathy remains limited by its reliance on controlled laboratory conditions and a predominant focus on WEIRD (Western, Educated, Industrialized, Rich, and Democratic) populations, which restricts the generalizability of findings (Henrich et al., 2010). Vulnerable populations—who often experience contextual uncertainty, socioeconomic precarity, and chronic stress—are underrepresented in neuroscientific studies, yet their experiences may significantly alter the neurocognitive mechanisms underlying empathy and prosocial behavior (Contreras-Huerta et al., 2020; Gamble et al., 2023; Ibáñez et al., 2023; Twenge et al., 2007; Zaki, 2014). Growing recognition of these limitations has spurred efforts to expand sampling beyond university settings, where most psychological experiments still rely on student convenience samples (Henrich et al., 2010; Nielsen et al., 2017; Rad et al., 2018). Ensuring that empathy research incorporates inclusive methodologies is essential for producing findings that accurately reflect human diversity and can be applied across social and cultural contexts (Henrich et al., 2010). Emerging technologies like portable neuroimaging devices offer promising solutions by enhancing ecological validity and expanding research beyond WEIRD populations (Klapprott & Debener, 2024; Lopez-Calderon & Luck, 2014; Park & Donaldson, 2019). Mobile EEG systems allow the investigation of neural dynamics in real-world settings while being significantly more cost-effective than traditional laboratory-based systems, making them feasible for research in resource-constrained regions (Almeida et al., 2024; Toppi et al., 2022). Low-income regions in the world, including Latin America and the Global South, have large populations living in vulnerable contexts, where studying empathy could yield critical insights. Mobile EEG systems, such as Emotiv’s EPOC FLEX, have demonstrated performance comparable to traditional EEG through advancements in hardware and signal processing (Chi et al., 2013; Craik et al., 2023; Krigolson et al., 2017; Lau-Zhu et al., 2019; Svensson et al., 2019; Valentin et al., 2018). These devices are non-invasive, wireless, and cost-effective, making them valuable for both general cognitive research and clinical applications (Craik et al., 2023; Krigolson et al., 2017; Sawangjai et al., 2019; Svensson et al., 2019). While technical limitations, such as signal-to-noise ratio (SNR) issues, persist, validation studies support the viability of mobile EEG for neuroimaging applications (Chi et al., 2013; Craik et al., 2023; Kataoka et al., 2022; Krigolson et al., 2017; Sawangjai et al., 2019; Stopczynski et al., 2014; Svensson et al., 2019; Valentin et al., 2018). Here, we aimed to validate the Emotiv EPOC FLEX 32-channel mobile EEG device for studying empathy for pain. To achieve this, we pursued three specific objectives: (1) to validate the mobile EEG system by assessing its capacity to replicate established ERP findings from a traditional empathy-for-pain paradigm (Jackson et al., 2004); (2) to compare electrophysiological responses between the mobile EEG and a research-grade stationary EEG system; and (3) to evaluate whether mobile EEG can serve as a reliable tool for testing novel task paradigms within the empathy-for-pain framework. Participants observed images of painful and non-painful limb scenarios while their electrocortical responses were recorded. We compared ERP responses between stationary EEG and the Emotiv EPOC FLEX systems. Furthermore, we included a recently developed task within the empathy-for-pain framework (Meng et al., 2012, 2013, 2019, 2023), comparing results both within (stationary and mobile EEG, respectively) and between systems. This allowed us to test whether EEG can also serve as a validation tool for novel paradigms in empathy-for-pain research. Our findings indicate that, while stationary EEG offers superior signal quality, mobile EEG reliably captures key empathy-related ERP components, supporting its utility for future research. These results suggest that mobile EEG technologies can serve as viable alternatives for studying empathy for pain in diverse settings, promoting methodological accessibility and inclusive neuroscience. By prioritizing more representative research paradigms, this study contributes to the advancement of empathy science, with potential applications for global mental health and social well-being. 2. Materials and methods 2.1 Participants 60 volunteers participated in the experiment. All participants were recruited from the student population of the Adolfo Ibañez University of Chile. All volunteers had a normal or corrected-to-normal vision, and had no reported neurological, psychiatric, or endocrine disease history. Subjects were placed into the Biosemi group (Biosemi system: N = 30, 19 female) or Emotiv group (Emotiv system: N = 30, 8 female). Before the experiment, the subjects read and, when they agreed, signed an informed consent form approved by the ethical committee of the Adolfo Ibañez University of Chile. 2.2 Pain Empathy Protocol The study aimed to assess participants’ neural empathic responses to others’ pain using two experimental paradigms: the pain empathy paradigm proposed by Meng et al. (2023) (Version V2) and the well-established paradigm by Jackson et al. (2004) (Version V1). Each paradigm included image pairs depicting painful and neutral situations, sourced from the database referenced in Jackson et al. (2004) and the Empathy for Limb Pain Picture Database (Meng et al., 2023). Version V1 contained 128 images (64 depicting pain), Version V2 contained 136 images (68 depicting pain). In each session 4 randomly selected pairs of images were used for practice and were not presented during the task, leaving 120 images in V1 and 128 in V2 for ERP analysis. For V1, these images were previously validated in a pool of approximately 100 students to assess both valence and intensity ratings. For V2, we employed the original stimulus set validated by Meng et al. (2023). The Biosemi and Emotiv groups performed the task in a dimly lit, sound-isolated room using a 24” Full HD (1920 × 1080) monitor (Figure 1A). During the task, participants focused on a white cross displayed on a black screen for a randomly determined duration between 1500 and 1800 ms. Then, an image depicting either a painful (pain condition) or neutral (neutral condition) situation was presented for 1500 ms. Images for evaluation were randomly selected with a 0.4 probability during the task. For these selected images, participants were asked to assess either pain intensity or unpleasantness on a 97-point Likert scale, ranging from 1 to 97, with each evaluation type being randomly chosen with equal probability (0.5). Images were presented in two blocks with a self-paced rest in between. In each block half of the image pairs (randomly selected) were presented in random order (Figure 1B). Figure 1. Pain Empathy Protocol. A) Schematic representation of the temporal sequence of image presentation blocks. B) Schematic representation of stimulus presentation. 2.3 EEG recording and analysis 2.3.1 Biosemi EEG recording and preprocessing The EEG data were recorded using a BioSemi ActiveTwo system (BioSemi, Amsterdam, Netherlands) at a sampling rate of 512 Hz, using 64 Ag/AgCl electrodes mounted according to the extended 10/20 system. All electrodes were referenced to the CMS and DRL electrodes during recording, with offset maintained between ±20 mV. Off-line the data were preprocessed and analyzed with MATLAB (MathWorks, Inc., Natick, MA) using EEGLAB toolbox (version 2024.2.1) (Delorme & Makeig, 2004) and ERPLAB (version 12.0.0) (Lopez-Calderon & Luck, 2014), automated with in-house scripts. For each subject, the two task blocks were concatenated into a continuous sequence. EEG data were high-pass filtered at 0.5 Hz with a 12 dB/oct roll-off IIR Butterworth filter. The data were then visually inspected to identify flat channels and channels with voltages beyond ±100 𝜇V, and marking them as bad. Independent component analysis (ICA) was performed, and components associated with eye blinks or movements, heart beats, muscle contraction, channel noise, or electrical interference were identified using the ICLabel toolbox (Pion-Tonachini et al., 2019), then components with an IC class probability score between 0.8 and 1 in those artifactual categories were removed. At this point bad channels were interpolated using spherical-spline interpolation, as implemented in EEGLAB. 2.3.2 Emotiv EEG recording and preprocessing The EEG data were recorded using an Emotiv Epoc Flex Gel system (Emotiv, San Francisco, United States), with a 128 Hz sampling rate from 32 Ag/AgCl electrodes mounted according to the extended 10/20 system. During the recording, impedances were kept below 20 kΩ by adjusting the Flex electrodes until they displayed a ‘green’ indicator in the Emotiv Pro software, which corresponds to impedance values below 20 kΩ. Off-line preprocessing was the same as with the BioSemi recordings. 2.3.4. Event-related potential analysis The data was filtered using a Butterworth low-pass filter with a 35 Hz cutoff and a 12 dB/octave roll-off, and re-referenced to infinity, using the three-concentric-sphere head model with the REST 1.2 toolbox (Dong et al., 20217). Continuous data were epoched into 1200 ms segments (200 ms before and 1000 ms after the visual stimulus onset). The pre-stimulus window was used to correct baseline activity in each trial. The remaining artefacts were automatically rejected with ERPLAB using 3 criteria: absolute voltage over 100 𝜇V; moving peak-to-peak absolute difference over 100 𝜇V, with a moving windows width of 200 ms and window step of 50 ms; and absolute between successive points over 30 𝜇V (see Supplementary Table 1 for accepted and rejected epochs by system). The remaining segments were then averaged for each combination of electrode and image type. 2.3.5. Signal-to-Noise Ratio analysis SNR was used to evaluate the relative strength of an evoked signal compared to background noise for both electrophysiological recording devices. Mathematically, it is defined as: \begin{equation} SNR\ =\frac{\text{Peak\ Amplitude}}{\text{Baseline\ Variance}}\nonumber \\ \end{equation} In this formula, the peak amplitude represents the maximum absolute value of the average signal within predefined time windows of interest, serving as an indicator of the strength of the ERP. The baseline variance quantifies the variability of the signal during a baseline period, which is assumed to reflect noise or spontaneous activity. A higher SNR value indicates a clearer distinction between the evoked signal and the background noise, thereby enhancing the reliability of signal detection and subsequent analysis. The baseline variance is calculated as follows: \begin{equation} Baseline\ Variance\ =\ \frac{1}{N}\sum_{i=1}^{N}{}\left(x_{i}-\mu\right)^{2}\nonumber \\ \end{equation} In this equation, N denotes the total number of data points,\({x_{i}}\) represents each individual data point, and μ is the mean of the dataset, computed as:\(\mu=\frac{1}{N}\sum_{i=1}^{N}{}x_{i}\) .Variance reflects the average squared deviation of the data points from the mean. A higher variance indicates greater dispersion in the data, while a lower variance suggests that the values are closer to the mean. In the context of SNR calculation, variance serves as a critical measure of baseline noise, enabling a robust evaluation of the signal’s reliability and detectability. 2.3.6. ERP Analysis by Recording System To characterize the ERP components in each recording system, we grouped the event-related potentials generated from the two image databases used. Subsequently, two types of analyses were performed. 2.3.6.1 Mass Univariate analysis For intra-subject exploratory analysis, a mass univariate test was conducted using the Mass Univariate ERP Toolbox (Groppe et al., 2011b). This approach enabled comparisons at each electrode and time point of interest, allowing for a more temporally and topologically focused exploration of ERP components based on a priori specifications. Permutation-based mass univariate cluster corrections were applied to analyses (Groppe et al., 2011b) to account for multiple comparisons with the following parameters: Condition (Neutral vs Pain), 10.000 permutations for specific time window: [0–800 ms], using an alpha of 0.05, and average neighbours channels of 4 (electrodes considered neighbours for clustering). Mass Univariate ERP Toolbox [Computer software]. Available from: https://openwetware.org/wiki/Mass_Univariate_ERP_Toolbox). 2.3.6.2 Time-Window Analysis of Evoked Potentials A statistical analysis was conducted based on channels of interest associated with the processing of the stimuli. Each channel was selected along the z-axis, specifically focusing on the frontal (Fz), central (Cz), and posterior (Pz) topographies. The maximum amplitude points within four temporal windows (100–200 ms, 200–300 ms, 300–500 ms, and 500–600 ms), corresponding to the N1, N2, P3, and LPP components, were used to define the time windows for analysis. For each component, the analysis window (N1 ± 20 ms; N2 ± 50, P3 and LPP ± 100 ms) was determined based on the identified amplitude peak. 2.3.7 ERP Analysis of Pain Empathy Paradigms To characterize the electrophysiological responses of both recording systems and their sensitivity to different pain empathy paradigms, we separated the ERPs according to the database used: Version 1 (V1) and Version 2 (V2). We then conducted two types of analyses: 2.3.7.1 Factorial Mass Univariate Test (FMUT) Analysis For exploratory ERP analysis, we employed the Factorial Mass Univariate Test using the FMUT toolbox (Groppe et al., 2011). This approach allowed for the comparison of each electrode and time point of interest, enabling a more temporally and topographically focused examination of ERP differences based on predefined factors. To correct for multiple comparisons, permutation-based cluster corrections were applied with the following parameters: Factors – Version (V1 vs. V2) and Condition (Neutral vs. Pain), 10,000 permutations, time window [0–800 ms], alpha = 0.05, and an average of 4 neighboring channels for clustering. The FMUT toolbox is available at: https://github.com/ericcfields/FMUT/releases. 2.3.7.2 Time-Window Analysis of Evoked Potentials A complementary statistical analysis was performed based on channels of interest associated with stimulus processing. Specifically, we focused on midline electrodes along the z-axis: frontal (Fz), central (Cz), and posterior (Pz) regions. Four temporal windows were defined according to the typical latencies of ERP components: 100–200 ms (N1), 200–300 ms (N2), 300–500 ms (P3), and 500–600 ms (LPP). For each component, the analysis window was centered around the observed peak amplitude: ±20 ms for N1, ±50 ms for N2, and ±100 ms for both P3 and LPP. 2.4. Statistical analysis 2.4.1. Behavioral analysis For statistical analysis, two types of analyses were conducted. First, empathy-related pain perception and perceived unpleasantness between groups recorded with Biosemi and Emotiv systems were compared using a two-way repeated measures ANOVA, with condition and system as main factors. Post hoc multiple comparisons were performed between specific conditions within each system using the Šídák’s correction for multiple comparisons. Secondly, another two-way repeated measures ANOVA was conducted to compare empathy-related pain perception and perceived discomfort, considering condition and version as factors. Post hoc analyses followed the previously mentioned procedure, with multiple comparisons specifically between conditions and versions within each recording system. In all cases, statistical significance was set at P < 0.05, and reported p-values were two-tailed. 2.4.2 Evoked Potentials and Signal-to-Noise Ratio analysis For each channel, the mean amplitude of each ERP component was compared between the pain and neutral conditions within the predefined analysis windows. For the Biosemi system, these windows were: N1 (165 ± 10 ms), N2 (270 ± 50 ms), P3 (395 ± 50 ms), and LPP (686 ± 50 ms). For the Emotiv system, the analysis windows were: N1 (152 ± 10 ms), N2 (261 ± 50 ms), P3 (384 ± 50 ms), and LPP (678 ± 50 ms). A repeated-measures ANOVA was conducted with system and condition as factors, setting a significance level of p < 0.05. Šídák’s correction for multiple comparisons was applied as a post hoc test. Multiple within-group comparisons were performed between the neutral and pain conditions. To assess the signal-to-noise ratio (SNR) quality associated with each ERP component for the BioSemi and Emotiv devices, the same analysis windows and statistical methods were used. In this case, multiple between-group comparisons were conducted, contrasting the neutral and pain conditions. All statistical analyses were performed using GraphPad Prism (version 7, GraphPad Software, San Diego, CA). 2.4.3 Mass Univariate analysis A total of 1000 random intra-subject permutations of the data were generated by the algorithm to estimate the null distribution (Manly, 2007). For each permutation, all t-scores corresponding to uncorrected p-values less than 0.05 (i.e., extreme t-scores) were grouped into clusters with neighboring extreme t-scores. This relatively conservative significance threshold was chosen to capture only relevant clusters. Electrodes within approximately 5.24 cm were considered spatial neighbors, and adjacent time points were considered temporal neighbors. The sum of the t-scores within each cluster defined the “mass” of that cluster. The most extreme cluster mass from each of the 2880 test sets was used to estimate the null-hypothesis distribution. P-values for each observed cluster were derived based on the percentile ranking of its mass within the permutation distribution. This cluster-based test leverages the fact that ERP effects are more likely than noise to extend across many adjacent electrodes and time points, making it the most appropriate mass univariate procedure for detecting broadly distributed effects (Groppe et al., 2011a; Maris & Oostenveld, 2007). 2.4.4 Factorial Mass Univariate Test (FMUT) Analysis The FMUT toolbox is based on the mass univariate approach for analyzing ERPs, which involves conducting ANOVAs at each time point and electrode of interest, followed by a correction method to control the Type I error. In our study, we used a permutation-based cluster mass correction, which evaluates the significance of clusters rather than individual electrodes or time points. To define clusters, a threshold is first established as the F-value that would be significant without correction. Spatially and temporally adjacent points exceeding this threshold are grouped into clusters, and the sum of all F-values within each cluster is calculated to obtain the cluster mass statistic. A null distribution is generated through permutation, and observed clusters that exceed the 1 – α percentile of this distribution are considered statistically significant. To calculate effects in factorial designs, FMUT simplifies the data into the most straightforward equivalent design for statistical testing (Welch, 1990). 3. Results 3.1 Behavioral Results First, we compared subjective perceptions of empathic pain and unpleasantness between participants in the BioSemi and Emotiv groups to identify significant similarities or differences that might influence electrophysiological outcomes measured by both systems. A two-way repeated measures ANOVA on behavioral data revealed a significant main effect of condition on perceived empathic pain [F(1, 58) = 951.9, P < 0.0001; M ± SD: Neutral = 13.26 ± 1.25, Pain = 74.47 ± 9.19]. Post hoc analyses using Šídák’s correction revealed that, under the Pain condition, participants reported significantly greater empathic pain compared to the Neutral condition in both the BioSemi group (M ± SD: Neutral = 12.37 ± 11.50, Pain = 80.93 ± 10.31, P < 0.0001) and Emotiv group (M ± SD: Neutral = 14.14 ± 9.32, Pain = 67.96 ± 9.20, P < 0.0001). Additionally, there was a significant main effect of the system [F(1, 58) = 11.55, P = 0.0012]. Post-hoc analyses using Šídák’s correction indicated that, under the Pain condition, participants in the BioSemi group reported significantly greater empathic pain compared to those in the Emotiv group (M ± SD: BioSemi = 80.93 ± 10.31, Emotiv = 67.96 ± 9.20, P < 0.01). No significant differences were found between systems under the Neutral condition (M ± SD: BioSemi = 12.37 ± 11.50, Emotiv = 14.14 ± 9.32, P = 0.74). Furthermore, a significant interaction between System and Condition was detected [F(1, 58) = 11.55, P = 0.012] (Figure 2A). In the analysis of perceived unpleasantness, a two-way repeated measures ANOVA revealed a significant main effect of Condition [F(1, 58) = 490.4, P < 0.0001; M ± SD: Neutral = 17.29 ± 0.22, Pain = 66.35 ± 11.36]. Post hoc analyses using Šídák’s correction revealed that, under the Pain condition, participants reported significantly greater perceived unpleasantness compared to the Neutral condition in both the BioSemi group (M ± SD: Neutral = 17.13 ± 12.86, Pain = 74.38 ± 13.64, P < 0.0001) and Emotiv group (M ± SD: Neutral = 17.44 ± 11.68, Pain = 58.31 ± 13.99, P < 0.0001). Additionally, there was a significant main effect of System [F(1, 58) = 9.53, P = 0.003]. Post-hoc analyses using Šídák’s correction indicated that, under the Pain condition, participants in the BioSemi group reported significantly greater unpleasantness compared to those in the Emotiv group (M ± SD: BioSemi = 74.38 ± 13.65, Emotiv = 58.31 ± 13.99, P < 0.0001). No significant differences were found between systems under the Neutral condition (M ± SD: BioSemi = 17.13 ± 12.86, Emotiv = 17.44 ± 11.68, P = 0.87). Furthermore, a significant interaction between System and Condition was detected [F(1, 58) = 13.67, P = 0.005] (Figure 2B). Figure 2. Subjective ratings of empathic pain and unpleasantness. A) Empathic pain scores for Neutral and Pain images. B) Unpleasantness scores for Neutral and Pain images. The Biosemi group is shown in orange bars, and the Emotiv group in blue bars. Error bars represent ± S.E.M., and asterisks indicate statistically significant differences or significant factorial effects (p < 0.05). We separately analyzed empathy responses in terms of subjective pain or subjective unpleasantness elicited by the two empathy-for-pain paradigms employed: the well-established paradigm proposed by Jackson et al. (2004) (Version V1) and the updated paradigm introduced by Meng et al. (2023) (Version V2). Responses to stimuli (Neutral and Pain) were grouped according to their corresponding paradigm version (V1 or V2). For the Biosemi group, a two-way repeated measures ANOVA indicated that perceived empathic pain was not modulated by version [F(1, 29) = 0.67, P = 0.41; M ± SD: V1 = 47.50 ± 47.73, V2 = 46.40 ± 49.05]. However, subjective pain ratings were significantly modulated by condition [F(1, 29) = 481.4, P < 0.0001; M ± SD: Neutral = 12.73 ± 1.43, Pain = 81.17 ± 0.11]. Post-hoc analyses using Šídák’s correction revealed that, under the Pain condition, participants reported significantly greater empathic pain in both V1 (M ± SD: Neutral = 12.75 ± 14.22, Pain = 81.25 ± 12.33, P < 0.0001) and V2 (M ± SD: Neutral = 11.71 ± 11.59, Pain = 81.08 ± 10.55, P < 0.0001) (Figure 3A). Similarly, the analysis for perceived unpleasantness showed no effect of version [F(1, 29) = 0.96, P = 0.96; M ± SD: V1 = 46.54 ± 37.70, V2 = 44.98 ± 43.25]. However, perceived unpleasantness was significantly modulated by condition [F(1, 29) = 316.5, P < 0.0001; M ± SD: Neutral = 17.13 ± 3.87, Pain = 74.38 ± 1.67]. Post-hoc tests (Šídák-corrected) confirmed that, under the Pain condition, participants reported significantly higher unpleasantness in both V1 (M ± SD: Neutral = 19.87 ± 16.24, Pain = 73.20 ± 15.00, P < 0.0001) and V2 (M ± SD: Neutral = 14.39 ± 12.01, Pain = 75.56 ± 14.21, P < 0.0001). Furthermore, a significant condition and version interaction was found [F(1, 29) = 8.07, P < 0.01] (Figure 3C). For the Emotiv group, a two-way repeated measures ANOVA indicated that perceived empathic pain was not modulated by version [F(1, 29) = 1.30, P = 0.26; M ± SD: V1 = 41.82 ± 37.16, V2 = 40.30 ± 38.94]. However, subjective pain ratings were significantly modulated by condition [F(1, 29) = 411.7, P < 0.0001; M ± SD: Neutral = 14.15 ± 1.96, Pain = 67.97 ± 0.18]. Post-hoc analyses using Šídák’s correction revealed that, under the Pain condition, participants reported significantly greater empathic pain than in the Neutral condition in both V1 (M ± SD: Neutral = 15.53 ± 10.52, Pain = 68.09 ± 9.61, P < 0.0001) and V2 (M ± SD: Neutral = 12.75 ± 10.23, Pain = 67.83 ± 10.52, P < 0.0001) (Figure 3B). Perceived unpleasantness showed a significant effect of version [F(1, 29) = 8.72, P = 0.006; M ± SD: V1 = 39.63 ± 37.70, V2 = 36.13 ± 43.25]. Post-hoc tests (Šídák-corrected) confirmed that, under the Neutral condition, participants reported significantly higher unpleasantness in V1 compared to V2 (M ± SD: V1 Neutral = 20.43 ± 13.97, V2 Neutral = 14.46 ± 10.98, P < 0.01). Furthermore, perceived unpleasantness was significantly modulated by condition [F(1, 29) = 180.0, P < 0.0001; M ± SD: Neutral = 17.45 ± 4.22, Pain = 58.31 ± 0.71]. Post-hoc tests (Šídák-corrected) confirmed that, under the Pain condition, participants reported significantly higher unpleasantness than in the Neutral condition in both V1 (M ± SD: Neutral = 20.43 ± 13.97, Pain = 58.81 ± 14.18, P < 0.0001) and V2 (M ± SD: Neutral = 14.46 ± 10.01, Pain = 57.80 ± 15.30, P < 0.0001) (Figure 3D). Figure 3. Subjective ratings of empathic pain and unpleasantness comparing paradigm versions V1 and V2. Panel A shows empathic pain scores for the Biosemi group, while Panel B shows empathic pain scores for the Emotiv group. Panel C displays unpleasantness scores for the Biosemi group, and Panel D presents unpleasantness scores for the Emotiv group. Black bars represent the Neutral condition, and red bars represent the Pain condition. Solid bars indicate the V1 paradigm, while dotted bars indicate the V2 paradigm. Error bars represent ± S.E.M., and asterisks indicate statistically significant differences or significant factorial effects (p < 0.05). 3.2 Electrophysiological Results 3.2.1 Results by Recording System 3.2.1.1 Signal-to-Noise Ratio Result Early components The voltage amplitude topographies and SNRs for early components (N1 and N2) and late components (P3 and LPP) were computed and visualized for the BioSemi and Emotiv system (Figures 4 and 5). Visual inspection indicated that the characteristic topography of the early components (N1 and N2) was present in both conditions (Pain and Neutral) across both systems; however, the BioSemi group exhibited a more pronounced response (Figures 4A, B). In terms of SNR topography, the BioSemi system showed significantly higher signal levels relative to baseline variance across all scalp electrodes in both conditions. In contrast, the Emotiv system exhibited increased signal strength in electrodes corresponding to the topography of each early component (N1 and N2) (Figures 4A, B). A two-way repeated-measures ANOVA conducted on specific electrodes associated with the topography of the components (Fz) revealed no significant main effect of the system factor, indicating comparable SNR quality across both recording systems. For N1, [F (1, 58) = 1.41, P = 0.23], post hoc analyses using Šídák’s correction confirmed that the groups did not exhibit significant differences in SNR values in either the Neutral (M ± SD: BioSemi = 16.80 ± 19.26, Emotiv = 8.92 ± 8.72, P = 0.12) and Pain conditions (M ± SD: BioSemi = 16.51 ± 15.51, Emotiv = 15.64 ± 19.94, P = 0.97) (Figure 4C). For N2, [F (1, 58) = 0.14, P = 0.70], Šídák’s post hoc test further indicated that the groups had similar SNR values in the Neutral condition (M ± SD: BioSemi = 19.02 ± 20.34, Emotiv = 15.75 ± 17.22, P = 0.75) and Pain condition (M ± SD: BioSemi = 18.59 ± 19.41, Emotiv = 18.85 ± 18.70, P = 0.99) (Figure 4D). Figure 4. Signal-to-Noise Quality for Early Components. (A, B) Scalp distributions of voltage amplitude and SNR for the N1 and N2 components in response to pain and neutral images. (C) Average SNR quantification for the N1 and N2 components in the Biosemi and Emotiv groups. Error bars represent ± S.E.M., and asterisks indicate statistically significant differences (*p < 0.05). Late components Visual inspection of the scalp SNR maps reveals that the BioSemi system exhibited higher signal strength across all electrodes. In contrast, the Emotiv system showed more localized signal enhancement, primarily limited to electrodes associated with the generation of each specific component—central and posterior sites for the P3, and central sites for the LPP (Figure 5A, B). A two-way repeated-measures ANOVA did not reveal a significant main effect of the system factor for the late components, suggesting no substantial differences in SNR quality between the BioSemi and Emotiv systems overall. For the P3 component at Pz, the analysis showed no significant main effect of system [F(1, 58) = 1.40, P = 0.24]. Post hoc comparisons using Šídák’s correction indicated no significant differences in SNR between systems in either the Neutral condition (M ± SD: BioSemi = 23.91 ± 18.30, Emotiv = 17.81 ± 17.56, P = 0.26) or the Pain condition (M ± SD: BioSemi = 18.20 ± 11.08, Emotiv = 16.02 ± 16.19, P = 0.83). Although the main effect of condition approached significance [F(1, 58) = 2.89, P = 0.09], no significant differences were observed in the post hoc tests (Figure 5C). For the LPP component at Cz, a similar pattern was found. The ANOVA did not yield a significant main effect of system [F(1, 58) = 1.54, P = 0.21], and post hoc analyses using Šídák’s correction showed no significant differences in SNR between systems in either the Pain condition (M ± SD: BioSemi = 16.05 ± 20.61, Emotiv = 11.29 ± 12.05, P = 0.31) or the Neutral condition (M ± SD: BioSemi = 9.60 ± 7.98, Emotiv = 7.24 ± 9.18, P = 0.74). However, a significant main effect of condition was detected [F(1, 58) = 7.38, P = 0.008]. Post hoc comparisons indicated that the BioSemi system exhibited significantly higher SNR values in the Pain condition compared to the Neutral condition (M ± SD: Neutral = 9.60 ± 7.98, Pain = 16.05 ± 20.61, P < 0.04) (Figure 5D). Figure 5. Signal-to-Noise Quality for Late Components . (A, B) Scalp distributions of voltage amplitude and SNR for the P3 and LPP components in response to pain and neutral images. (C) Average SNR quantification for the P3 and LPP components in the Biosemi and Emotiv groups. Error bars represent ± S.E.M., and asterisks indicate statistically significant differences (*p < 0.05). 3.2.1.2 Cluster-Based Permutation Analysis of Pain Empathy A mass univariate permutation analysis was performed at the intra-subject level to analyze the sensitivity of the electroencephalographic systems in distinguishing between the Pain and Neutral conditions. In the Biosemi group, two distinct clusters emerged (P < 0.05). A negative cluster was observed between approximately 270 and 410 ms, predominantly involving central and posterior electrodes along the midline (z-axis) as well as lateral electrodes in both hemispheres. Additionally, a large positive cluster, spanning from about 450 to 800 ms, was identified in both the Biosemi and Emotiv groups. This positive cluster encompassed most midline electrodes, along with anterior, central, and posterior sites across both hemispheres (Figure 6A, B). Figure 6. Cluster topography relative to mass univariate test. Warm-colored rectangles indicate electrodes/time points where ERPs under the Pain condition are significantly more positive compared to the Neutral condition, whereas cool-colored rectangles indicate significantly more negative ERPs. Gray rectangles denote electrodes/time points with no significant differences. Electrodes are arranged topographically along the y-axis: left hemisphere electrodes are displayed at the top, right hemisphere electrodes at the bottom, and midline electrodes are shown in the center, ordered from anterior to posterior. 3.2.1.3 Evoked Potentials Results for Pain Empathy Early components To evaluate the sensitivity of the recording systems to each experimental condition associated with bottom-up processing, we analyzed the temporal windows associated with the N1 component in response to the stimulus in frontal areas (Fz channel). A two-way repeated-measures ANOVA indicated that N1 amplitude was not modulated by condition [F(1, 58) = 1.64, P = 0.20; M ± SD: Neutral = -2.61 ± 0.49, Pain = -2.74 ± 0.46]. The analysis also did not reveal a system effect, with the Emotiv group showing similar N1 amplitudes in both conditions compared to the BioSemi group [F(1, 58) = 1.64, P = 0.13; M ± SD: BioSemi = -3.01 ± 0.05, Emotiv = -2.34 ± 0.08] (Figure 7A, C). Additionally, we assessed the temporal windows associated with the N2 component in frontal areas (Fz channel). A two-way repeated-measures ANOVA indicated that N2 amplitude was not modulated by condition [F(1, 58) = 1.15, P = 0.28; M ± SD: Neutral = -3.40 ± 0.58, Pain = -3.50 ± 0.62] nor by system factor [F(1, 58) = 2.13, P = 0.28; M ± SD: BioSemi = -3.88 ± 0.09, Emotiv = -3.03 ± 0.05] (Figure 7B, D). Figure 7. Effect of pain empathy on N1 and N2 components. The N1 and N2 components were elicited in response to a visual stimulus. The black line represents the neutral image, while the red line represents pain-related images for the Biosemi (A) and Emotiv (B) groups. Panels C and D show the average quantification of N1 and N2 amplitudes for the pain and neutral conditions in the Biosemi and Emotiv groups. Error bars indicate ± S.E.M., and asterisks denote statistically significant differences (*p < 0.05). Late components Subsequently, we evaluated the effect of empathy on top-down processes in both recording systems by focusing on temporal windows associated with late components. Specifically, we analyzed the impact of pain empathy on the P3 component at the Pz electrode (Figure 8A, B) and the LPP component at the Cz electrode (Figure 9A, B) in both groups. The repeated-measures ANOVA revealed that the P3 component was significantly modulated by condition [F(1, 58) = 6.09, P < 0.05; M ± SD: Neutral = 3.59 ± 0.54, Pain = 3.36 ± 0.20]. Post hoc analyses with Šídák’s correction showed that the BioSemi group exhibited significantly greater P3 amplitudes in the Neutral condition compared to the Pain condition (M ± SD: Neutral = 3.98 ± 2.14, Pain = 3.50 ± 2.17, P < 0.01). In contrast, the Emotiv group showed no significant difference in P3 amplitude between conditions (M ± SD: Neutral = 3.20 ± 1.48, Pain = 3.21 ± 1.64, P = 0.99). Additionally, the P3 component was not significantly affected by the system factor, indicating comparable amplitudes across recording systems [F(1, 48) = 0.13, P = 0.71; M ± SD: BioSemi = 3.74 ± 0.34, Emotiv = 3.21 ± 0.004] (Figure 8C). Figure 8. Effect of pain empathy on the P3 component. The P3 components were elicited in response to a visual stimulus. The black line represents the neutral image, while the red line represents pain-related images for the Biosemi (A) and Emotiv (B) groups. Panel C shows the average quantification of P3 amplitude for the pain and neutral conditions in the Biosemi and Emotiv groups. Error bars indicate ± S.E.M., and asterisks denote statistically significant differences (*p < 0.05). Next, we analyzed the LPP component, which is associated with sustained attention and emotional processing, to examine its modulation by pain empathy in both recording systems (Figure 9A, B). The repeated-measures ANOVA revealed a main effect of condition, showing a significant increase in LPP amplitude for the Pain condition across both groups [F(1, 58) = 44.73, P < 0.0001; M ± SD: Neutral = 1.04 ± 0.22, Pain = 1.73 ± 0.20]. Post hoc analyses using Šídák’s correction confirmed that LPP amplitude was significantly greater in the Pain condition for both the BioSemi group (M ± SD: Neutral = 1.20 ± 1.05, Pain = 1.87 ± 1.08, P < 0.0001) and the Emotiv group (M ± SD: Neutral = 0.88 ± 0.75, Pain = 1.97 ± 1.08, P < 0.0001). In contrast, no significant main effect of the system factor was detected, indicating comparable LPP responses across both systems [F(1, 58) = 1.84, P = 0.18; M ± SD: BioSemi = 1.54 ± 0.47, Emotiv = 1.23 ± 0.50] (Figure 9C). Figure 9. Effect of pain empathy on the LPP component. The LPP components were elicited in response to a visual stimulus. The black line represents the neutral image, while the red line represents pain-related images for the Biosemi (A) and Emotiv (B) groups. Panel C shows the average quantification of LPP amplitude for the pain and neutral conditions in the Biosemi and Emotiv groups. Error bars indicate ± S.E.M., and asterisks denote statistically significant differences (*p < 0.05). 3.2.2 Results by Task Version In order to evaluate the sensitivity of each recording system to pain empathy-related components, we separately examined the responses to the two empathy-for-pain paradigms used: the well-established paradigm by Jackson et al. (2004) (Version V1) and the updated version proposed by Meng et al. (2023) (Version V2). Stimuli were grouped according to their respective paradigm (V1 or V2), and both early and late ERP components in response to Neutral and Pain conditions were analyzed. This exploratory analysis included both cluster-based methods and time-window analyses specific to each ERP component. Furthermore, we aimed to determine whether this approach could be used as a tool to validate new pain empathy paradigms by comparing their capacity to elicit reliable neural markers across different recording systems. 3.2.2.1 Cluster-Based Permutation Analysis of Pain Empathy Paradigms For the Biosemi system, a significant cluster was identified for the condition factor (P < 0.0001), spanning a temporal window from 223 to 801 ms. This cluster covered widespread scalp regions, with the greatest spatial extent at CPz, while the peak temporal mass was found at 363 ms. (Figure 10A). In the Emotiv system, a significant main effect of the condition factor was observed in a cluster (P < 0.0001), with a temporal window spanning 461 to 797 ms. The cluster was mainly distributed over frontocentral and parietal regions, with the greatest spatial extent at CP2, which also coincided with the peak of the temporal mass at 633 ms. The highest temporal peak occurred at 641 ms, suggesting sustained activity in midline and lateralized centro-parietal regions (Figure 10B). Figure 10. Cluster Topography for the Factorial Univariate Test on the Condition Factor. The raster diagram illustrates the significant main effect of the condition factor, as determined by a permutation test based on the cluster mass statistic. Each colored electrode/time point represents a significant p-value (p < 0.05) along with its corresponding F-value. Gray rectangles indicate electrodes and time points where no significant effect was observed. The electrodes are organized along the y-axis in a topographically meaningful manner: electrodes located on the left and right sides of the head are grouped toward the top and bottom of the figure, respectively, while midline electrodes are displayed in the center. Within these three groupings, the y-axis from top to bottom corresponds to the scalp from anterior to posterior. For the Biosemi system, a highly significant cluster (P < 0.0001) was detected for the version factor, covering a broad temporal window from 113 to 691 ms. This cluster showed widespread activation across the scalp, with the largest spatial peak at FC1 and the highest temporal peak at 285 ms. The maximum spatial mass was observed at PO7, while the highest temporal mass occurred at 265 ms (Figure 11A). For the Emotiv system, a highly significant cluster (P < 0.0001) was detected for the version factor spanned from 188 to 461 ms, mainly localized in parietal sites, with a spatial and mass peak at O1, while the temporal mass peak occurred at 250 ms and temporal peak occurred at 242 (Figure 11B). Figure 11. Cluster Topography for the Factorial Univariate Test on the Version Factor. The raster diagram illustrates the significant main effect of the version factor, as determined by a permutation test based on the cluster mass statistic. Each colored electrode/time point represents a significant p-value (p < 0.05) along with its corresponding F-value. Gray rectangles indicate electrodes and time points where no significant effect was observed. The electrodes are organized along the y-axis in a topographically meaningful manner: electrodes located on the left and right sides of the head are grouped toward the top and bottom of the figure, respectively, while midline electrodes are displayed in the center. Within these three groupings, the y-axis from top to bottom corresponds to the scalp from anterior to posterior. Additionally, a significant interaction between the factors version and condition was observed exclusively in the BioSemi system. This significant cluster (P = 0.032) extended from 488 to 668 ms over parietocentral electrodes, predominantly in the left hemisphere, with a spatial peak at electrode CP1 and a mass peak at electrode P3. The temporal mass peak occurred at 590 ms, while the temporal peak was observed at 613 ms (Figure 12A). In contrast, no significant interaction was observed in the Emotiv system Figure 12B). Figure 12. Cluster Topography for the Factorial Univariate Test of the Version × Condition Interaction. The raster diagram illustrates the significant interaction effect between the factors Version and Condition, as determined by a permutation test based on the cluster mass statistic. Each colored electrode/time point represents a significant p-value (p < 0.05) along with its corresponding F-value. Gray rectangles indicate electrodes and time points where no significant effect was observed. The electrodes are organized along the y-axis in a topographically meaningful manner: electrodes located on the left and right sides of the head are grouped toward the top and bottom of the figure, respectively, while midline electrodes are displayed in the center. Within these three groupings, the y-axis from top to bottom corresponds to the scalp from anterior to posterior. These findings reveal significant condition-related effects in both EEG systems, with BioSemi showing earlier and more widespread activations, while Emotiv exhibited a later and more localized response (Figure 10). Moreover, version-related effects elicited broad spatiotemporal activation patterns in BioSemi, in contrast to the more focal effects observed in the frontocentral and centroparietal regions with Emotiv (Figure 11). Additionally, the BioSemi system was sensitive enough to detect interaction effects between condition and version, highlighting its capacity to differentiate subtle variations across experimental conditions (Figure 12). 3.2.2.2 Evoked Potential Analysis of Pain Empathy Paradigms Early components Figure 13 shows the topography and waveforms of the Fz channel for the N1 and N2 components in both conditions (Neutral and Pain), across both versions, as well as the delta (V2 minus V1), for the Biosemi and Emotiv systems. Visual inspection indicates that, in both paradigms, the N1 and N2 components elicited by visual stimuli exhibit a similar topographic distribution (Figure 13A, B), and also showed a similar wave pattern (Figure 13C, D). However, the N2 component is reduced in version V2 across both systems and in both the neutral and pain conditions. Figure 13. Topography of N1 and N2 associated with Pain Empathy Paradigms. Scalp voltage distributions for the N1 and N2 components in response to pain and neutral images in both paradigms for the Biosemi and Emotiv systems (A, B). Signal associated with the N1 and N2 components elicited by neutral and pain images in both paradigms for the Biosemi and Emotiv systems (C, D). Solid lines represent the V1 paradigm, while dashed lines represent the V2 paradigm. Black lines indicate the neutral condition, and red lines indicate the pain condition. To assess the impact of pain images on bottom-up processes, we analyzed the temporal windows associated with the N1 component in response to the stimulus in frontal areas (Fz channel). For the Biosemi system a two-way ANOVA with repeated measures indicated that N1 amplitude was not modulated by condition [F (1, 29) = 1.30, P = 0.26; M ± SD: Neutral = -2.96 ± 0.18, Pain = -3.07 ± 0.18] or version [F (1, 29) = 1.84, P = 0.18 M ± SD: V1 = -3.14 ± 0.07, V2 = -2.88 ± 0.07] (Figure 14A). For the Emotiv system N1 amplitude was not modulated by condition [F (1, 29) = 0.70, P = 0.40 M ± SD: Neutral = -2.27 ± 0.05, Pain = -2.41 ± 0.04] or version [F (1, 29) = 0.09, P = 0.75 M ± SD: V1 = -2.30 ± 0.10, V2 = -2.37 ± 0.09] (Figure 14B). Additionally, to evaluate the impact of pain images on involuntary attention processes, we assessed the temporal windows associated with the N2 component in frontal areas (Fz channel). For the Biosemi system, a two-way ANOVA with repeated measures indicated that N2 amplitude was modulated by version [F (1, 29) = 87.58, P < 0.0001; M ± SD: V1 = -4.56 ± 0.10; V2 = -3.20 ± 0.07]. Post hoc analyses using Šídák’s correction confirmed that N2 amplitude was greater in the V1 version for both the Neutral condition (M ± SD: V1 Neutral = -4.48 ± 3.01, V2 Neutral = -3.14 ± 2.88, P < 0.0001) and the pain condition (M ± SD: V1 Pain = -4.63 ± 2.95, V2 Pain = -3.25 ± 2.83, P < 0.0001). The analysis showed that N2 was not modulated by the condition factor [F (1, 29) = 1.26, P = 0.27; M ± SD: Neutral = -3.81 ± 0.94, Pain = -3.94 ± 0.97] (Figure 14C). For the Emotiv system N2 amplitude was modulated by version [F (1, 29) = 19.21, P = 0.001; M ± SD: V1 = -3.43 ± 0.02, V2 = -2.65 ± 0.13]. Post hoc analyses using Šídák’s correction confirmed that N2 amplitude was greater in the V1 version for Neutral condition (M ± SD: V1 Neutral = -3.45 ± 1.54, V2 Neutral = -2.52 ± 1.45, P < 0.0001), and pain condition (M ± SD: V1 Pain = -3.41 ± 1.75, V2 Pain = -2.72 ± 1.53, P = 0.0001). The analysis showed that N2 was not modulated by the condition factor [F (1, 29) = 0.25, P = 0.61; M ± SD: Neutral = -2.99 ± 0.65, Pain = -3.06 ± 0.48] (Figure 14D). Figure 14. Amplitude of N1 and N2 Components in Pain Empathy Paradigms. Bar graphs representing the amplitude of the N1 and N2 components in response to pain and neutral images for both the Biosemi and Emotiv systems. Panel A shows the N1 amplitude for the Biosemi system, while Panel B shows the N1 amplitude for the Emotiv system. Panel C displays the N2 amplitude for the Biosemi system, and Panel D presents the N2 amplitude for the Emotiv system. Black bars represent the Neutral condition, and red bars represent the Pain condition. Solid bars indicate the V1 paradigm, while dotted bars represent the V2 paradigm. Error bars ± S.E.M. and asterisks indicate statistically significant differences (*: p < 0.05). Late components Subsequently, we assessed the effect of empathy on top-down processes by analyzing temporal windows associated with the late component to examine its modulation by pain empathy in both versions of the pain empathy paradigms. Figure 15 presents the topography and waveforms of the Pz channel for the P3 component in both conditions (Neutral and Pain), across both versions, and the delta (V2 minus V1), in the Biosemi and Emotiv systems. Visual analysis indicates that in both paradigms, the P3 components associated with visual stimuli exhibit a similar topographic distribution (Figure 15 A, B), and also showed a similar wave pattern (Figure 15C, D). Figure 15. Topography of P3 associated with Pain Empathy Paradigms . Scalp voltage distributions for the P3 component in response to pain and neutral images in both paradigms for the Biosemi and Emotiv systems (A, B). Signal associated with the P3 component elicited by neutral and pain images in both paradigms for the Biosemi and Emotiv systems (C, D). Solid lines represent the V1 paradigm, while dashed lines represent the V2 paradigm. Black lines indicate the neutral condition, and red lines indicate the pain condition. For the Biosemi system, a two-way repeated-measures ANOVA revealed that P3 amplitude was significantly modulated by condition [F(1, 29) = 19.20, p < 0.0001; M ± SD: Neutral = 3.98 ± 0.35, Pain = 3.50 ± 0.07]. Post hoc analyses with Šídák’s correction indicated that significant differences between conditions were present only in version V1 (M ± SD: V1 Neutral = 4.23 ± 2.34, V1 Pain = 3.55 ± 2.47, p < 0.0001), whereas no significant differences were found for version V2 (M ± SD: V2 Neutral = 3.73 ± 2.08, V2 Pain = 3.45 ± 1.99, p = 0.99). Additionally, the main effect of version was not significant [F(1, 29) = 2.724, p = 0.10; M ± SD: V1 = 3.89 ± 0.48, V2 = 3.59 ± 0.19]. Furthermore, the analysis revealed an interaction between condition and version [F(1, 29) = 2.724, p = 0.10], indicating that the difference in P3 amplitude between the Neutral and Pain conditions was primarily observed in version V1 (Figure 17A). For the Emotiv system, a two-way ANOVA with repeated measures indicated that P3 amplitude was not modulated by version factor [F (1, 29) = 1.05, P = 0.31; M ± SD: V1 = 3.30 ± 0.16, V2 = 3.12 ± 0.17] or condition factor [F (1, 29) = 0.32, P = 0.57; M ± SD: Neutral = 3.20 ± 0.30, Pain = 3.21 ± 0.04] (Figure 17B). We analyzed the LPP component, which is associated with sustained attention and emotional processing, to examine its modulation by pain empathy in both versions of the pain empathy paradigms. Figure 16 presents the topography and waveforms of the Cz channel for the LPP component in both conditions (Neutral and Pain), across both versions, and the delta (V2 minus V1), in the Biosemi and Emotiv systems. Visual analysis indicates that in both paradigms, the LPP components associated with visual stimuli exhibit a similar topographic distribution (Figure 16A, B), and also showed a similar wave pattern (Figure 16C, D). However, the LPP component appears weaker in the neutral condition across both systems. Figure 16. Topography of LPP associated with Pain Empathy Paradigms. Scalp voltage distributions for the LPP component in response to pain and neutral images in both paradigms for the Biosemi and Emotiv systems (A, B). Signal associated with the LPP component elicited by neutral and pain images in both paradigms for the Biosemi and Emotiv systems (C, D). Solid lines represent the V1 paradigm, while dashed lines represent the V2 paradigm. Black lines indicate the neutral condition, and red lines indicate the pain condition. Figure 17. Amplitude of P3 and LPP Components in Pain Empathy Paradigms. Bar graphs representing the amplitude of the P3 and LPP components in response to pain and neutral images for both the Biosemi and Emotiv systems. Panel A shows the P3 amplitude for the Biosemi system, while Panel B shows the P3 amplitude for the Emotiv system. Panel C displays the LPP amplitude for the Biosemi system, and Panel D presents the LPP amplitude for the Emotiv system. Black bars represent the Neutral condition, and red bars represent the Pain condition. Solid bars indicate the V1 paradigm, while dotted bars represent the V2 paradigm. Error bars ± S.E.M. and asterisks indicate statistically significant differences (*: p < 0.05). We analyzed the LPP component, which is associated with sustained attention and emotional processing, to examine its modulation by pain empathy. For the Biosemi system, the repeated-measures ANOVA revealed a main effect of condition, indicating a significant increase in LPP amplitude for the Pain condition [F (1, 29) = 47.69, P < 0.0001; M ± SD: Neutral = 1.20 ± 0.06, Pain = 1.87 ± 0.15]. Post hoc analyses using Šídák’s correction confirmed that the LPP exhibited a greater amplitude in the Pain condition for both the V1 version (M ± SD: V1 Neutral = 1.24 ± 1.19, V1 Pain = 1.77 ± 1.08, P < 0.01) and V2 version (M ± SD: V2 Neutral = 1.16 ± 1.03, V2 Pain = 1.98 ± 1.29, P < 0.0001). However, the analysis showed that LPP amplitude was not modulated by version [F (1, 29) = 0.264, P = 0.61; M ± SD: V1 = 1.50 ± 0.37, V2 = 1.57 ± 0.58] (Figure 17C). For the Emotiv system, the repeated-measures ANOVA also revealed a main effect of condition, showing a significant increase in LPP amplitude for the Pain condition [F (1, 29) = 15.17, P = 0.0005; M ± SD: Neutral = 0.88 ± 0.20, Pain = 1.59 ± 0.16]. Post hoc analyses using Šídák’s correction confirmed that the LPP exhibited a greater amplitude in the Pain condition for both the V1 version (M ± SD: V1 Neutral = 1.02 ± 0.99, V1 Pain = 1.71 ± 1.06, P < 0.05) and V2 version (M ± SD: V2 Neutral = 0.73 ± 0.94, V2 Pain = 1.48 ± 1.09, P < 0.05). However, the analysis showed that LPP amplitude was not modulated by version [F (1, 29) = 3.04, P = 0.09; M ± SD: V1 = 1.36 ± 0.48, V2 = 1.11 ± 0.52] (Figure 17D). Discussion Empathy, the capacity to share and understand others’ affective states (Almeida et al., 2024; Cameron et al., 2019; Coll, 2018; Toppi et al., 2022), is a key driver of prosocial behavior and social cohesion (Bernhardt & Singer, 2012; Eisenberg & Miller, 1987; Ibáñez et al., 2023; Mar, 2010). It also serves as a protective factor for mental health, fostering overall well-being (Almeida et al., 2024; Bernhardt & Singer, 2012; Bird & Viding, 2014; Brown & Brown, 2015; Cameron et al., 2019; Eisenberg & Miller, 1987; Ibáñez et al., 2023; Zaki, 2014). Social neuroscience has advanced our understanding of the neural mechanisms underlying empathic responses, offering insights for interventions that enhance empathy. However, most studies rely on stationary neuroimaging techniques in controlled laboratory settings, limiting participation from populations with restricted access to such facilities. This methodological constraint reduces the generalizability of findings, particularly for vulnerable communities. This study aimed to bridge this gap by validating data from a mobile EEG device, which enables research in more diverse and ecologically valid settings. We compared mobile EEG findings to those from conventional stationary EEG, focusing on an ERP research paradigm widely used to study empathy for pain. While mobile EEG showed a relatively lower signal-to-noise ratio, the two systems yielded comparable results in late ERP components of empathy for pain. In particular, the LPP component exhibited similar sensitivity across both devices, though stationary EEG provided overall stronger signal quality. These results highlight the potential of mobile EEG technology to democratize neuroscientific research, expanding participation and enhancing inclusivity in empathy studies. Intriguingly, we found no early ERP component effects for empathy for pain across systems, and tasks, diverging from prior results (Decety et al., 2010; Fan & Han, 2008; Lamm et al., 2010; Singer et al., 2004). This inconsistency aligns with broader concerns about replicability in neuroscience and psychology, where low statistical power, flexible analytical pipelines, and publication biases have been shown to substantially inflate false-positive rates (Aarts et al., 2015; Button et al., 2013). This discrepancy may stem from the considerable variability in early components across studies, suggesting that these responses are more context-dependent than reliable markers of automatic empathic processing (Coll, 2018; Ibáñez et al., 2012; Vecchio & De Pascalis, 2022; Wu & Han, 2021). Methodological challenges may contribute to this variability, particularly insufficient correction for multiple comparisons, which can lead to false positives (Coll, 2018; Vecchio & De Pascalis, 2022; Wu & Han, 2021). Additionally, the absence of clearly defined hypotheses and the flexibility of analytical methods in some studies complicate the interpretation of early component findings (Coll, 2018). These issues mirror the ’replication crisis’ identified across experimental paradigms, where up to 60% of effects fail to replicate under standardized conditions (Open Science Collaboration, 2015). Our results, in which early ERP components failed to differentiate between painful and neutral stimuli, align with research suggesting that these responses primarily reflect initial sensory processing rather than direct engagement in empathic mechanisms (Coll, 2018; Sun et al., 2017; Vecchio & De Pascalis, 2022). Thus, these findings challenge the assumption that early ERP components reliably index the automaticity of empathy, calling for a reevaluation of the temporal and functional frameworks traditionally associated with empathy-related neural processes (Coll, 2018) Both EEG systems yielded consistent results for the later LPP component, reinforcing its robustness as a neural marker of empathic responses (Choi et al., 2014; Choi & Watanuki, 2014; Groen et al., 2012). Our findings align with recent evidence showing that mobile EEG systems produce results comparable to stationary EEG, particularly for later ERP components linked to empathy (Toppi et al., 2022). The LPP reflects evaluative and contextual processing, suggesting that empathy for pain engages deliberate, reflective mechanisms beyond automatic affective sharing (Coll, 2018; Sun et al., 2017; Vecchio & De Pascalis, 2022). Consistent with prior research, the significant modulation of the LPP by vicarious pain highlights its role in the sustained cognitive appraisal of motivationally relevant stimuli (Coll, 2018; Rodriguez et al., 2023; Sun et al., 2017; Vecchio & De Pascalis, 2022). These findings underscore the capability of mobile EEG systems to reliably capture the neural underpinnings of empathy. Conversely, although the P3 component—linked to attentional allocation and the cognitive evaluation of emotionally salient stimuli (Coll, 2018; Keil et al., 2007; Meinhardt & Pekrun, 2003; Sun et al., 2017; Vecchio & De Pascalis, 2022) showed sensitivity to observed pain, these differences were not consistent across versions nor systems. This suggests that the P3 may reflect earlier evaluative processes characterized by inherent variability and context dependency rather than being specifically involved in empathy for pain (Kok, 2001; Mugruza-Vassallo & Potter, 2019). The observed trends in P3 align with theoretical models that link this component to early attention and evaluative stages, which are subject to variability and context dependence (Coll, 2018; Sun et al., 2017; Vecchio & De Pascalis, 2022). Collectively, these findings emphasize the complementary roles of the P3 and LPP components within the broader framework of empathy. The LPP emerges as a particularly robust and reliable marker of empathic sensitivity, whereas the P3 reflects more variable evaluative processes. Distinguishing the functional roles of the P3 and LPP underscores the need for future research on their dynamic interactions and contextual relevance in empathy (Coll, 2018; Vecchio & De Pascalis, 2022). Our findings challenge theoretical models proposing a sequential progression from early affective sharing to later cognitive reevaluation. The absence of consistent modulation in early ERP components challenges their reliability as markers of automatic affective sharing, suggesting this process is more context-dependent and variable than previously assumed (Coll, 2018). Conversely, the late components emerged as more reliable markers of vicarious pain processing, reflecting sustained attention and cognitive appraisal of emotionally salient stimuli (Coll, 2018; Sun et al., 2017; Vecchio & De Pascalis, 2022). These findings highlight the complex interplay between perceptual, cognitive, and affective processes in empathy (Coll, 2018; Vecchio & De Pascalis, 2022). Our findings contribute to the growing evidence supporting mobile EEG as a powerful tool for expanding neuroscientific research. Studies have shown that devices like the Emotiv EPOC and Muse can reliably capture neural signals in real-world settings (Craik et al., 2023; Krigolson et al., 2017; Sawangjai et al., 2019). Mobile EEG has been successfully applied to studies of cognitive load, emotional processing, and social interactions, often yielding results comparable to stationary EEG (Toppi et al., 2022; Lau-Zhu et al., 2019). However, despite its widespread use in attention (Debener et al., 2015; Gramann et al., 2017; Makeig et al., 2009; Wascher et al., 2013; Zink et al., 2016), memory (Debener et al., 2015; Hanslmayr et al., 2012; Makeig et al., 2009; Salvidegoitia et al., 2019; Wascher et al., 2013), and motor research (Gwin et al., 2010; Seeber et al., 2014; Sipp et al., 2013; Wagner et al., 2014; Zink et al., 2016), its application in empathy research remains limited. Our study addresses this gap by demonstrating that mobile EEG can reliably measure neural responses to observed pain, replicating key ERP components associated with empathy. Notably, we found that mobile EEG can detect differences between task versions under the empathy-for-pain framework, similar to stationary EEG. This suggests that mobile EEG systems might be sensitive to specific visual cues that modulate neural responses (Krugliak & Clarke, 2022; Rassam et al., 2024). Consequently, mobile EEG could serve as a cost-efficient method for validating novel tasks within established ERP research paradigms. However, given the increased susceptibility of mobile EEG to signal noise, variations in task design may influence the reliability of empathy-related ERP findings. Future research should refine paradigms and validate novel tasks to enhance signal stability and replicability. Overall, our findings underscore the potential of mobile EEG for investigating empathy in real-world contexts with sufficient sensitivity to stimuli, offering valuable applications for research in underrepresented populations. Future research should prioritize the development of advanced signal-processing algorithms to improve the reliability of mobile EEG data. Compared to stationary setups, mobile EEG systems exhibit lower SNR, which may hinder the detection of subtle neural signals under certain contexts (Debener et al., 2012; Gramann et al., 2013). While mobile EEG enhances ecological validity, it also introduces greater variability due to movement artifacts and environmental noise, complicating data interpretation. Additionally, further research is needed to examine the variability of empathic responses across cultural and socioeconomic contexts (Chiao & Immordino-Yang, 2013), especially among vulnerable populations experiencing chronic stress or social exclusion (Lau-Zhu et al., 2019). Validating mobile EEG technology creates opportunities to explore individual differences in empathic processing among populations with reduced mobility or heightened vulnerability, shedding light on how physical, emotional, and social constraints shape neural responses to empathy. Beyond empathy research, mobile EEG has the potential to transform social neuroscience by enabling real-time investigation of empathic engagement in naturalistic settings (Ibáñez, 2022; Martínez-Pernía et al., 2023; Troncoso et al., 2023; Troncoso et al., 2024). For example, it could facilitate studies on how individuals dynamically respond to social interactions in everyday environments, capturing the interplay between neural activity and real-world social experiences. Examining how contextual factors—such as social cohesion and environmental adversity—modulate empathic engagement could provide valuable insights into both adaptive and maladaptive empathy processes. Additionally, integrating mobile EEG with portable hemodynamic imaging modalities, such as functional near-infrared spectroscopy (fNIRS) (Buccino et al., 2016; Pinti et al., 2018), would enable simultaneous characterization of cortical and subcortical empathy networks. Addressing these methodological and conceptual challenges will be critical for harnessing mobile neuroimaging technologies to advance global mental health initiatives and promote socially equitable neuroscience research. Expanding the scope of mobile EEG applications will not only deepen our understanding of empathy but also inform interventions aimed at enhancing social well-being across diverse populations. Our study underscores the transformative potential of mobile EEG in advancing empathy research, particularly in underserved populations and regions with limited access to traditional neuroimaging facilities. By enabling data collection in diverse and remote settings, mobile EEG helps bridge critical gaps in the field, fostering a more inclusive and comprehensive understanding of the neural mechanisms underlying empathy. Expanding beyond WEIRD populations is essential, as globally representative samples—especially from underrepresented regions such as Latin America—remain largely absent from neuroscience research. Validating mobile EEG technology offers a key opportunity to include populations historically excluded due to geographical, economic, or infrastructural barriers. Addressing these methodological and practical challenges will not only refine the study of empathy but also democratize access to cutting-edge neuroscience. Ultimately, these advancements mark a crucial step toward making empathy research more globally relevant, ensuring its applicability across diverse cultural, socioeconomic, and geographic contexts. Limitations This study presents several limitations that should be considered when interpreting the findings. First, the study employed a between-subjects design, with different participants assigned to each EEG system. Although this design allowed for a direct comparison between systems, it introduces inter-individual variability that may influence both behavioral and neural responses. A within-subject design, where the same participants complete the task under both recording systems, would provide more robust evidence of equivalence. Second, the gender distribution differed substantially between groups (19 females in the BioSemi group vs. 8 in the Emotiv group). Given that sex-related differences have been reported in both empathy-related ERP components and pain perception, this imbalance could partly account for behavioral and neural differences observed across groups. Future studies should ensure better matching or statistically control for gender effects. Third, while both EEG systems captured key late ERP components (particularly the LPP), the differences in signal quality and component sensitivity (notably for earlier components) may reflect both technical disparities and systematic differences in subjective engagement or emotional responsiveness. The fact that participants in the BioSemi group reported higher pain and unpleasantness ratings suggests that their experience of the task may have been more emotionally salient or intense. This difference in subjective engagement may, in turn, have enhanced the amplitude or detectability of empathy-related ERP components in this group. Future studies should examine whether differences in signal fidelity across EEG systems interact with participants’ psychological responses to influence neural data quality. Addressing these issues will be crucial for establishing the validity of mobile EEG as a reliable tool in social and affective neuroscience research across diverse populations and settings. Acknowledgements DH is supported by an ANID/FONDECYT Regular (1231117) research grant. LSCH is supported by an ANID/FONDECYT Inicio (11250611), a COES ANID/FONDAP/1523A0005, and a Universidad Adolfo Ibáñez PAI grant. VS is supported by ANID/FONDECYT Inicio (11221227) and an Universidad Adolfo Ibáñez FEI grant. FRT is supported by Universidad Adolfo Ibáñez PAI grant. References Almeida, R., Prata, C., Pereira, M. R., Barbosa, F., & Ferreira-Santos, F. (2024). Neuronal Correlates of Empathy: A Systematic Review of Event-Related Potentials Studies in Perceptual Tasks. Brain Sciences, 14 (5), 504. https://doi.org/10.3390/brainsci14050504 Aarts, A. A., Anderson, J. E., Anderson, C. J., Attridge, P. R., Attwood, A., Axt, J., Babel, M., Bahník, Š., Baranski, E., Barnett-Cowan, M., Bartmess, E., Beer, J., Bell, R., Bentley, H., Beyan, L., Binion, G., Borsboom, D., Bosch, A., Bosco, F. A., . . . Zuni, K. (2015). Estimating the reproducibility of psychological science. Science, 349 (6251). https://doi.org/10.1126/science.aac4716 Batson, C. D. (1991). The altruism question: Toward a social-psychological answer. Lawrence Erlbaum Associates, Inc . Bernhardt, B. C., & Singer, T. (2012). The Neural Basis of Empathy. Annual Review Of Neuroscience, 35 (1), 1-23. https://doi.org/10.1146/annurev-neuro-062111-150536 Bird, G., & Viding, E. (2014). The self to other model of empathy: Providing a new framework for understanding empathy impairments in psychopathy, autism, and alexithymia. Neuroscience & Biobehavioral Reviews, 47 , 520-532. https://doi.org/10.1016/j.neubiorev.2014.09.021 Brown, S. L., & Brown, R. M. (2015). Connecting prosocial behavior to improved physical health: Contributions from the neurobiology of parenting. Neuroscience & Biobehavioral Reviews , 55, 1-17. https://doi.org/10.1016/j.neubiorev.2015.04.004 Buccino, A. P., Keles, H. O., & Omurtag, A. (2016). Hybrid EEG-fNIRS Asynchronous Brain-Computer Interface for Multiple Motor Tasks. PLoS ONE, 11 (1), e0146610. https://doi.org/10.1371/journal.pone.0146610 Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., & Munafò, M. R. (2013). Power failure: why small sample size undermines the reliability of neuroscience. Nature Reviews. Neuroscience, 14 (5), 365-376. https://doi.org/10.1038/nrn3475 Cameron, C. D., Hutcherson, C. A., Ferguson, A. M., Scheffer, J. A., Hadjiandreou, E., & Inzlicht, M. (2019). Empathy is hard work: People choose to avoid empathy because of its cognitive costs. Journal of Experimental Psychology General, 148 (6), 962–976. https://doi.org/10.1037/xge0000595 Cao, Y., Contreras-Huerta, L. S., McFadyen, J., & Cunnington, R. (2015). Racial bias in neural response to others’ pain is reduced with other-race contact. Cortex, 70 , 68-78. https://doi.org/10.1016/j.cortex.2015.02.010 Cheng, Y., Hung, A., & Decety, J. (2012). Dissociation between affective sharing and emotion understanding in juvenile psychopaths. Development And Psychopathology, 24 (2), 623-636. https://doi.org/10.1017/s095457941200020x Chi, Y. M., Wang, Y., Wang, Y., Jung, T., Kerth, T., & Cao, Y. (2013). A Practical Mobile Dry EEG System for Human Computer Interfaces. En Lecture notes in computer science (pp. 649-655). https://doi.org/10.1007/978-3-642-39454-6_69 Chiao, J. Y., & Immordino-Yang, M. H. (2013). Modularity and the Cultural Mind. Perspectives On Psychological Science, 8 (1), 56-61. https://doi.org/10.1177/1745691612469032 Choi, D., & Watanuki, S. (2014). Effect of empathy trait on attention to faces: an event-related potential (ERP) study. Journal Of Physiological Anthropology, 33 (1). https://doi.org/10.1186/1880-6805-33-4 Choi, D., Nishimura, T., Motoi, M., Egashira, Y., Matsumoto, R., & Watanuki, S. (2014). Effect of empathy trait on attention to various facial expressions: evidence from N170 and late positive potential (LPP). Journal Of Physiological Anthropology, 33 (1). https://doi.org/10.1186/1880-6805-33-18 Coll, M. (2018). Meta-analysis of ERP investigations of pain empathy underlines methodological issues in ERP research. Social Cognitive and Affective Neuroscience, 13 (10), 1003–1017. https://doi.org/10.1093/scan/nsy072 Contreras-Huerta, L. S. (2023). A cost-benefit framework for prosocial motivation—Advantages and challenges. Frontiers In Psychiatry, 14 . https://doi.org/10.3389/fpsyt.2023.1170150 Contreras-Huerta, L. S., Baker, K. S., Reynolds, K. J., Batalha, L., & Cunnington, R. (2013). Racial Bias in Neural Empathic Responses to Pain. PLoS ONE, 8 (12), e84001. https://doi.org/10.1371/journal.pone.0084001 Contreras-Huerta, L. S., Coll, M., Bird, G., Yu, H., Prosser, A., Lockwood, P. L., Murphy, J., Crockett, M., & Apps, M. A. (2023). Neural representations of vicarious rewards are linked to interoception and prosocial behaviour. NeuroImage , 269 , 119881. https://doi.org/10.1016/j.neuroimage.2023.119881 Contreras-Huerta, L. S., Hielscher, E., Sherwell, C. S., Rens, N., & Cunnington, R. (2014). Intergroup relationships do not reduce racial bias in empathic neural responses to pain. Neuropsychologia, 64 , 263-270. https://doi.org/10.1016/j.neuropsychologia.2014.09.045 Contreras-Huerta, L. S., Pisauro, M. A., & Apps, M. A. (2020). Effort shapes social cognition and behaviour: A neuro-cognitive framework. Neuroscience & Biobehavioral Reviews, 118 , 426-439. https://doi.org/10.1016/j.neubiorev.2020.08.003 Corbera, S., Ikezawa, S., Bell, M., & Wexler, B. (2014). Physiological evidence of a deficit to enhance the empathic response in schizophrenia. European Psychiatry, 29 (8), 463-472. https://doi.org/10.1016/j.eurpsy.2014.01.005 Craig, A. D. (2008). How do you feel — now? The anterior insula and human awareness. Nature Reviews. Neuroscience, 10 (1), 59-70. https://doi.org/10.1038/nrn2555 Craik, A., González-España, J. J., Alamir, A., Edquilang, D., Wong, S., Rodríguez, L. S., Feng, J., Francisco, G. E., & Contreras-Vidal, J. L. (2023). Design and Validation of a Low-Cost Mobile EEG-Based Brain–Computer Interface. Sensors, 23 (13), 5930. https://doi.org/10.3390/s23135930 De Waal, F. B. (2007). Putting the Altruism Back into Altruism: The Evolution of Empathy. Annual Review Of Psychology, 59 (1), 279-300. https://doi.org/10.1146/annurev.psych.59.103006.093625 Debener, S., Emkes, R., De Vos, M., & Bleichner, M. (2015). Unobtrusive ambulatory EEG using a smartphone and flexible printed electrodes around the ear. Scientific Reports, 5 (1). https://doi.org/10.1038/srep16743 Debener, S., Minow, F., Emkes, R., Gandras, K., & De Vos, M. (2012). How about taking a low‐cost, small, and wireless EEG for a walk? Psychophysiology, 49 (11), 1617-1621. https://doi.org/10.1111/j.1469-8986.2012.01471.x Decety, J., & Jackson, P. L. (2004). The Functional Architecture of Human Empathy. Behavioral And Cognitive Neuroscience Reviews, 3 (2), 71-100. https://doi.org/10.1177/1534582304267187 Decety, J., & Michalska, K. J. (2009). Neurodevelopmental changes in the circuits underlying empathy and sympathy from childhood to adulthood. Developmental Science, 1 3(6), 886-899. https://doi.org/10.1111/j.1467-7687.2009.00940.x Decety, J., & Moriguchi, Y. (2007). The empathic brain and its dysfunction in psychiatric populations: implications for intervention across different clinical conditions. BioPsychoSocial Medicine, 1 (1), 22. https://doi.org/10.1186/1751-0759-1-22 Decety, J., Bartal, I. B., Uzefovsky, F., & Knafo-Noam, A. (2015). Empathy as a driver of prosocial behaviour: highly conserved neurobehavioural mechanisms across species. Philosophical Transactions Of The Royal Society B Biological Sciences, 371 (1686), 20150077. https://doi.org/10.1098/rstb.2015.0077 Decety, J., Yang, C., & Cheng, Y. (2010). Physicians down-regulate their pain empathy response: An event-related brain potential study. NeuroImage, 50 (4), 1676-1682. https://doi.org/10.1016/j.neuroimage.2010.01.025 Dong, L.; Li, F.; Liu, Q.; Wen, X.; Lai, Y.; Xu, P.; Yao, D. MATLAB Toolboxes for Reference Electrode Standardization Technique (REST) of Scalp EEG. Front. Neurosci. 2017, 11, 601 Eisenberg, N., & Miller, P. A. (1987). The relation of empathy to prosocial and related behaviors. Psychological Bulletin, 101 (1), 91-119. https://doi.org/10.1037/0033-2909.101.1.91 Eklund, J. H., & Meranius, M. S. (2020). Toward a consensus on the nature of empathy: A review of reviews. Patient Education And Counseling, 104 (2), 300-307. https://doi.org/10.1016/j.pec.2020.08.022 Fabi, S., & Leuthold, H. (2016). Empathy for pain influences perceptual and motor processing: Evidence from response force, ERPs, and EEG oscillations. Social Neuroscience , 1-16. https://doi.org/10.1080/17470919.2016.1238009 Fan, Y., & Han, S. (2007). Temporal dynamic of neural mechanisms involved in empathy for pain: An event-related brain potential study. Neuropsychologia, 46 (1), 160-173. https://doi.org/10.1016/j.neuropsychologia.2007.07.023 Fan, Y., Duncan, N. W., De Greck, M., & Northoff, G. (2010). Is there a core neural network in empathy? An fMRI based quantitative meta-analysis. Neuroscience & Biobehavioral Reviews, 35 (3), 903-911. https://doi.org/10.1016/j.neubiorev.2010.10.009 Fuentes, M. A., Lavín, C., Contreras-Huerta, L. S., Miguel, H., & Jubal, E. R. (2014). Stochastic model predicts evolving preferences in the Iowa gambling task. Frontiers In Computational Neuroscience, 5 . https://doi.org/10.3389/fncom.2014.00167 Gamble, R. S., Henry, J. D., & Vanman, E. J. (2023). Empathy moderates the relationship between cognitive load and prosocial behaviour. Scientific Reports, 13 (1). https://doi.org/10.1038/s41598-023-28098-x Gramann, K., Fairclough, S. H., Zander, T. O., & Ayaz, H. (2017). Editorial: Trends in Neuroergonomics. Frontiers In Human Neuroscience, 11 . https://doi.org/10.3389/fnhum.2017.00165 Gramann, K., Ferris, D. P., Gwin, J., & Makeig, S. (2013). Imaging natural cognition in action. International Journal Of Psychophysiology, 91 (1), 22-29. https://doi.org/10.1016/j.ijpsycho.2013.09.003 Groen, Y., Wijers, A., Tucha, O., & Althaus, M. (2012). Are there sex differences in ERPs related to processing empathy-evoking pictures? Neuropsychologia, 51 (1), 142-155. https://doi.org/10.1016/j.neuropsychologia.2012.11.012 Gwin, J. T., Gramann, K., Makeig, S., & Ferris, D. P. (2010). Electrocortical activity is coupled to gait cycle phase during treadmill walking. NeuroImage, 54 (2), 1289-1296. https://doi.org/10.1016/j.neuroimage.2010.08.066 Hanslmayr, S., Staudigl, T., & Fellner, M. (2012). Oscillatory power decreases and long-term memory: the information via desynchronization hypothesis. Frontiers In Human Neuroscience, 6 . https://doi.org/10.3389/fnhum.2012.00074 Hein, G., Engelmann, J. B., Vollberg, M. C., & Tobler, P. N. (2015). How learning shapes the empathic brain. Proceedings Of The National Academy Of Sciences, 113 (1), 80-85. https://doi.org/10.1073/pnas.1514539112 Henrich, J., Heine, S. J., & Norenzayan, A. (2010). The weirdest people in the world? Behavioral and Brain Sciences, 33 (2–3), 61–83. https://doi.org/10.1017/s0140525x0999152x Ibanez, A. (2022). The mind’s golden cage and cognition in the wild. Trends In Cognitive Sciences, 26 (12), 1031-1034. https://doi.org/10.1016/j.tics.2022.07.008 Ibanez, A., Matallana, D., & Miller, B. (2023). Can prosocial values improve brain health? Frontiers In Neurology, 14 . https://doi.org/10.3389/fneur.2023.1202173 Ibanez, A., Melloni, M., Huepe, D., Helgiu, E., Rivera-Rei, A., Canales-Johnson, A., Baker, P., & Moya, A. (2012). What event-related potentials (ERPs) bring to social neuroscience? Social Neuroscience, 7 (6), 632-649. https://doi.org/10.1080/17470919.2012.691078 Jackson, P. L., Meltzoff, A. N., & Decety, J. (2004). How do we perceive the pain of others? A window into the neural processes involved in empathy. NeuroImage, 24 (3), 771-779. https://doi.org/10.1016/j.neuroimage.2004.09.006 Kataoka, H., Takatani, T., & Sugie, K. (2022). Two-Channel Portable Biopotential Recording System Can Detect REM Sleep Behavioral Disorder: Validation Study with a Comparison of Polysomnography. Parkinson S Disease, 2022 , 1-5. https://doi.org/10.1155/2022/1888682 Keil, A., Bradley, M. M., Junghofer, M., Russmann, T., Lowenthal, W., & Lang, P. J. (2007). Cross-modal attention capture by affective stimuli: Evidence from event-related potentials. Cognitive Affective & Behavioral Neuroscience, 7 (1), 18-24. https://doi.org/10.3758/cabn.7.1.18 Klapprott, M., & Debener, S. (2024). Mobile EEG for the study of cognitive-motor interference during swimming? Frontiers In Human Neuroscience, 18 . https://doi.org/10.3389/fnhum.2024.1466853 Kok, A. (2001). On the utility of P3 amplitude as a measure of processing capacity. Psychophysiology, 38 (3), 557-577. https://doi.org/10.1017/s0048577201990559 Krigolson, O. E., Williams, C. C., Norton, A., Hassall, C. D., & Colino, F. L. (2017). Choosing MUSE: Validation of a Low-Cost, Portable EEG System for ERP Research. Frontiers In Neuroscience, 11 . https://doi.org/10.3389/fnins.2017.00109 Krugliak, A., & Clarke, A. (2022). Towards real-world neuroscience using mobile EEG and augmented reality. Scientific Reports, 12 (1). https://doi.org/10.1038/s41598-022-06296-3 Lamm, C., Decety, J., & Singer, T. (2010). Meta-analytic evidence for common and distinct neural networks associated with directly experienced pain and empathy for pain. NeuroImage, 54 (3), 2492-2502. https://doi.org/10.1016/j.neuroimage.2010.10.014 Larson, M. J., & Carbine, K. A. (2016). Sample size calculations in human electrophysiology (EEG and ERP) studies: A systematic review and recommendations for increased rigor. International Journal Of Psychophysiology, 111 , 33-41. https://doi.org/10.1016/j.ijpsycho.2016.06.015 Lau-Zhu, A., Lau, M. P., & McLoughlin, G. (2019). Mobile EEG in research on neurodevelopmental disorders: Opportunities and challenges. Developmental Cognitive Neuroscience, 36 , 100635. https://doi.org/10.1016/j.dcn.2019.100635 Lavin, C., Martín, R. S., Bravo, D., Contreras, L., & Isla, P. (2011). Potenciales cerebrales relacionados a feedback en el estudio del aprendizaje y la toma de decisiones económicas. Revista Latinoamericana de Psicología, 43 (3), 455-471. https://doi.org/10.14349/rlp.v43i3.220 Lopez-Calderon, J., & Luck, S. J. (2014). ERPLAB: an open-source toolbox for the analysis of event-related potentials. Frontiers In Human Neuroscience, 8 . https://doi.org/10.3389/fnhum.2014.00213 Luo, S., & Han, S. (2014). The association between an oxytocin receptor gene polymorphism and cultural orientations. Culture And Brain, 2 (1), 89-107. https://doi.org/10.1007/s40167-014-0017-5 Makeig, S., Gramann, K., Jung, T., Sejnowski, T. J., & Poizner, H. (2009). Linking brain, mind and behavior. International Journal Of Psychophysiology, 73 (2), 95-100. https://doi.org/10.1016/j.ijpsycho.2008.11.008 Mar, R. A. (2010). The Neural Bases of Social Cognition and Story Comprehension. Annual Review Of Psychology, 62 (1), 103-134. https://doi.org/10.1146/annurev-psych-120709-145406 Martínez-Pernía, D., Cea, I., Troncoso, A., Blanco, K., Vergara, J. C., Baquedano, C., Araya-Veliz, C., Useros-Olmo, A., Huepe, D., Carrera, V., Silva, V. M., & Vergara, M. (2023). “I am feeling tension in my whole body”: An experimental phenomenological study of empathy for pain. Frontiers In Psychology, 13 . https://doi.org/10.3389/fpsyg.2022.999227 Meinhardt, J., & Pekrun, R. (2003). Attentional resource allocation to emotional events: An ERP study. Cognition & Emotion, 17 (3), 477-500. https://doi.org/10.1080/02699930244000039 Meng, J., Hu, L., Shen, L., Yang, Z., Chen, H., Huang, X., & Jackson, T. (2012). Emotional primes modulate the responses to others’ pain: an ERP study. Experimental Brain Research, 220 (3-4), 277-286. https://doi.org/10.1007/s00221-012-3136-2 Meng, J., Jackson, T., Chen, H., Hu, L., Yang, Z., Su, Y., & Huang, X. (2013). Pain perception in the self and observation of others: An ERP investigation. NeuroImage, 72 , 164-173. https://doi.org/10.1016/j.neuroimage.2013.01.024 Meng, J., Li, Y., Luo, L., Li, L., Jiang, J., Liu, X., & Shen, L. (2023). The Empathy for Pain Stimuli System (EPSS): Development and preliminary validation. Behavior Research Methods, 56, 784–803. https://doi.org/10.3758/s13428-023-02087-4 Meng, J., Shen, L., Li, Z., & Peng, W. (2019). Top-down Effects on Empathy for Pain in Adults with Autistic Traits. Scientific Reports, 9 (1). https://doi.org/10.1038/s41598-019-44400-2 Mugruza-Vassallo, C., & Potter, D. (2019). Context Dependence Signature, Stimulus Properties and Stimulus Probability as Predictors of ERP Amplitude Variability. Frontiers In Human Neuroscience, 13 . https://doi.org/10.3389/fnhum.2019.00039 Nielsen, M., Haun, D., Kärtner, J., & Legare, C. H. (2017). The persistent sampling bias in developmental psychology: A call to action. Journal Of Experimental Child Psychology, 162 , 31-38. https://doi.org/10.1016/j.jecp.2017.04.017 Park, J. L., & Donaldson, D. I. (2019). Detecting the neural correlates of episodic memory with mobile EEG: Recollecting objects in the real world. NeuroImage, 193 , 1-9. https://doi.org/10.1016/j.neuroimage.2019.03.013 Pinti, P., Tachtsidis, I., Hamilton, A., Hirsch, J., Aichelburg, C., Gilbert, S., & Burgess, P. W. (2018). The present and future use of functional near‐infrared spectroscopy (fNIRS) for cognitive neuroscience. Annals Of The New York Academy Of Sciences, 1464 (1), 5-29. https://doi.org/10.1111/nyas.13948 Rad, M. S., Martingano, A. J., & Ginges, J. (2018). Toward a psychology of Homo sapiens : Making psychological science more representative of the human population. Proceedings Of The National Academy Of Sciences, 115 (45), 11401-11405. https://doi.org/10.1073/pnas.1721165115 Rassam, R., Chen, Q., & Gai, Y. (2024). Competing Visual Cues Revealed by Electroencephalography: Sensitivity to Motion Speed and Direction. Brain Sciences, 14 (2), 160. https://doi.org/10.3390/brainsci14020160 Rodriguez, K., Ibarra, I. P., Musick, A., Hoerr, J., Napoli, D., & Berry, D. R. (2023). Event-related correlates of compassion for social pain. Social Neuroscience, 18 (2), 91-102. https://doi.org/10.1080/17470919.2023.2208878 Salvidegoitia, M. P., Jacobsen, N., Bauer, A. R., Griffiths, B., Hanslmayr, S., & Debener, S. (2019). Out and about: Subsequent memory effect captured in a natural outdoor environment with smartphone EEG. Psychophysiology, 56 (5). https://doi.org/10.1111/psyp.13331 Sawangjai, P., Hompoonsup, S., Leelaarporn, P., Kongwudhikunakorn, S., & Wilaiprasitporn, T. (2019). Consumer Grade EEG Measuring Sensors as Research Tools: A Review. IEEE Sensors Journal, 20 (8), 3996-4024. https://doi.org/10.1109/jsen.2019.2962874 Seeber, M., Scherer, R., Wagner, J., Solis-Escalante, T., & Müller-Putz, G. R. (2014). EEG beta suppression and low gamma modulation are different elements of human upright walking. Frontiers In Human Neuroscience, 8 . https://doi.org/10.3389/fnhum.2014.00485 Sessa, P., Meconi, F., & Han, S. (2014). Double dissociation of neural responses supporting perceptual and cognitive components of social cognition: Evidence from processing of others’ pain. Scientific Reports, 4 (1). https://doi.org/10.1038/srep07424 Shamay-Tsoory, S. G. (2010). The Neural Bases for Empathy. The Neuroscientist, 17 (1), 18-24. https://doi.org/10.1177/1073858410379268 Shamay-Tsoory, S. G., Aharon-Peretz, J., & Perry, D. (2008). Two systems for empathy: a double dissociation between emotional and cognitive empathy in inferior frontal gyrus versus ventromedial prefrontal lesions. Brain, 132 (3), 617-627. https://doi.org/10.1093/brain/awn279 Singer, T., & Lamm, C. (2009). The Social Neuroscience of Empathy. Annals Of The New York Academy Of Sciences, 1156 (1), 81-96. https://doi.org/10.1111/j.1749-6632.2009.04418.x Singer, T., Seymour, B., O’Doherty, J., Kaube, H., Dolan, R. J., & Frith, C. D. (2004). Empathy for Pain Involves the Affective but not Sensory Components of Pain. Science, 303 (5661), 1157-1162. https://doi.org/10.1126/science.1093535 Sipp, A. R., Gwin, J. T., Makeig, S., & Ferris, D. P. (2013). Loss of balance during balance beam walking elicits a multifocal theta band electrocortical response. Journal Of Neurophysiology, 110 (9), 2050-2060. https://doi.org/10.1152/jn.00744.2012 Stopczynski, A., Stahlhut, C., Larsen, J. E., Petersen, M. K., & Hansen, L. K. (2014). The smartphone brain Scanner: a portable Real-Time neuroimaging system. (2), e86733. https://doi.org/10.1371/journal.pone.0086733 Sun, Y., Lin, X., Ye, W., Wang, N., Wang, J., & Luo, F. (2017). A Screening Mechanism Differentiating True from False Pain during Empathy. Scientific Reports, 7 (1). https://doi.org/10.1038/s41598-017-11963-x Svensson, T., Chung, U., Tokuno, S., Nakamura, M., & Svensson, A. K. (2019). A validation study of a consumer wearable sleep tracker compared to a portable EEG system in naturalistic conditions. Journal Of Psychosomatic Research, 126 , 109822. https://doi.org/10.1016/j.jpsychores.2019.109822 Toppi, J., Siniatchkin, M., Vogel, P., Freitag, C. M., Astolfi, L., & Ciaramidaro, A. (2022). A novel approach to measure brain-to-brain spatial and temporal alignment during positive empathy. Scientific Reports, 12 (1). https://doi.org/10.1038/s41598-022-18911-4 Troncoso, A., Blanco, K., Rivera-Rei, Á., & Martínez-Pernía, D. (2024). Empathy bodyssence: temporal dynamics of sensorimotor and physiological responses and the subjective experience in synchrony with the other’s suffering. Frontiers In Psychology, 15 . https://doi.org/10.3389/fpsyg.2024.1362064 Troncoso, A., Soto, V., Gomila, A., & Martínez-Pernía, D. (2023). Moving beyond the lab: investigating empathy through the Empirical 5E approach. Frontiers In Psychology, 14 . https://doi.org/10.3389/fpsyg.2023.1119469 Twenge, J. M., Baumeister, R. F., DeWall, C. N., Ciarocco, N. J., & Bartels, J. M. (2007). Social exclusion decreases prosocial behavior. Journal Of Personality And Social Psychology, 92 (1), 56-66. https://doi.org/10.1037/0022-3514.92.1.56 Vaes, J., Meconi, F., Sessa, P., & Olechowski, M. (2016). Minimal humanity cues induce neural empathic reactions towards non-human entities. Neuropsychologia, 89 , 132-140. https://doi.org/10.1016/j.neuropsychologia.2016.06.004 Valentin, O., Ducharme, M., Cretot-Richert, G., Monsarrat-Chanon, H., Viallet, G., Delnavaz, A., & Voix, J. (2018). Validation and Benchmarking of a Wearable EEG Acquisition Platform for Real-World Applications. I EEE Transactions On Biomedical Circuits And Systems, 1 . https://doi.org/10.1109/tbcas.2018.2876240 Vecchio, A., & De Pascalis, V. (2022). ERP indicators of situational empathy pain. Behavioural Brain Research, 439 , 114-224. https://doi.org/10.1016/j.bbr.2022.114224 Wagner, J., Solis-Escalante, T., Scherer, R., Neuper, C., & Müller-Putz, G. (2014). It’s how you get there: walking down a virtual alley activates premotor and parietal areas. Frontiers In Human Neuroscience, 8 . https://doi.org/10.3389/fnhum.2014.00093 Wascher, E., Heppner, H., & Hoffmann, S. (2013). Towards the measurement of event-related EEG activity in real-life working environments. International Journal Of Psychophysiology, 9 1(1), 3-9. https://doi.org/10.1016/j.ijpsycho.2013.10.006 Woodman, G. F. (2010). A brief introduction to the use of event-related potentials in studies of perception and attention. Attention Perception & Psychophysics, 72 (8), 2031-2046. https://doi.org/10.3758/app.72.8.2031 Wu, T., & Han, S. (2021). Neural mechanisms of modulations of empathy and altruism by beliefs of others’ pain. eLife, 10 . https://doi.org/10.7554/elife.66043 Xiang, Y., Wang, Y., Gao, S., Zhang, X., & Cui, R. (2018). Neural Mechanisms With Respect to Different Paradigms and Relevant Regulatory Factors in Empathy for Pain. Frontiers In Neuroscience, 12 . https://doi.org/10.3389/fnins.2018.00507 Zaki, J. (2014). Empathy: A motivated account. Psychological Bulletin, 140 (6), 1608-1647. https://doi.org/10.1037/a0037679 Zaki, J., & Ochsner, K. N. (2012). The neuroscience of empathy: progress, pitfalls and promise. Nature Neuroscience, 15 (5), 675-680. https://doi.org/10.1038/nn.3085 Zink, R., Hunyadi, B., Van Huffel, S., & De Vos, M. (2016). Mobile EEG on the bike: disentangling attentional and physical contributions to auditory attention tasks. Journal Of Neural Engineering, 13 (4), 046017. https://doi.org/10.1088/1741-2560/13/4/046017 Information & Authors Information Version history V1 Version 1 27 August 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Authors Affiliations Felipe Rojas-Thomas Universidad Adolfo Ibanez Centro de Neurociencia Social y Cognitiva View all articles by this author Fiorella Macchiavello 0009-0008-9746-5379 Universidad Adolfo Ibanez Centro de Neurociencia Social y Cognitiva View all articles by this author Vicente Soto 0000-0003-3494-4213 Universidad Adolfo Ibanez Centro de Neurociencia Social y Cognitiva View all articles by this author Álvaro Rivera-Rei 0000-0002-3674-7291 Universidad Adolfo Ibanez Centro de Neurociencia Social y Cognitiva View all articles by this author Daniel O’Byrne Universidad Adolfo Ibanez Centro de Neurociencia Social y Cognitiva View all articles by this author José Bórquez Universidad Adolfo Ibanez Centro de Neurociencia Social y Cognitiva View all articles by this author Consuelo Ruiz Universidad Adolfo Ibanez Centro de Neurociencia Social y Cognitiva View all articles by this author David Huepe 0000-0001-8351-5314 [email protected] Universidad Adolfo Ibanez Centro de Neurociencia Social y Cognitiva View all articles by this author Sebastian Contreras-Huerta Universidad Adolfo Ibanez Centro de Neurociencia Social y Cognitiva View all articles by this author Metrics & Citations Metrics Article Usage 334 views 146 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Felipe Rojas-Thomas, Fiorella Macchiavello, Vicente Soto, et al. 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