The effect of heart rate variability biofeedback on cortical arousal and its subsequent influence on vigilance: a randomized controlled study

The effect of heart rate variability biofeedback on cortical arousal and its subsequent influence on vigilance: a randomized controlled study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The effect of heart rate variability biofeedback on cortical arousal and its subsequent influence on vigilance: a randomized controlled study Ryuji Saito, Kazuki Yoshida, Daisuke Sawamura This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8939605/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Heart rate variability biofeedback (HRVBF) has been investigated as a breathing-based intervention to enhance cognitive function, yet its efficacy varies across studies. Understanding the mechanisms underlying this variability is essential for optimizing HRVBF protocols. This study examined the effects of HRVBF and the relationship between cortical arousal and vigilance using multimodal assessments. Fifty-one healthy young adults were randomly assigned to HRVBF (n = 25) or an active control group (n = 26). Participants completed 15-min HRVBF or control breathing with pre- and post-assessments of physiological (heart rate variability, electroencephalography), behavioral (sustained attention to response task), and subjective arousal measures. The results indicated that the HRVBF group had no significant effects on any outcomes compared to the control group. Both groups showed increased root mean square of successive differences, N2 event-related potential Go amplitudes, commission error rates, and subjective arousal, along with decreased N2 No-Go amplitudes and reaction times. Polynomial regression revealed a significant quadratic relationship between changes in alpha band power and commission error rates in the HRVBF group (R² = 0.276, p = 0.029), whereas no significant relationship was found in controls. Although HRVBF did not outperform control breathing, the quadratic association between cortical arousal and vigilance suggests that HRVBF may regulate arousal toward optimal levels. This inverted U-shaped pattern may help explain variability in HRVBF efficacy and highlights the importance of considering non-linear arousal–vigilance dynamics in future research Trial Registration: UMIN-CTR UMIN000058098 (June 5, 2025). https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000066395 heart rate variability biofeedback EEG cortical arousal vigilance polynomial regression Figures Figure 1 Figure 2 Figure 3 Figure 3 Introduction Heart rate variability biofeedback (HRVBF) is a breathing-based intervention that integrates slow-paced respiration with real-time heart rate variability (HRV) waveform feedback to enhance autonomic nervous system regulation (Lehrer et al., 2013 ). By guiding respiration at an individual’s resonant frequency, typically between 4.5 and 6.5 breaths per minute (approximately 0.1 Hz), HRVBF synchronizes respiratory sinus arrhythmia, baroreflex activity, and blood pressure oscillations, thereby augmenting vagally mediated HRV (vmHRV) (Giorgi & Tedeschi, 2025 ; Sevoz-Couche & Laborde, 2022 ). Within the neurovisceral integration framework, vmHRV is considered an index of the functional integrity of prefrontal–subcortical circuits that support executive functions, including attention, inhibitory control, and cognitive flexibility (Smith et al., 2017 ; Thayer & Lane, 2009 ). Accordingly, HRVBF has been increasingly examined as a potential strategy for improving cognitive function. Recent systematic reviews and meta-analyses have reported beneficial effects of HRVBF on cognitive outcomes (Tinello et al., 2022 ) and psychiatric symptoms (Pizzoli et al., 2021 ) across diverse populations. However, despite accumulating evidence supporting HRVBF efficacy, reported effect sizes are generally small to moderate, with substantial variability in outcomes observed in recent meta-analytic findings (Lehrer et al., 2020 ). Clarifying the mechanisms contributing to this variability is essential for refining HRVBF protocols and identifying individuals most likely to experience cognitive benefits from training. One potential mechanism that may account for the variable efficacy of HRVBF involves its modulation of cortical arousal and the subsequent influence on cognitive function. Arousal, defined as the global activation state of the central and autonomic nervous systems, plays a fundamental role in regulating vigilance, attention, and higher-order cognitive processes (Pribram & McGuinness, 1975 ). The relationship between arousal and performance has been classically described by the Yerkes-Dodson law, which posits an inverted U-shaped function in which optimal performance occurs at moderate arousal levels, with decrements observed at both low and high extremes (Yerkes & Dodson, 1908 ). In electroencephalography (EEG), frequency components, particularly alpha band power in occipital regions, have been considered reliable indicators of cortical arousal (Kim et al., 2021 ; Schubring & Schupp, 2021 ), with decreased alpha band power typically associated with heightened arousal levels (Barry et al., 2007 ; Duda et al., 2025 ; Wolska et al., 2019 ). Previous studies have demonstrated positive correlations between vmHRV and alpha band power (Attar et al., 2021 ; Kawashima et al., 2024 ; Melo et al., 2022 ), suggesting that higher vmHRV is associated with relaxed alertness, a state characterized by sustained attentional engagement accompanied by low physiological stress. Given that HRVBF may modulate arousal toward optimal levels alongside increases in vmHRV, HRVBF-induced changes in arousal may contribute to improvements in cognitive function. Vigilance, defined as the capacity to sustain attention over extended periods, represents a critical cognitive function that depends on optimal arousal regulation (Oken et al., 2006 ). The sustained attention to response task (SART) has been widely used to assess vigilance, requiring participants to respond to frequent Go stimuli while withholding responses to rare No-Go stimuli (Robertson et al., 1997 ). Performance on the SART is sensitive to fluctuations in arousal and attention, with commission errors (inappropriate responses to No-Go stimuli) serving as a primary behavioral indicator of diminished inhibitory control (Johnson et al., 2020 ; Smilek et al., 2010 ). In the present study, vigilance is operationalized as behavioral performance on the SART. Event-related potentials (ERPs), particularly the N2 and P3 components, serve as neurophysiological indices of inhibitory control and attentional resource allocation during the SART (Ahumada-Méndez et al., 2022 ). Larger N2 and P3 amplitudes are interpreted as reflecting greater top-down cognitive control associated with these functions (Cheval et al., 2021 ; Kotani et al., 2025 ). If HRVBF modulates arousal to an appropriate level through autonomic–cortical coupling, HRVBF training is expected to enhance vigilance and increase the corresponding ERP amplitudes. Despite the theoretical rationale linking HRVBF, arousal, and vigilance, empirical investigations of this relationship remain limited. Previous HRVBF studies have primarily focused on autonomic outcomes or clinical symptoms (Lehrer et al., 2020 ; Pizzoli et al., 2021 ; Spada et al., 2022 ), with limited attention to the cognitive and neurophysiological mechanisms that may underlie the training effects (Tinello et al., 2022 ). Furthermore, the assumption of linear relationships between physiological and behavioral variables may obscure more complex, non-linear dynamics that characterize physiological–cognitive interactions (Johannessen et al., 2020 ; Young & Benton, 2015 ). As studies examining the Yerkes-Dodson law have suggested, the relationship between arousal and cognitive function is inherently non-linear, following an inverted U-shaped pattern (Diamond, 2005 ; Nieuwenhuis, 2024 ). This implies that identical changes in arousal may produce divergent effects on cognitive function, depending on an individual’s baseline arousal level, with enhancement for those at suboptimal levels but minimal for those already near the optimal range. Linear models, which assume uniform directional effects, are inherently unable to capture such state-dependent dynamics. To provide a more comprehensive understanding, it is necessary to examine not only the effects of HRVBF on cortical arousal and vigilance but also the association between them by integrating multimodal measurements, including HRV, EEG, and behavioral indicators, with polynomial regression analysis. The present study aimed to examine the effects of HRVBF on cortical arousal and vigilance, as well as the association between these variables as a potential mechanism underlying variable effects. To address this objective, we employed a multimodal approach integrating HRV, EEG, and the SART. We tested two primary hypotheses. First, we hypothesized that HRVBF would increase vmHRV and modulate cortical arousal toward an optimal level, as indexed by changes in alpha band power, and that these changes would be accompanied by enhanced vigilance, reflected by reduced commission errors and larger N2 and P3 amplitudes. Second, we hypothesized that the association between HRVBF-induced changes in cortical arousal and vigilance would be better explained by a quadratic model than by linear or cubic models in polynomial regression. Methods Participants Based on a previous study reporting a positive correlation between vmHRV and EEG alpha band power (Attar et al., 2021 ), we performed a sample size estimation using G*Power 3.1 (Faul et al., 2009 ) with a two-tailed test, an effect size (r = 0.53), a significance level (α = 0.05), and statistical power (1 − β = 0.80). The required sample size was calculated as 23 participants per group, yielding a total of 46 participants across two groups. To account for a potential 20% dropout rate and possible outliers, the final target sample size was set at 56 participants. Participants were stratified by sex and randomly assigned to the HRVBF or control groups. Right-handed healthy undergraduate and graduate students were recruited. Given the established effects of age on HRV (Tiwari et al., 2021 ), participants aged 18–30 years were enrolled. The exclusion criteria were as follows: (1) a history of psychiatric disorders or neurological, cardiovascular, or respiratory diseases; (2) a history of dizziness or loss of consciousness associated with controlled breathing; (3) current use of medications affecting the autonomic nervous system; and (4) neurological or visual impairments that could interfere with task performance. Breathing training Breathing training was conducted using the emWave Pro (HeartMath LLC, Boulder Creek, CA, USA), which displayed the real-time HRV waveform on a monitor. The emWave Pro generated a coherence score representing heart rhythm coherence, characterized by a sine wave-like pattern in the HRV waveform. The coherence score was automatically calculated by the emWave Pro based on established methodology (McCraty, 2022 ). The average coherence score across the training session was analyzed as an index of training performance. In the HRVBF group, training was administered according to a previously published protocol (Lehrer et al., 2013 ). First, the resonance frequency, defined as the respiratory rate that maximizes HRV amplitude, was determined from a range of 6.5, 6.0, 5.5, 5.0, and 4.5 breaths per minute. Subsequently, participants performed paced breathing at their individual resonance frequency with a 1:1 inhalation-to-exhalation (IE) ratio, guided by a visual breathing pacer. Participants were instructed to modulate the HRV waveform toward a sine wave-like pattern while receiving real-time visual feedback. They were further instructed to perform pursed-lip, diaphragmatic breathing, inhaling through the nose and exhaling through the mouth, with the goal of achieving higher coherence scores. In the control group, sham-paced breathing training was conducted according to our previous study (Saito et al., 2024 ). First, the respiratory rate with the least effect on HRV was determined from the range of 14, 13, 12, 11, and 10 breaths per minute. Next, participants performed paced breathing at the determined respiratory rate with a 1:1 IE ratio, guided by a breathing pacer without visual feedback of the HRV waveform. Participants were instructed to inhale through the nose and exhale through the mouth, as well as aim for higher coherence scores. Behavioral measurement and analysis Based on previous studies (McMackin et al., 2020 ; Owens et al., 2021 ; Robertson et al., 1997 ), vigilance was assessed using the SART. The SART was created and presented using PsychoPy (v2025.1.1) (Peirce et al., 2019 ). We defined “1–2, 4–9” as Go stimuli and “3” as the No-Go stimulus. Participants were instructed to press the space key as quickly and accurately as possible when Go stimuli were presented and to withhold responses when the No-Go stimulus was presented. Participants were seated approximately 60 cm from a 23-inch monitor with a resolution of 1920 × 1080 pixels. In the SART, a fixation cross was presented for 900 ms, followed by stimulus presentation for 250 ms. Reaction time (RT) was calculated from stimulus onset to 1000 ms, and the interstimulus interval was 1650 ms. The SART consisted of 360 trials, with 88.9% Go stimuli (320 trials) and 11.1% No-Go stimuli (40 trials). A 0–10 visual analog scale (VAS) was presented between task blocks to assess task-related subjective arousal (0 = “Not sleepy at all”, 10 = “Very sleepy”) and fatigue (0 = “Not tired at all”, 10 = “Very tired”). Before the formal experiment, participants completed 18 practice trials to familiarize themselves with the task. We defined a key press to Go stimuli as a correct response, a key press to a No-Go stimulus as a commission error, and no response to Go stimuli as an omission error. We calculated the mean reaction time (mRT) for correct responses, the commission error rate (CER), and the omission error rate (OER). To evaluate vigilance stability, we calculated the reaction time coefficient of variation (RTCV) for correct responses by dividing the standard deviation of the RT by the mRT. The VAS values for task-related arousal and fatigue were averaged across all task blocks. HRV recording and analysis Pulse wave data were recorded using an infrared pulse plethysmograph ear sensor supplied with the emWave Pro (HeartMath LLC, Boulder Creek, CA, USA) during seated rest. To minimize artifacts associated with the beginning and end of the recording, 5-min pulse wave data were obtained by removing the first and last 30 sec from a 6-min recording. Inter-beat interval (IBI) data were extracted at a sampling frequency of 370 Hz and analyzed using Kubios HRV Premium version 3.4.3 (Kubios Oy, Kuopio, Finland). During preprocessing, the IBI data were detrended using the smoothness prior’s method (λ = 500) and artifact-corrected using an automatic correction algorithm. IBI data with corrected beats exceeding 5% were classified as invalid, and participants with invalid data were excluded from analysis. Subsequently, IBI data were resampled at 4 Hz, and Fast Fourier Transform was applied with a window width of 256 s and 50% overlap. Based on recommendations for HRV analysis (Laborde et al., 2017 ), root mean square of successive differences (RMSSD) (ms) was calculated as a time-domain parameter and HF (0.15–0.4 Hz) (ms 2 ) as a frequency-domain parameter to index vmHRV. EEG recording and pre-processing EEG data were recorded using actiCHamp Plus (BrainProducts GmbH, Munich, Germany) with active electrodes mounted on an elastic cap according to the international 10–20 system. Electrodes were placed at the following 31 sites: Fp1, Fp2, F3, F4, F7, F8, Fz, FC1, FC2, FC5, FC6, FCz, C3, C4, Cz, T7, T8, CP1, CP2, CP5, CP6, TP9, TP10, P3, P4, P7, P8, Pz, O1, O2, and Oz. The reference electrode was placed at the nose tip, and the ground electrode was placed on the forehead. Electrooculogram (EOG) electrodes were placed at the infraorbital and supraorbital regions of the left eye to record vertical EOG and at the outer canthus of each eye to record horizontal EOG. Electrode impedance was maintained below 5 kΩ, and the sampling rate was 500 Hz. Participants were instructed to fix their gaze with eyes open and to avoid blinking and head movement during the EEG recording. All EEG data were processed using Brainstorm (Tadel et al., 2019 ). EEG signals were band-pass filtered between 1–40 Hz and re-referenced to the average reference. Independent component analysis was applied to remove artifacts related to eye blinks and eye movements. Time-frequency and ERP analysis We analyzed 5-min resting-state data obtained by removing the first and last 30 sec from a 6-min recording and task-related data collected during the SART. Resting-state data were epoched into 2000 ms intervals with 50% temporal overlap. Task-related data were epoched from 200 ms before to 1000 ms after stimulus onset. Baseline correction was performed using the 200 ms pre-stimulus period. Epochs exceeding ± 80 µV relative to baseline were rejected from analysis, and the remaining epochs were averaged. Time–frequency analysis focused on alpha band (8–13.5 Hz) power in parietal–occipital regions (P3, Pz, P4, O1, Oz, and O2). These electrodes and the frequency band were selected with reference to previous studies evaluating cortical arousal using resting-state EEG (Barry et al., 2007 ; Duda et al., 2025 ; Wolska et al., 2019 ). Time–frequency decomposition of epoched resting-state data was performed using Morlet wavelets. Absolute alpha band power (ABP) (µV 2 ) was averaged across parietal–occipital regions. Based on previous studies examining frontal-parietal N2 and P3 during the SART (McMackin et al., 2020 ; Owens et al., 2021 ; Zordan et al., 2008 ), electrodes were selected for ERP analysis: Fz, FCz, Cz, and Pz for N2 and P3. N2 and P3 amplitudes were averaged across electrodes. Time windows were defined for N2 (250–400 ms) and P3 (350–550 ms) based on visual inspection of grand-average waveforms and previous studies (McMackin et al., 2020 ; Owens et al., 2021 ; Zordan et al., 2008 ). Epochs corresponding to correct responses in No-Go and Go trials were analyzed. Baseline-to-peak negative and positive amplitudes (µV) were extracted for N2 and P3, respectively. Experimental procedure Figure 1 shows the overall experimental procedure of this study. Physiological, behavioral, and subjective measurements were performed before and after the breathing training. The experiment was conducted between 14:00 and 18:00 h, considering the influence of time of day on autonomic activity. The room temperature during the experiment was maintained at 24.6 ± 1.2 ℃. Participants were instructed to obtain sufficient sleep the night before the measurement, to participate at least two hours after eating, to refrain from alcohol or caffeine consumption, and to avoid strenuous exercise on the day of the experiment. A demographic questionnaire collected information regarding sex, age, height (cm), and weight (kg), and body mass index (BMI) was calculated. These demographic factors are known to influence HRV (Tiwari et al., 2021 ). Handedness was assessed using the Edinburgh Handedness Inventory (EHI) (Oldfield, 1971 ). Subjective arousal was assessed using the Japanese version of the Karolinska Sleepiness Scale (KSS-J), a 9-point scale highly correlated with physiological markers of sleepiness such as EEG activity (Kaida et al., 2006 ). A paper-based 10-cm VAS was administered to assess confidence in group assignment (0 = “I definitely belong to a control group,” 5 = “I don’t know,” and 10 = “I definitely belong to an intervention group”) to verify successful blinding. For physiological and behavioral measurements, resting-state HRV and EEG were recorded for 6 min, and task-related EEG was recorded during the SART for approximately 12 min. Fifteen-minute breathing training was performed in both the HRVBF and control groups. Each session comprised three main phases: pre-measurement, breathing training, and post-measurement EEG, electroencephalography; EHI, Edinburgh Handedness Inventory; ERP, event-related potential; HRV, heart rate variability; HRVBF, heart rate variability biofeedback; KSS-J, Japanese version of the Karolinska Sleepiness Scale; SART, sustained attention to response task; VAS, visual analog scale Statistical analysis Demographic characteristics, mean coherence scores, and VAS scores for blinding assessment were compared between groups using independent-samples t -tests. Training effects were examined using a 2 × 2 mixed-design analysis of variance (ANOVA) with group (HRVBF vs. control) as the between-participant factor and time (pre- and post-measurement) as the within-participant factor. This analysis was applied to vmHRV indices (RMSSD and HF), ABP, ERP amplitudes (N2 and P3 in Go and No-Go trials), vigilance indicators (mRT, RTCV, CER, and OER), task-related arousal and fatigue, and KSS-J scores. When a significant interaction was observed, post hoc analyses with Bonferroni correction were conducted. Partial eta-squared ( η p 2 ) was calculated to estimate effect size. To examine whether HRVBF-induced changes in resting-state arousal were associated with behavioral and physiological outcomes, post–pre change scores (Δ) were calculated and multiple regression analyses were performed using the forced-entry method. The model included ΔABP and ΔKSS-J as explanatory variables, and each Δbehavioral indicator and ΔERP amplitude served as a dependent variable. Explanatory and dependent variables were selected based on the significance of the regression model and standardized partial regression coefficients (β). To further explore potential non-linear associations, polynomial regression analyses (linear, quadratic, and cubic) were conducted for each group, and model fit was compared. This hierarchical approach allows detection of complex physiological relationships that may not be captured by linear models (Kroc & Olvera Astivia, 2025 ) and helps mitigate overfitting associated with higher-order polynomials (Gelman & Imbens, 2019 ). All statistical analyses were performed using SPSS version 30.0 (IBM Corp., Armonk, NY, USA), with statistical significance set at p < 0.05. Results Figure 2 shows the flow diagram based on the Consolidated Standards of Reporting Trials (CONSORT) 2025 (Hopewell et al., 2025 ). Due to noisy HRV data, three participants in the HRVBF group and two in the control group were excluded, and 25 participants in the HRVBF group (21.0 ± 2.4 years, 15 females) and 26 in the control group (20.9 ± 2.0 years, 14 females) were included in the final analysis. The flow diagram illustrates the participant recruitment process and procedural pathway leading to the final analytic dataset HRV, heart rate variability; HRVBF, heart rate variability biofeedback Participant characteristics Table 1 shows the intergroup comparisons of participant characteristics. Intergroup comparisons revealed no significant differences between groups ( p > 0.05). No intergroup comparison was performed for respiratory rate because the difference resulted directly from the experimental manipulation. The absence of intergroup differences in blinding assessment indicated successful group blinding. Table 1 Group comparison of demographic and training-related data. Parameters Groups p HRVBF (n = 25) Control (n = 26) Mean (SD) A. Demographic data Age (years) 21.0 (2.4) 20.9 (2.0) 0.904 Height (cm) 165.4 (7.3) 163.3 (10.5) 0.423 Weight (kg) 57.3 (7.7) 56.0 (9.8) 0.607 BMI (kg/m 2 ) 20.9 (1.9) 20.9 (2.0) 0.992 EHI 92.3 (19.7) 97.2 (7.6) 0.254 B. Training-related data Respiratory rate (breaths per minute) 5.5 (0.7) 12.7 (1.1) NA Average coherence score 4.3 (1.6) 1.8 (0.8) 0.000 * VAS score for blinding assessment (cm) 5.3 (2.4) 5.6 (2.3) 0.659 BMI, body mass index; EHI, Edinburgh Handedness Inventory; HRVBF, heart rate variability biofeedback; SD, standard deviation; VAS, visual analog scale. * p < 0.001. Breath training effects Table 2 presents the results of the 2 × 2 mixed-design ANOVA. Regarding vmHRV indices, the ANOVA revealed a significant main effect of time on RMSSD ( F (1, 49) = 5.551, p = 0.023, η p 2 = 0.102), indicating that RMSSD increased in both the HRVBF and control groups from pre- to post-training. For ERPs, significant main effects of time were observed for N2 Go ( F (1, 49) = 7.433, p = 0.009, η p 2 = 0.132) and N2 No-Go ( F (1, 49) = 5.827, p = 0.020, η p 2 = 0.106), showing that N2 Go amplitude increased and N2 No-Go amplitude decreased across both groups following training. For vigilance indicators, significant main effects of time were found for mRT ( F (1, 49) = 14.346, p < 0.001, η p 2 = 0.226) and CER ( F (1, 49) = 13.652, p = 0.001, η p 2 = 0.218), indicating decreased mRT and increased CER in both groups from pre- to post-training. Significant main effects of group and time were observed for task-related arousal ( F (1, 49) = 6.658, p = 0.013, η p 2 = 0.120) and fatigue ( F (1, 49) = 13.269, p = 0.001, η p 2 = 0.213), respectively. These results indicate that task-related arousal was higher in the HRVBF group compared with the control group, whereas task-related fatigue increased over time in both groups. The KSS-J score ( F (1, 49) = 17.174, p < 0.001, η p 2 = 0.260) showed a significant main effect of time, indicating decreased subjective arousal in both groups. However, no significant group × time interactions were observed for any variable ( p > 0.05). Table 2 ANOVA results for physiological, behavioral, and subjective variables. Parameters Groups Source of variation HRVBF (n = 25) Control (n = 26) Group Time Group × Time Pre Post Pre Post F p η p 2 F p η p 2 F p η p 2 Mean (SD) RMSSD (ms) 49.8 (21.4) 53.9 (32.9) 43.5 (16.7) 54.9 (31.0) 0.155 0.695 0.003 5.551 0.023 * 0.102 1.233 0.272 0.025 HF (ms 2 ) 1215.7 (982.8) 1299.4 (1703.9) 904.5 (820.5) 1228.1 (1345.5) 0.382 0.540 0.008 1.464 0.232 0.029 0.508 0.479 0.010 ABP (×10 − 3 µV 2 ) 4.16 (4.51) 4.63 (8.57) 3.31 (2.34) 3.47 (2.43) 0.613 0.437 0.012 0.262 0.611 0.005 0.058 0.811 0.001 N2 Go (µV) -1.44 (0.99) -0.98 (1.09) -1.02 (1.29) -0.88 (1.24) 0.707 0.405 0.014 7.433 0.009 ** 0.132 2.144 0.150 0.042 N2 No-Go (µV) -4.08 (2.24) -4.30 (2.46) -3.70 (2.00) -4.46 (1.91) 0.038 0.846 0.001 5.827 0.020 * 0.106 1.777 0.189 0.035 P3 Go (µV) 1.41 (1.04) 1.51 (1.17) 1.97 (1.19) 2.09 (1.50) 3.139 0.083 0.060 0.780 0.381 0.016 0.007 0.935 0.000 P3 No-Go (µV) 6.36 (2.14) 6.43 (3.07) 6.61 (2.11) 6.57 (2.00) 0.104 0.749 0.002 0.002 0.962 0.000 0.059 0.809 0.001 mRT (ms) 363.8 (44.5) 356.1 (39.9) 362.3 (38.1) 346.0 (39.7) 0.284 0.596 0.006 14.346 0.000 *** 0.226 1.864 0.178 0.037 RTCV 0.15 (0.04) 0.16 (0.05) 0.16 (0.04) 0.17 (0.04) 1.183 0.282 0.024 3.534 0.066 0.067 0.019 0.890 0.000 CER (%) 28.4 (16.4) 31.3 (17.3) 27.3 (20.2) 36.2 (21.6) 0.136 0.714 0.003 13.652 0.001 *** 0.218 3.499 0.067 0.067 OER (%) 0.5 (1.1) 2.3 (8.2) 0.6 (1.4) 0.7 (1.1) 0.813 0.372 0.016 1.470 0.231 0.029 0.986 0.326 0.020 Task-related arousal 6.8 (2.4) 6.8 (2.0) 6.0 (2.0) 5.0 (1.8) 6.658 0.013 * 0.120 3.483 0.068 0.066 3.011 0.089 0.058 Task-related fatigue 4.2 (2.1) 5.1 (2.5) 4.1 (2.6) 5.2 (2.3) 0.003 0.953 0.000 13.269 0.001 *** 0.213 0.094 0.760 0.002 KSS-J score 4.0 (1.6) 5.0 (1.9) 4.2 (1.8) 6.1 (1.6) 3.528 0.066 0.067 17.174 0.000 *** 0.260 1.713 0.197 0.034 ABP, alpha band power; CER, commission error rate; HF, high frequency; HRVBF, heart rate variability biofeedback; KSS-J, Japanese version of the Karolinska Sleepiness Scale; mRT, mean reaction time; OER, omission error rate; RMSSD, root mean square of successive differences; RTCV, reaction time coefficient of variation; SD, standard deviation. * p < 0.05; ** p < 0.01; *** p < 0.001 Relationship between arousal and behavioral and physiological indicators The multiple regression analysis conducted to identify the arousal indicator associated with behavioral and physiological outcomes showed that only the regression equation predicting ΔCER was significant ( p = 0.045). The adjusted coefficient of determination (R 2 ) was 0.085. The standardized β coefficient for ΔABP was significantly negative (β = -0.273, p = 0.049). The variance inflation factor was 1.001 for all explanatory variables, indicating no evidence of multicollinearity. The Durbin–Watson statistic was 1.483, indicating independence of residuals. Subsequently, polynomial regression analyses were performed separately for each group, with ΔABP as the explanatory variable and ΔCER as the dependent variable (Fig. 3 ). In the HRVBF group, the quadratic regression model was significant (R 2 = 0.276, p = 0.029), whereas the linear (R 2 = 0.140, p = 0.065) and cubic (R 2 = 0.303, p = 0.052) models were not significant. In contrast, in the control group, none of the polynomial regression models (linear: R 2 = 0.000, p = 0.918; quadratic: R 2 = 0.042, p = 0.613; cubic: R 2 = 0.076, p = 0.621) reached statistical significance. The figure illustrates the polynomial regression of ΔCER on ΔABP in the HRVBF and control groups. Δ indicates the change from pre- to post-measurement. Statistical significance was observed only for the quadratic regression model in the HRVBF group (R 2 = 0.276, p = 0.029). The 95% confidence interval is displayed for the quadratic model only ABP, alpha band power; CER, commission error rate; HRVBF, heart rate variability biofeedback Discussion Using HRV, EEG, and the SART, this study investigated the effects of HRVBF on cortical arousal and vigilance, as well as the relationship between these variables, by testing two specific hypotheses. Contrary to the first hypothesis that HRVBF would modulate cortical arousal toward an optimal level and enhance vigilance, the findings did not demonstrate superior physiological or behavioral improvements compared with sham-paced breathing. Instead, both groups exhibited physiological relaxation and reduced inhibitory control, characterized by increased RMSSD and CER, together with decreased N2 No-Go amplitudes. However, the second hypothesis was supported: polynomial regression analyses revealed a significant quadratic relationship between ΔABP and ΔCER exclusively in the HRVBF group. This finding suggests that an inverted U-shaped relationship between arousal level and performance may underlie the variable efficacy of HRVBF training. Contrary to the initial prediction, the HRVBF group did not demonstrate significant effects relative to the control group, and both groups showed comparable relaxation responses. The increase in RMSSD observed in both groups likely reflects vagal activation induced by paced breathing itself, independent of HRV feedback (Laborde et al., 2022a ). Consistent with this physiological relaxation, decreased mRT and increased CER, together with reduced N2 No-Go amplitudes, collectively indicate diminished inhibitory control over time, potentially driven by reduced vigilance. Previous studies using the SART or ERP paradigms have reported similar patterns (Einziger et al., 2021 ; Wilson et al., 2016 ), and the increases in task-related fatigue and KSS-J scores further support this interpretation, indicating that both groups experienced a comparable decline in vigilance following training. These findings regarding HRVBF effects may reflect the complex dynamics observed in single-session HRVBF interventions. Although a single-session HRVBF has been shown to promote physiological and subjective relaxation, as evidenced by increases in HRV (Lin et al., 2020 ), EEG changes (Prinsloo et al., 2013 ), and self-reported relaxation (Blum et al., 2019 ; Mazgelytė et al., 2022 ), its acute effects on cognitive function remain mixed (Bahameish & Stockman, 2024 ; Blaser et al., 2023 ; J. Lee & Finkelstein, 2015 ; Prinsloo et al., 2011 ; Sherlin et al., 2009 ). Several previous studies have demonstrated beneficial effects under specific conditions, particularly during high-stress contexts (Blaser et al., 2023 ; Prinsloo et al., 2011 ; Sherlin et al., 2009 ). Even in multi-session HRVBF interventions, one study has suggested no significant improvement in cognitive function (Nashiro et al., 2023 ), whereas other studies have reported positive effects (Jester et al., 2019 ; Sutarto et al., 2013 ). Therefore, in single-session HRVBF, relaxation may represent the dominant outcome, potentially inducing a state of relative hypo-arousal. Although such a state may be advantageous for stress reduction, it may also mask potential cognitive benefits of HRVBF or even reduce vigilance during a monotonous task such as the SART in the present study. Future research should adopt longitudinal designs to determine whether repeated HRVBF training produces arousal-regulation effects that predominate over relaxation effects and whether such training improves vigilance indicators under conditions of high stress or cognitive load. Our analysis indicated a significant quadratic relationship between ΔABP and ΔCER exclusively in the HRVBF group. This inverted U-shaped relationship indicates that both positive and negative changes in ABP were associated with decreases in CER (i.e., enhanced vigilance), whereas minimal or no change in ABP was associated with increases in CER (i.e., reduced vigilance). These findings suggest that the efficacy of HRVBF may be associated with shifting cortical arousal towards an optimal functional range. A recent theoretical model has posited that higher vmHRV serves as an indicator of the capacity to flexibly allocate cognitive resources to meet situational demands (Laborde et al., 2022b ; Magnon et al., 2022 ). In parallel with vmHRV changes, relatively stable alpha oscillatory activity may reflect ineffective arousal regulation associated with diminished vigilance, whereas dynamic modulation of alpha oscillations may indicate adaptive arousal regulation, characterized by active re-engagement of attention and flexible allocation of neural resources. This interpretation aligns with recent EEG research suggesting that dynamic alpha oscillations are critical for sustaining attention (Li et al., 2023 ; Peylo et al., 2024 ). Furthermore, the absence of this quadratic relationship in the control group suggests that HRVBF training, unlike paced breathing alone, may engage a distinct regulatory mechanism that facilitates optimization of arousal states and vigilance performance. Mechanistically, this effect may involve activation of vagal afferent pathways projecting to the locus coeruleus in the brainstem. The locus coeruleus-noradrenaline system functions as a central regulator of arousal and neural gain, and its activity is closely linked to attentional control and vigilance (Torres et al., 2025 ). It has been proposed that increased vmHRV enhances vagal signals to the locus coeruleus, thereby optimizing cortical excitability and gain modulation (Duschek et al., 2013 ; Mather & Thayer, 2018 ). Consequently, this bottom-up regulatory influence may strengthen the coupling between cortical arousal and vigilance performance. From a practical perspective, the observed quadratic relationship suggests that individual variability in arousal responses to HRVBF training may contribute to heterogeneity in training efficacy. Although these findings are limited to healthy young adults and a single training session, it is possible that those changes in alpha oscillatory dynamics could serve as indicators for identifying individuals who are more likely to benefit from HRVBF. Overall, these findings suggest that while HRVBF does not uniformly produce superior effects compared to paced breathing, it uniquely demonstrates a non-linear relationship between cortical arousal and vigilance. This insight may inform future intervention strategies aimed at shifting individual arousal states towards optimal ranges. Certain limitations should be acknowledged in this study. First, the multiple regression analysis identified CER as the only significant dependent variable predicted by arousal indicators, which limits the generalizability of the findings to other aspects of vigilance. The absence of significant associations with mRT, RTCV, OER, and ERP components suggests that the arousal–vigilance relationship may be specific to inhibitory control processes indexed by CER. Second, although the SART is well established for assessing sustained attention, it may not fully capture the multidimensional nature of vigilance. Alternative behavioral paradigms, such as the psychomotor vigilance task or continuous performance task under varying cognitive loads, may reveal different arousal–vigilance relationships. Third, participants consisted exclusively of healthy young adults. Abnormalities in arousal regulation are known to occur in older adults (T.-H. Lee et al., 2018 ) and in clinical populations, including individuals with depression (Xie et al., 2024 ), anxiety disorders (Florido et al., 2025 ), and attention deficit/hyperactivity disorder (Strauß et al., 2018 ). Therefore, the observed arousal–vigilance relationship may differ in older or clinical populations characterized by atypical arousal profiles, which limits the generalizability of the present findings. Future research should examine optimized arousal regulation and the persistence of the inverted U-shaped relationship by recruiting diverse cohorts and implementing longitudinal HRVBF protocols combined with varied vigilance tasks. Conclusion This study investigated the effects of HRVBF on cortical arousal and vigilance, as well as the relationship between these constructs, using multimodal physiological and behavioral measurements. HRVBF did not demonstrate superior improvements in vigilance compared with sham-paced breathing. The results suggest that relaxation effects may predominate during a single session of HRVBF. Polynomial regression analyses indicated a significant quadratic relationship between changes in cortical arousal and vigilance exclusively in the HRVBF group. This inverted U-shaped relationship suggests that HRVBF may facilitate arousal regulation toward optimal levels, potentially explaining the variable efficacy observed in previous studies. These findings highlight the importance of considering non-linear arousal–vigilance relationships when evaluating HRVBF interventions. Future research should employ longitudinal designs with diverse populations to examine the persistence of the relationship between optimal arousal regulation and subsequent cognitive benefits. Declarations Ethics approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Declaration of Helsinki and its later amendments. This study was approved by the Ethics Committee of the Faculty of Health Sciences, Hokkaido University (approval number: 25-37). Consent to participate Informed consent was obtained from all individual participants included in the study. Funding statement This study was funded by the Japan Society for the Promotion of Science Grant-in-Aid for Scientific Research (JSPS KAKENHI Grant Number 24K23754). Author contributions Ryuji Saito : Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization, Project administration, Funding acquisition. Kazuki Yoshida : Conceptualization, Methodology, Validation, Writing – review and editing. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8939605","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":598376320,"identity":"4753d8fd-56cb-4283-ac14-4e40dede05b1","order_by":0,"name":"Ryuji Saito","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYLCCBAYGOQMGNiDLACHIjF9LAoMxiVqA1iRuAGshBpg3sD/88PCHTfp29rY0CYaCe4n9DDwGDD9qGNjNcWiROcBjLJGQkJa7s+fYMQkGg+LEmQ08Bow9xxiYLRuwa5GQf8MA1HI4d8ON9DbpPwYJiRvuvzFg4G1gYDY4gEMLA/vjH0At6QZALUBbgFoOAG35i1cLgxnIlgSDG2nH4FqY8dvCY2aRkJZmuOHMsWQLoBbjmQ1sBYdljkng9gvQYTd/2NjIGxxvM7zB8CdBtp+BeePDNzU2ybhCDAM4gswGOkki2YCASjiwhzHsiNYyCkbBKBgFwx0AADUhUI14D+0sAAAAAElFTkSuQmCC","orcid":"","institution":"Health Sciences University of Hokkaido","correspondingAuthor":true,"prefix":"","firstName":"Ryuji","middleName":"","lastName":"Saito","suffix":""},{"id":598376321,"identity":"60e778da-995d-420b-8a5e-bc12e40d5739","order_by":1,"name":"Kazuki Yoshida","email":"","orcid":"","institution":"Hokkaido University","correspondingAuthor":false,"prefix":"","firstName":"Kazuki","middleName":"","lastName":"Yoshida","suffix":""},{"id":598376322,"identity":"621ce241-4f43-4b92-8931-8c90fa4e955f","order_by":2,"name":"Daisuke Sawamura","email":"","orcid":"","institution":"Hokkaido University","correspondingAuthor":false,"prefix":"","firstName":"Daisuke","middleName":"","lastName":"Sawamura","suffix":""}],"badges":[],"createdAt":"2026-02-22 14:08:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8939605/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8939605/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104168429,"identity":"191358fc-0ca8-4a3e-92b0-800a144369c4","added_by":"auto","created_at":"2026-03-08 14:32:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":73270,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExperimental procedure and timeline\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8939605/v1/af1cf7c459376c7407fe1923.png"},{"id":104168427,"identity":"d2cbe5a7-ad72-45ef-a86f-0498a3d735c2","added_by":"auto","created_at":"2026-03-08 14:32:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":206554,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCONSORT 2025 flow diagram\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8939605/v1/143ff44ca7e3401c9cf7cf9a.png"},{"id":104779589,"identity":"83d815fa-4ab7-47b3-babe-1be722b9d48b","added_by":"auto","created_at":"2026-03-17 07:42:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":163773,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQuadratic relationship between changes in ABP and CER following HRVBF\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8939605/v1/b4ea69ba100349491187e122.png"},{"id":104355266,"identity":"4e57548a-82ff-4f0b-b078-4f0f6608c9d1","added_by":"auto","created_at":"2026-03-10 21:51:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":163773,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQuadratic relationship between changes in ABP and CER following HRVBF\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"32.png","url":"https://assets-eu.researchsquare.com/files/rs-8939605/v1/4b3f57a6a426d4b6b2c67512.png"},{"id":104784136,"identity":"1db0b8a0-fadb-4a28-93bd-edf36a3a1ba6","added_by":"auto","created_at":"2026-03-17 08:05:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1747696,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8939605/v1/861658cd-da15-4429-bfad-f0376bd60d8e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The effect of heart rate variability biofeedback on cortical arousal and its subsequent influence on vigilance: a randomized controlled study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHeart rate variability biofeedback (HRVBF) is a breathing-based intervention that integrates slow-paced respiration with real-time heart rate variability (HRV) waveform feedback to enhance autonomic nervous system regulation (Lehrer et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). By guiding respiration at an individual\u0026rsquo;s resonant frequency, typically between 4.5 and 6.5 breaths per minute (approximately 0.1 Hz), HRVBF synchronizes respiratory sinus arrhythmia, baroreflex activity, and blood pressure oscillations, thereby augmenting vagally mediated HRV (vmHRV) (Giorgi \u0026amp; Tedeschi, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sevoz-Couche \u0026amp; Laborde, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Within the neurovisceral integration framework, vmHRV is considered an index of the functional integrity of prefrontal\u0026ndash;subcortical circuits that support executive functions, including attention, inhibitory control, and cognitive flexibility (Smith et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Thayer \u0026amp; Lane, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Accordingly, HRVBF has been increasingly examined as a potential strategy for improving cognitive function. Recent systematic reviews and meta-analyses have reported beneficial effects of HRVBF on cognitive outcomes (Tinello et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and psychiatric symptoms (Pizzoli et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) across diverse populations. However, despite accumulating evidence supporting HRVBF efficacy, reported effect sizes are generally small to moderate, with substantial variability in outcomes observed in recent meta-analytic findings (Lehrer et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Clarifying the mechanisms contributing to this variability is essential for refining HRVBF protocols and identifying individuals most likely to experience cognitive benefits from training.\u003c/p\u003e \u003cp\u003eOne potential mechanism that may account for the variable efficacy of HRVBF involves its modulation of cortical arousal and the subsequent influence on cognitive function. Arousal, defined as the global activation state of the central and autonomic nervous systems, plays a fundamental role in regulating vigilance, attention, and higher-order cognitive processes (Pribram \u0026amp; McGuinness, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1975\u003c/span\u003e). The relationship between arousal and performance has been classically described by the Yerkes-Dodson law, which posits an inverted U-shaped function in which optimal performance occurs at moderate arousal levels, with decrements observed at both low and high extremes (Yerkes \u0026amp; Dodson, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e1908\u003c/span\u003e). In electroencephalography (EEG), frequency components, particularly alpha band power in occipital regions, have been considered reliable indicators of cortical arousal (Kim et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Schubring \u0026amp; Schupp, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), with decreased alpha band power typically associated with heightened arousal levels (Barry et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Duda et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wolska et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Previous studies have demonstrated positive correlations between vmHRV and alpha band power (Attar et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kawashima et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Melo et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), suggesting that higher vmHRV is associated with relaxed alertness, a state characterized by sustained attentional engagement accompanied by low physiological stress. Given that HRVBF may modulate arousal toward optimal levels alongside increases in vmHRV, HRVBF-induced changes in arousal may contribute to improvements in cognitive function.\u003c/p\u003e \u003cp\u003eVigilance, defined as the capacity to sustain attention over extended periods, represents a critical cognitive function that depends on optimal arousal regulation (Oken et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The sustained attention to response task (SART) has been widely used to assess vigilance, requiring participants to respond to frequent Go stimuli while withholding responses to rare No-Go stimuli (Robertson et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Performance on the SART is sensitive to fluctuations in arousal and attention, with commission errors (inappropriate responses to No-Go stimuli) serving as a primary behavioral indicator of diminished inhibitory control (Johnson et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Smilek et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In the present study, vigilance is operationalized as behavioral performance on the SART. Event-related potentials (ERPs), particularly the N2 and P3 components, serve as neurophysiological indices of inhibitory control and attentional resource allocation during the SART (Ahumada-M\u0026eacute;ndez et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Larger N2 and P3 amplitudes are interpreted as reflecting greater top-down cognitive control associated with these functions (Cheval et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kotani et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). If HRVBF modulates arousal to an appropriate level through autonomic\u0026ndash;cortical coupling, HRVBF training is expected to enhance vigilance and increase the corresponding ERP amplitudes.\u003c/p\u003e \u003cp\u003eDespite the theoretical rationale linking HRVBF, arousal, and vigilance, empirical investigations of this relationship remain limited. Previous HRVBF studies have primarily focused on autonomic outcomes or clinical symptoms (Lehrer et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pizzoli et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Spada et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), with limited attention to the cognitive and neurophysiological mechanisms that may underlie the training effects (Tinello et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, the assumption of linear relationships between physiological and behavioral variables may obscure more complex, non-linear dynamics that characterize physiological\u0026ndash;cognitive interactions (Johannessen et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Young \u0026amp; Benton, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). As studies examining the Yerkes-Dodson law have suggested, the relationship between arousal and cognitive function is inherently non-linear, following an inverted U-shaped pattern (Diamond, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Nieuwenhuis, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This implies that identical changes in arousal may produce divergent effects on cognitive function, depending on an individual\u0026rsquo;s baseline arousal level, with enhancement for those at suboptimal levels but minimal for those already near the optimal range. Linear models, which assume uniform directional effects, are inherently unable to capture such state-dependent dynamics. To provide a more comprehensive understanding, it is necessary to examine not only the effects of HRVBF on cortical arousal and vigilance but also the association between them by integrating multimodal measurements, including HRV, EEG, and behavioral indicators, with polynomial regression analysis.\u003c/p\u003e \u003cp\u003eThe present study aimed to examine the effects of HRVBF on cortical arousal and vigilance, as well as the association between these variables as a potential mechanism underlying variable effects. To address this objective, we employed a multimodal approach integrating HRV, EEG, and the SART. We tested two primary hypotheses. First, we hypothesized that HRVBF would increase vmHRV and modulate cortical arousal toward an optimal level, as indexed by changes in alpha band power, and that these changes would be accompanied by enhanced vigilance, reflected by reduced commission errors and larger N2 and P3 amplitudes. Second, we hypothesized that the association between HRVBF-induced changes in cortical arousal and vigilance would be better explained by a quadratic model than by linear or cubic models in polynomial regression.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eBased on a previous study reporting a positive correlation between vmHRV and EEG alpha band power (Attar et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), we performed a sample size estimation using G*Power 3.1 (Faul et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) with a two-tailed test, an effect size (r\u0026thinsp;=\u0026thinsp;0.53), a significance level (α\u0026thinsp;=\u0026thinsp;0.05), and statistical power (1\u0026thinsp;\u0026minus;\u0026thinsp;β\u0026thinsp;=\u0026thinsp;0.80). The required sample size was calculated as 23 participants per group, yielding a total of 46 participants across two groups. To account for a potential 20% dropout rate and possible outliers, the final target sample size was set at 56 participants. Participants were stratified by sex and randomly assigned to the HRVBF or control groups.\u003c/p\u003e \u003cp\u003eRight-handed healthy undergraduate and graduate students were recruited. Given the established effects of age on HRV (Tiwari et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), participants aged 18\u0026ndash;30 years were enrolled. The exclusion criteria were as follows: (1) a history of psychiatric disorders or neurological, cardiovascular, or respiratory diseases; (2) a history of dizziness or loss of consciousness associated with controlled breathing; (3) current use of medications affecting the autonomic nervous system; and (4) neurological or visual impairments that could interfere with task performance.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBreathing training\u003c/h3\u003e\n\u003cp\u003eBreathing training was conducted using the emWave Pro (HeartMath LLC, Boulder Creek, CA, USA), which displayed the real-time HRV waveform on a monitor. The emWave Pro generated a coherence score representing heart rhythm coherence, characterized by a sine wave-like pattern in the HRV waveform. The coherence score was automatically calculated by the emWave Pro based on established methodology (McCraty, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The average coherence score across the training session was analyzed as an index of training performance.\u003c/p\u003e \u003cp\u003eIn the HRVBF group, training was administered according to a previously published protocol (Lehrer et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). First, the resonance frequency, defined as the respiratory rate that maximizes HRV amplitude, was determined from a range of 6.5, 6.0, 5.5, 5.0, and 4.5 breaths per minute. Subsequently, participants performed paced breathing at their individual resonance frequency with a 1:1 inhalation-to-exhalation (IE) ratio, guided by a visual breathing pacer. Participants were instructed to modulate the HRV waveform toward a sine wave-like pattern while receiving real-time visual feedback. They were further instructed to perform pursed-lip, diaphragmatic breathing, inhaling through the nose and exhaling through the mouth, with the goal of achieving higher coherence scores.\u003c/p\u003e \u003cp\u003eIn the control group, sham-paced breathing training was conducted according to our previous study (Saito et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). First, the respiratory rate with the least effect on HRV was determined from the range of 14, 13, 12, 11, and 10 breaths per minute. Next, participants performed paced breathing at the determined respiratory rate with a 1:1 IE ratio, guided by a breathing pacer without visual feedback of the HRV waveform. Participants were instructed to inhale through the nose and exhale through the mouth, as well as aim for higher coherence scores.\u003c/p\u003e\n\u003ch3\u003eBehavioral measurement and analysis\u003c/h3\u003e\n\u003cp\u003eBased on previous studies (McMackin et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Owens et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Robertson et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), vigilance was assessed using the SART. The SART was created and presented using PsychoPy (v2025.1.1) (Peirce et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). We defined \u0026ldquo;1\u0026ndash;2, 4\u0026ndash;9\u0026rdquo; as Go stimuli and \u0026ldquo;3\u0026rdquo; as the No-Go stimulus. Participants were instructed to press the space key as quickly and accurately as possible when Go stimuli were presented and to withhold responses when the No-Go stimulus was presented. Participants were seated approximately 60 cm from a 23-inch monitor with a resolution of 1920 \u0026times; 1080 pixels. In the SART, a fixation cross was presented for 900 ms, followed by stimulus presentation for 250 ms. Reaction time (RT) was calculated from stimulus onset to 1000 ms, and the interstimulus interval was 1650 ms. The SART consisted of 360 trials, with 88.9% Go stimuli (320 trials) and 11.1% No-Go stimuli (40 trials). A 0\u0026ndash;10 visual analog scale (VAS) was presented between task blocks to assess task-related subjective arousal (0 = \u0026ldquo;Not sleepy at all\u0026rdquo;, 10 = \u0026ldquo;Very sleepy\u0026rdquo;) and fatigue (0 = \u0026ldquo;Not tired at all\u0026rdquo;, 10 = \u0026ldquo;Very tired\u0026rdquo;). Before the formal experiment, participants completed 18 practice trials to familiarize themselves with the task.\u003c/p\u003e \u003cp\u003eWe defined a key press to Go stimuli as a correct response, a key press to a No-Go stimulus as a commission error, and no response to Go stimuli as an omission error. We calculated the mean reaction time (mRT) for correct responses, the commission error rate (CER), and the omission error rate (OER). To evaluate vigilance stability, we calculated the reaction time coefficient of variation (RTCV) for correct responses by dividing the standard deviation of the RT by the mRT. The VAS values for task-related arousal and fatigue were averaged across all task blocks.\u003c/p\u003e\n\u003ch3\u003eHRV recording and analysis\u003c/h3\u003e\n\u003cp\u003ePulse wave data were recorded using an infrared pulse plethysmograph ear sensor supplied with the emWave Pro (HeartMath LLC, Boulder Creek, CA, USA) during seated rest. To minimize artifacts associated with the beginning and end of the recording, 5-min pulse wave data were obtained by removing the first and last 30 sec from a 6-min recording. Inter-beat interval (IBI) data were extracted at a sampling frequency of 370 Hz and analyzed using Kubios HRV Premium version 3.4.3 (Kubios Oy, Kuopio, Finland). During preprocessing, the IBI data were detrended using the smoothness prior\u0026rsquo;s method (λ\u0026thinsp;=\u0026thinsp;500) and artifact-corrected using an automatic correction algorithm. IBI data with corrected beats exceeding 5% were classified as invalid, and participants with invalid data were excluded from analysis. Subsequently, IBI data were resampled at 4 Hz, and Fast Fourier Transform was applied with a window width of 256 s and 50% overlap. Based on recommendations for HRV analysis (Laborde et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), root mean square of successive differences (RMSSD) (ms) was calculated as a time-domain parameter and HF (0.15\u0026ndash;0.4 Hz) (ms\u003csup\u003e2\u003c/sup\u003e) as a frequency-domain parameter to index vmHRV.\u003c/p\u003e\n\u003ch3\u003eEEG recording and pre-processing\u003c/h3\u003e\n\u003cp\u003eEEG data were recorded using actiCHamp Plus (BrainProducts GmbH, Munich, Germany) with active electrodes mounted on an elastic cap according to the international 10\u0026ndash;20 system. Electrodes were placed at the following 31 sites: Fp1, Fp2, F3, F4, F7, F8, Fz, FC1, FC2, FC5, FC6, FCz, C3, C4, Cz, T7, T8, CP1, CP2, CP5, CP6, TP9, TP10, P3, P4, P7, P8, Pz, O1, O2, and Oz. The reference electrode was placed at the nose tip, and the ground electrode was placed on the forehead. Electrooculogram (EOG) electrodes were placed at the infraorbital and supraorbital regions of the left eye to record vertical EOG and at the outer canthus of each eye to record horizontal EOG. Electrode impedance was maintained below 5 kΩ, and the sampling rate was 500 Hz. Participants were instructed to fix their gaze with eyes open and to avoid blinking and head movement during the EEG recording. All EEG data were processed using Brainstorm (Tadel et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). EEG signals were band-pass filtered between 1\u0026ndash;40 Hz and re-referenced to the average reference. Independent component analysis was applied to remove artifacts related to eye blinks and eye movements.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTime-frequency and ERP analysis\u003c/h2\u003e \u003cp\u003eWe analyzed 5-min resting-state data obtained by removing the first and last 30 sec from a 6-min recording and task-related data collected during the SART. Resting-state data were epoched into 2000 ms intervals with 50% temporal overlap. Task-related data were epoched from 200 ms before to 1000 ms after stimulus onset. Baseline correction was performed using the 200 ms pre-stimulus period. Epochs exceeding\u0026thinsp;\u0026plusmn;\u0026thinsp;80 \u0026micro;V relative to baseline were rejected from analysis, and the remaining epochs were averaged.\u003c/p\u003e \u003cp\u003eTime\u0026ndash;frequency analysis focused on alpha band (8\u0026ndash;13.5 Hz) power in parietal\u0026ndash;occipital regions (P3, Pz, P4, O1, Oz, and O2). These electrodes and the frequency band were selected with reference to previous studies evaluating cortical arousal using resting-state EEG (Barry et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Duda et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wolska et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Time\u0026ndash;frequency decomposition of epoched resting-state data was performed using Morlet wavelets. Absolute alpha band power (ABP) (\u0026micro;V\u003csup\u003e2\u003c/sup\u003e) was averaged across parietal\u0026ndash;occipital regions.\u003c/p\u003e \u003cp\u003eBased on previous studies examining frontal-parietal N2 and P3 during the SART (McMackin et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Owens et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zordan et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), electrodes were selected for ERP analysis: Fz, FCz, Cz, and Pz for N2 and P3. N2 and P3 amplitudes were averaged across electrodes. Time windows were defined for N2 (250\u0026ndash;400 ms) and P3 (350\u0026ndash;550 ms) based on visual inspection of grand-average waveforms and previous studies (McMackin et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Owens et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zordan et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Epochs corresponding to correct responses in No-Go and Go trials were analyzed. Baseline-to-peak negative and positive amplitudes (\u0026micro;V) were extracted for N2 and P3, respectively.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExperimental procedure\u003c/h3\u003e\n\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the overall experimental procedure of this study. Physiological, behavioral, and subjective measurements were performed before and after the breathing training. The experiment was conducted between 14:00 and 18:00 h, considering the influence of time of day on autonomic activity. The room temperature during the experiment was maintained at 24.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 ℃. Participants were instructed to obtain sufficient sleep the night before the measurement, to participate at least two hours after eating, to refrain from alcohol or caffeine consumption, and to avoid strenuous exercise on the day of the experiment. A demographic questionnaire collected information regarding sex, age, height (cm), and weight (kg), and body mass index (BMI) was calculated. These demographic factors are known to influence HRV (Tiwari et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Handedness was assessed using the Edinburgh Handedness Inventory (EHI) (Oldfield, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1971\u003c/span\u003e). Subjective arousal was assessed using the Japanese version of the Karolinska Sleepiness Scale (KSS-J), a 9-point scale highly correlated with physiological markers of sleepiness such as EEG activity (Kaida et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). A paper-based 10-cm VAS was administered to assess confidence in group assignment (0 = \u0026ldquo;I definitely belong to a control group,\u0026rdquo; 5 = \u0026ldquo;I don\u0026rsquo;t know,\u0026rdquo; and 10 = \u0026ldquo;I definitely belong to an intervention group\u0026rdquo;) to verify successful blinding. For physiological and behavioral measurements, resting-state HRV and EEG were recorded for 6 min, and task-related EEG was recorded during the SART for approximately 12 min. Fifteen-minute breathing training was performed in both the HRVBF and control groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEach session comprised three main phases: pre-measurement, breathing training, and post-measurement\u003c/p\u003e \u003cp\u003eEEG, electroencephalography; EHI, Edinburgh Handedness Inventory; ERP, event-related potential; HRV, heart rate variability; HRVBF, heart rate variability biofeedback; KSS-J, Japanese version of the Karolinska Sleepiness Scale; SART, sustained attention to response task; VAS, visual analog scale\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDemographic characteristics, mean coherence scores, and VAS scores for blinding assessment were compared between groups using independent-samples \u003cem\u003et\u003c/em\u003e-tests.\u003c/p\u003e \u003cp\u003eTraining effects were examined using a 2 \u0026times; 2 mixed-design analysis of variance (ANOVA) with group (HRVBF vs. control) as the between-participant factor and time (pre- and post-measurement) as the within-participant factor. This analysis was applied to vmHRV indices (RMSSD and HF), ABP, ERP amplitudes (N2 and P3 in Go and No-Go trials), vigilance indicators (mRT, RTCV, CER, and OER), task-related arousal and fatigue, and KSS-J scores. When a significant interaction was observed, post hoc analyses with Bonferroni correction were conducted. Partial eta-squared (\u003cem\u003eη\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e) was calculated to estimate effect size.\u003c/p\u003e \u003cp\u003eTo examine whether HRVBF-induced changes in resting-state arousal were associated with behavioral and physiological outcomes, post\u0026ndash;pre change scores (Δ) were calculated and multiple regression analyses were performed using the forced-entry method. The model included ΔABP and ΔKSS-J as explanatory variables, and each Δbehavioral indicator and ΔERP amplitude served as a dependent variable. Explanatory and dependent variables were selected based on the significance of the regression model and standardized partial regression coefficients (β). To further explore potential non-linear associations, polynomial regression analyses (linear, quadratic, and cubic) were conducted for each group, and model fit was compared. This hierarchical approach allows detection of complex physiological relationships that may not be captured by linear models (Kroc \u0026amp; Olvera Astivia, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and helps mitigate overfitting associated with higher-order polynomials (Gelman \u0026amp; Imbens, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). All statistical analyses were performed using SPSS version 30.0 (IBM Corp., Armonk, NY, USA), with statistical significance set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the flow diagram based on the Consolidated Standards of Reporting Trials (CONSORT) 2025 (Hopewell et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Due to noisy HRV data, three participants in the HRVBF group and two in the control group were excluded, and 25 participants in the HRVBF group (21.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4 years, 15 females) and 26 in the control group (20.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0 years, 14 females) were included in the final analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe flow diagram illustrates the participant recruitment process and procedural pathway leading to the final analytic dataset\u003c/p\u003e \u003cp\u003eHRV, heart rate variability; HRVBF, heart rate variability biofeedback\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eParticipant characteristics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the intergroup comparisons of participant characteristics. Intergroup comparisons revealed no significant differences between groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). No intergroup comparison was performed for respiratory rate because the difference resulted directly from the experimental manipulation. The absence of intergroup differences in blinding assessment indicated successful group blinding.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGroup comparison of demographic and training-related data.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eGroups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHRVBF\u003c/p\u003e \u003cp\u003e (n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl \u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eA. Demographic data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.0 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.9 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165.4 (7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e163.3 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.3 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.0 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.9 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.9 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEHI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92.3 (19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.2 (7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eB. Training-related data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory rate (breaths per minute)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.5 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.7 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage coherence score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.3 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAS score for blinding assessment (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.3 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.6 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eBMI, body mass index; EHI, Edinburgh Handedness Inventory; HRVBF, heart rate variability biofeedback; SD, standard deviation; VAS, visual analog scale.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eBreath training effects\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results of the 2 \u0026times; 2 mixed-design ANOVA. Regarding vmHRV indices, the ANOVA revealed a significant main effect of time on RMSSD (\u003cem\u003eF\u003c/em\u003e (1, 49)\u0026thinsp;=\u0026thinsp;5.551, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023, \u003cem\u003eη\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.102), indicating that RMSSD increased in both the HRVBF and control groups from pre- to post-training. For ERPs, significant main effects of time were observed for N2 Go (\u003cem\u003eF\u003c/em\u003e (1, 49)\u0026thinsp;=\u0026thinsp;7.433, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009, \u003cem\u003eη\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.132) and N2 No-Go (\u003cem\u003eF\u003c/em\u003e (1, 49)\u0026thinsp;=\u0026thinsp;5.827, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020, \u003cem\u003eη\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.106), showing that N2 Go amplitude increased and N2 No-Go amplitude decreased across both groups following training. For vigilance indicators, significant main effects of time were found for mRT (\u003cem\u003eF\u003c/em\u003e (1, 49)\u0026thinsp;=\u0026thinsp;14.346, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003eη\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.226) and CER (\u003cem\u003eF\u003c/em\u003e (1, 49)\u0026thinsp;=\u0026thinsp;13.652, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, \u003cem\u003eη\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.218), indicating decreased mRT and increased CER in both groups from pre- to post-training. Significant main effects of group and time were observed for task-related arousal (\u003cem\u003eF\u003c/em\u003e (1, 49)\u0026thinsp;=\u0026thinsp;6.658, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013, \u003cem\u003eη\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.120) and fatigue (\u003cem\u003eF\u003c/em\u003e (1, 49)\u0026thinsp;=\u0026thinsp;13.269, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, \u003cem\u003eη\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.213), respectively. These results indicate that task-related arousal was higher in the HRVBF group compared with the control group, whereas task-related fatigue increased over time in both groups. The KSS-J score (\u003cem\u003eF\u003c/em\u003e (1, 49)\u0026thinsp;=\u0026thinsp;17.174, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003eη\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.260) showed a significant main effect of time, indicating decreased subjective arousal in both groups. However, no significant group \u0026times; time interactions were observed for any variable (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eANOVA results for physiological, behavioral, and subjective variables.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"17\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eGroups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"11\" nameend=\"c17\" namest=\"c7\"\u003e \u003cp\u003eSource of variation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eHRVBF (n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;26)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c17\" namest=\"c14\"\u003e \u003cp\u003eGroup \u0026times; Time\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e\u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003ePost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c10\" namest=\"c9\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eη\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c14\" namest=\"c13\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eη\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eη\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSSD (ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.8 (21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.9 (32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e43.5 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54.9 (31.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.023\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHF (ms\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1215.7 (982.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1299.4 (1703.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e904.5 (820.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1228.1 (1345.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABP (\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e \u0026micro;V\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.16 (4.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.63 (8.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e3.31 (2.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.47 (2.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2 Go (\u0026micro;V)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.44 (0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.98 (1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-1.02 (1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.88 (1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.009\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2 No-Go (\u0026micro;V)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.08 (2.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.30 (2.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-3.70 (2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.46 (1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.020\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP3 Go (\u0026micro;V)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.41 (1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.51 (1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.97 (1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.09 (1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP3 No-Go (\u0026micro;V)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.36 (2.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.43 (3.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e6.61 (2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.57 (2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emRT (ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e363.8 (44.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e356.1 (39.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e362.3 (38.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e346.0 (39.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e14.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRTCV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.15 (0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16 (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.16 (0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17 (0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCER (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.4 (16.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.3 (17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e27.3 (20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.2 (21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e13.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOER (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.6 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTask-related arousal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.8 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.8 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e6.0 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.0 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.013\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTask-related fatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.2 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.1 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e4.1 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.2 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e13.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKSS-J score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.0 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e4.2 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.1 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e17.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.000\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"17\"\u003eABP, alpha band power; CER, commission error rate; HF, high frequency; HRVBF, heart rate variability biofeedback; KSS-J, Japanese version of the Karolinska Sleepiness Scale; mRT, mean reaction time; OER, omission error rate; RMSSD, root mean square of successive differences; RTCV, reaction time coefficient of variation; SD, standard deviation.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"17\"\u003e\u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; \u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; \u003csup\u003e***\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRelationship between arousal and behavioral and physiological indicators\u003c/h2\u003e \u003cp\u003eThe multiple regression analysis conducted to identify the arousal indicator associated with behavioral and physiological outcomes showed that only the regression equation predicting ΔCER was significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045). The adjusted coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e) was 0.085. The standardized β coefficient for ΔABP was significantly negative (β = -0.273, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049). The variance inflation factor was 1.001 for all explanatory variables, indicating no evidence of multicollinearity. The Durbin\u0026ndash;Watson statistic was 1.483, indicating independence of residuals.\u003c/p\u003e \u003cp\u003eSubsequently, polynomial regression analyses were performed separately for each group, with ΔABP as the explanatory variable and ΔCER as the dependent variable (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In the HRVBF group, the quadratic regression model was significant (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.276, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029), whereas the linear (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.140, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.065) and cubic (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.303, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.052) models were not significant. In contrast, in the control group, none of the polynomial regression models (linear: R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.000, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.918; quadratic: R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.042, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.613; cubic: R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.076, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.621) reached statistical significance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe figure illustrates the polynomial regression of ΔCER on ΔABP in the HRVBF and control groups. Δ indicates the change from pre- to post-measurement. Statistical significance was observed only for the quadratic regression model in the HRVBF group (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.276, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029). The 95% confidence interval is displayed for the quadratic model only\u003c/p\u003e \u003cp\u003eABP, alpha band power; CER, commission error rate; HRVBF, heart rate variability biofeedback\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eUsing HRV, EEG, and the SART, this study investigated the effects of HRVBF on cortical arousal and vigilance, as well as the relationship between these variables, by testing two specific hypotheses. Contrary to the first hypothesis that HRVBF would modulate cortical arousal toward an optimal level and enhance vigilance, the findings did not demonstrate superior physiological or behavioral improvements compared with sham-paced breathing. Instead, both groups exhibited physiological relaxation and reduced inhibitory control, characterized by increased RMSSD and CER, together with decreased N2 No-Go amplitudes. However, the second hypothesis was supported: polynomial regression analyses revealed a significant quadratic relationship between ΔABP and ΔCER exclusively in the HRVBF group. This finding suggests that an inverted U-shaped relationship between arousal level and performance may underlie the variable efficacy of HRVBF training.\u003c/p\u003e \u003cp\u003eContrary to the initial prediction, the HRVBF group did not demonstrate significant effects relative to the control group, and both groups showed comparable relaxation responses. The increase in RMSSD observed in both groups likely reflects vagal activation induced by paced breathing itself, independent of HRV feedback (Laborde et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). Consistent with this physiological relaxation, decreased mRT and increased CER, together with reduced N2 No-Go amplitudes, collectively indicate diminished inhibitory control over time, potentially driven by reduced vigilance. Previous studies using the SART or ERP paradigms have reported similar patterns (Einziger et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wilson et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and the increases in task-related fatigue and KSS-J scores further support this interpretation, indicating that both groups experienced a comparable decline in vigilance following training. These findings regarding HRVBF effects may reflect the complex dynamics observed in single-session HRVBF interventions. Although a single-session HRVBF has been shown to promote physiological and subjective relaxation, as evidenced by increases in HRV (Lin et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), EEG changes (Prinsloo et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and self-reported relaxation (Blum et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mazgelytė et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), its acute effects on cognitive function remain mixed (Bahameish \u0026amp; Stockman, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Blaser et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; J. Lee \u0026amp; Finkelstein, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Prinsloo et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Sherlin et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Several previous studies have demonstrated beneficial effects under specific conditions, particularly during high-stress contexts (Blaser et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Prinsloo et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Sherlin et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Even in multi-session HRVBF interventions, one study has suggested no significant improvement in cognitive function (Nashiro et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), whereas other studies have reported positive effects (Jester et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sutarto et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Therefore, in single-session HRVBF, relaxation may represent the dominant outcome, potentially inducing a state of relative hypo-arousal. Although such a state may be advantageous for stress reduction, it may also mask potential cognitive benefits of HRVBF or even reduce vigilance during a monotonous task such as the SART in the present study. Future research should adopt longitudinal designs to determine whether repeated HRVBF training produces arousal-regulation effects that predominate over relaxation effects and whether such training improves vigilance indicators under conditions of high stress or cognitive load.\u003c/p\u003e \u003cp\u003eOur analysis indicated a significant quadratic relationship between ΔABP and ΔCER exclusively in the HRVBF group. This inverted U-shaped relationship indicates that both positive and negative changes in ABP were associated with decreases in CER (i.e., enhanced vigilance), whereas minimal or no change in ABP was associated with increases in CER (i.e., reduced vigilance). These findings suggest that the efficacy of HRVBF may be associated with shifting cortical arousal towards an optimal functional range. A recent theoretical model has posited that higher vmHRV serves as an indicator of the capacity to flexibly allocate cognitive resources to meet situational demands (Laborde et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e; Magnon et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In parallel with vmHRV changes, relatively stable alpha oscillatory activity may reflect ineffective arousal regulation associated with diminished vigilance, whereas dynamic modulation of alpha oscillations may indicate adaptive arousal regulation, characterized by active re-engagement of attention and flexible allocation of neural resources. This interpretation aligns with recent EEG research suggesting that dynamic alpha oscillations are critical for sustaining attention (Li et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Peylo et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, the absence of this quadratic relationship in the control group suggests that HRVBF training, unlike paced breathing alone, may engage a distinct regulatory mechanism that facilitates optimization of arousal states and vigilance performance. Mechanistically, this effect may involve activation of vagal afferent pathways projecting to the locus coeruleus in the brainstem. The locus coeruleus-noradrenaline system functions as a central regulator of arousal and neural gain, and its activity is closely linked to attentional control and vigilance (Torres et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). It has been proposed that increased vmHRV enhances vagal signals to the locus coeruleus, thereby optimizing cortical excitability and gain modulation (Duschek et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Mather \u0026amp; Thayer, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Consequently, this bottom-up regulatory influence may strengthen the coupling between cortical arousal and vigilance performance. From a practical perspective, the observed quadratic relationship suggests that individual variability in arousal responses to HRVBF training may contribute to heterogeneity in training efficacy. Although these findings are limited to healthy young adults and a single training session, it is possible that those changes in alpha oscillatory dynamics could serve as indicators for identifying individuals who are more likely to benefit from HRVBF. Overall, these findings suggest that while HRVBF does not uniformly produce superior effects compared to paced breathing, it uniquely demonstrates a non-linear relationship between cortical arousal and vigilance. This insight may inform future intervention strategies aimed at shifting individual arousal states towards optimal ranges.\u003c/p\u003e \u003cp\u003eCertain limitations should be acknowledged in this study. First, the multiple regression analysis identified CER as the only significant dependent variable predicted by arousal indicators, which limits the generalizability of the findings to other aspects of vigilance. The absence of significant associations with mRT, RTCV, OER, and ERP components suggests that the arousal\u0026ndash;vigilance relationship may be specific to inhibitory control processes indexed by CER. Second, although the SART is well established for assessing sustained attention, it may not fully capture the multidimensional nature of vigilance. Alternative behavioral paradigms, such as the psychomotor vigilance task or continuous performance task under varying cognitive loads, may reveal different arousal\u0026ndash;vigilance relationships. Third, participants consisted exclusively of healthy young adults. Abnormalities in arousal regulation are known to occur in older adults (T.-H. Lee et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and in clinical populations, including individuals with depression (Xie et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), anxiety disorders (Florido et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and attention deficit/hyperactivity disorder (Strau\u0026szlig; et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Therefore, the observed arousal\u0026ndash;vigilance relationship may differ in older or clinical populations characterized by atypical arousal profiles, which limits the generalizability of the present findings. Future research should examine optimized arousal regulation and the persistence of the inverted U-shaped relationship by recruiting diverse cohorts and implementing longitudinal HRVBF protocols combined with varied vigilance tasks.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study investigated the effects of HRVBF on cortical arousal and vigilance, as well as the relationship between these constructs, using multimodal physiological and behavioral measurements. HRVBF did not demonstrate superior improvements in vigilance compared with sham-paced breathing. The results suggest that relaxation effects may predominate during a single session of HRVBF. Polynomial regression analyses indicated a significant quadratic relationship between changes in cortical arousal and vigilance exclusively in the HRVBF group. This inverted U-shaped relationship suggests that HRVBF may facilitate arousal regulation toward optimal levels, potentially explaining the variable efficacy observed in previous studies. These findings highlight the importance of considering non-linear arousal\u0026ndash;vigilance relationships when evaluating HRVBF interventions. Future research should employ longitudinal designs with diverse populations to examine the persistence of the relationship between optimal arousal regulation and subsequent cognitive benefits.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Declaration of Helsinki and its later amendments. This study was approved by the Ethics Committee of the Faculty of Health Sciences, Hokkaido University (approval number: 25-37).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Japan Society for the Promotion of Science Grant-in-Aid for Scientific Research (JSPS KAKENHI Grant Number 24K23754).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRyuji Saito\u003c/strong\u003e: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing \u0026ndash; original draft, Visualization, Project administration, Funding acquisition. \u003cstrong\u003eKazuki Yoshida\u003c/strong\u003e: Conceptualization, Methodology, Validation, Writing \u0026ndash; review and editing. \u003cstrong\u003eDaisuke Sawamura\u003c/strong\u003e: Validation, Resources, Writing \u0026ndash; review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict-of-Interest Statement \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhumada-M\u0026eacute;ndez, F., Lucero, B., Avenanti, A., Saracini, C., Mu\u0026ntilde;oz-Quezada, M. 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ERP components activated by the Go! and WITHHOLD! conflict in the random Sustained Attention to Response Task. \u003cem\u003eBrain and Cognition\u003c/em\u003e, \u003cem\u003e66\u003c/em\u003e(1), 57\u0026ndash;64. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bandc.2007.05.005\u003c/span\u003e\u003cspan address=\"10.1016/j.bandc.2007.05.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"applied-psychophysiology-and-biofeedback","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"apbi","sideBox":"Learn more about [Applied Psychophysiology and Biofeedback](http://link.springer.com/journal/10484)","snPcode":"10484","submissionUrl":"https://submission.nature.com/new-submission/10484/3","title":"Applied Psychophysiology and Biofeedback","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"heart rate variability biofeedback, EEG, cortical arousal, vigilance, polynomial regression","lastPublishedDoi":"10.21203/rs.3.rs-8939605/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8939605/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHeart rate variability biofeedback (HRVBF) has been investigated as a breathing-based intervention to enhance cognitive function, yet its efficacy varies across studies. Understanding the mechanisms underlying this variability is essential for optimizing HRVBF protocols. This study examined the effects of HRVBF and the relationship between cortical arousal and vigilance using multimodal assessments. Fifty-one healthy young adults were randomly assigned to HRVBF (n = 25) or an active control group (n = 26). Participants completed 15-min HRVBF or control breathing with pre- and post-assessments of physiological (heart rate variability, electroencephalography), behavioral (sustained attention to response task), and subjective arousal measures. The results indicated that the HRVBF group had no significant effects on any outcomes compared to the control group. Both groups showed increased root mean square of successive differences, N2 event-related potential Go amplitudes, commission error rates, and subjective arousal, along with decreased N2 No-Go amplitudes and reaction times. Polynomial regression revealed a significant quadratic relationship between changes in alpha band power and commission error rates in the HRVBF group (R² = 0.276, \u003cem\u003ep\u003c/em\u003e = 0.029), whereas no significant relationship was found in controls. Although HRVBF did not outperform control breathing, the quadratic association between cortical arousal and vigilance suggests that HRVBF may regulate arousal toward optimal levels. This inverted U-shaped pattern may help explain variability in HRVBF efficacy and highlights the importance of considering non-linear arousal–vigilance dynamics in future research\u003c/p\u003e\n\u003cp\u003eTrial Registration: UMIN-CTR UMIN000058098 (June 5, 2025).\u003c/p\u003e\n\u003cp\u003ehttps://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000066395\u003c/p\u003e","manuscriptTitle":"The effect of heart rate variability biofeedback on cortical arousal and its subsequent influence on vigilance: a randomized controlled study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-08 14:32:03","doi":"10.21203/rs.3.rs-8939605/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-29T14:59:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-27T17:52:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-26T11:24:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"236618465589463459557507609455387807064","date":"2026-02-27T22:43:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"131103293460115118677407476250113655835","date":"2026-02-27T17:21:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-25T18:52:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-25T07:07:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-25T07:05:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Applied Psychophysiology and Biofeedback","date":"2026-02-22T13:57:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"applied-psychophysiology-and-biofeedback","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"apbi","sideBox":"Learn more about [Applied Psychophysiology and Biofeedback](http://link.springer.com/journal/10484)","snPcode":"10484","submissionUrl":"https://submission.nature.com/new-submission/10484/3","title":"Applied Psychophysiology and Biofeedback","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a2d00eba-ad9f-4d63-97c9-05991ac1efb0","owner":[],"postedDate":"March 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T11:53:36+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-08 14:32:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8939605","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8939605","identity":"rs-8939605","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}