{"paper_id":"39defa82-55d8-4e69-b126-fa93a8c3ffd4","body_text":"Neurophysiological Effects of Gayatri Mantra Meditation on Emotional Processing: An EEG-ERP 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 Neurophysiological Effects of Gayatri Mantra Meditation on Emotional Processing: An EEG-ERP Study Nitesh Sharma, Dushyant Soni, Manvi Jain, Jyoti Kumar, Rahul Garg This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6932735/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study has studied the impact of Gayatri Mantra (GM) meditation on emotional processing using event-related potentials (ERPs) and self-report measures. Twenty-four healthy male participants (M = 32.37, SD = 8.02 years) voluntarily participated in this study. Based on their self-reported daily routine practices, participants were categorized into two groups: meditation practitioners (n = 12) and non-practitioners (n = 12), who did not engage in any mind-body practices. All participants completed standardized psychometric assessments, including the Emotional Competence Scale, Emotion Regulation Questionnaire, Anashakti (non-attachment) Scale, and Brief Resilience Scale. EEG-ERP data were recorded using a 64-channel EEG during passive viewing of 120 affective images (40 positive, 40 negative, 40 neutral) from the International Affective Picture System (IAPS) repository. Results revealed significantly reduced ERP-Late Positive Potential (LPP) amplitudes in practitioners across mid, right and left-centroparietal sites, with the most significant reduction at CPz in response to negative stimuli (p = 0.043) and near significance at right centroparietal regions (p = 0.073). Self-report data revealed that practitioners demonstrated significantly greater Emotional Competence (p = 0.003) and Anasakti (non-attachment) (p = 0.043), along with higher scores on emotion regulation and resilience, suggesting a consistent trend toward enhanced emotional well-being. These findings suggest that sustained long-term GM meditation practice is associated with enhanced emotional regulation, reduced neural reactivity to affective stimuli, and improved emotional wellbeing. The combined neurophysiological and psychological evidence underscores the potential of GM meditation in cultivating emotional resilience and affective balance for overall wellbeing. Cognitive Neuroscience Psychology Gayatri Mantra Meditation Emotion Regulation Wellbeing EEG Event Related Potential Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Emotion regulation is a fundamental psychological process that enables individuals to modulate their emotional responses to internal and external stimuli, facilitating adaptive functioning and maintaining psychological wellbeing (Gross, 1998 ). Dysregulation of emotional processing, characterized by heightened emotional reactivity or difficulties in managing emotions, and has been implicated in various psychopathological conditions, including anxiety, depression, borderline personality disorder, and post-traumatic stress disorder (PTSD) (Gross & Muñoz, 1995 ; Aldao et al., 2010 ). Further, it has been reported that impaired emotion regulation has been linked to increased vulnerability to stress, cognitive dysfunction, and disruptions in social interactions (Sheppes et al., 2015 ). These consequences highlight the need for research on self-management interventions that have the potential to strengthen emotional regulation abilities leading towards enhancement of emotional balance and wellbeing. Therefore, research on emotion regulation has grown substantially over the past few decades, with growing attention to evidence-based approaches aimed at enhancing emotional regulation, particularly through mindfulness-based practices (Raugh et al., 2025 ; Hoge et al., 2021 ). Neurophysiological Marker in Emotion Regulation Research With advancements in neurophysiological tools such as electroencephalography (EEG), and functional magnetic resonance imaging (fMRI), deeper insights into the neural mechanisms underlying emotional processing have become possible to explore (Richter et al., 2024 ). Event-related potentials (ERPs) are one of the neurophysiological methods in EEG data analysis that have the potential to assess neural responses to stimuli. Late Positive Potential (LPP) are one of the ERP measures that have been reported to be associated with sustained attention and emotional processing (MacNamara et al., 2022 ). The LPP typically emerges around 400 milliseconds after stimulus onset and sustains throughout stimulus presentation (Hajcak et al., 2010 ). Enhanced LPP amplitudes are observed in response to emotionally arousing stimuli, reflecting increased emotional engagement, while reduced LPP amplitudes indicate diminished emotional reactivity (Hajcak et al., 2010 ). This modulation of the LPP has been reported to be linked to both automatic and controlled processing of emotional stimuli, making it a valuable marker for studying emotion regulation (MacNamara et al., 2022 ; Hajcak et al., 2010 ). Modulation of emotional processing through meditation Among the various methods for enhancing emotional regulation, meditation has emerged as one of the most promising interventions. Meditation has been reported to modulate neural mechanisms associated with affective processing (Tang et al., 2015 ). Studies using neurophysiological techniques, such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), have demonstrated that meditation can influence brain activity patterns associated with emotional regulation, leading to enhanced psychological wellbeing (Cahn & Polich, 2013 ). Research has investigated the effects of mindfulness meditation on emotional processing, focusing on its influence on ERP components. For instance, Sobolewski et al. ( 2011 ) observed that long-term mindfulness practitioners exhibited reduced LPP amplitudes in response to negative stimuli, suggesting decreased emotional reactivity in long-term mindfulness practitioners. Similarly, Deng et al. ( 2019 ) found that mindfulness induction in adolescents led to reduced P1 and LPP amplitudes for negative stimuli. Zhang et al. ( 2019 ) has reported that breath-focused mindfulness (BFM) reduces affective modulation of early ERP components (P1, N2) and the late positive potential (LPP) for both pleasant and negative images, suggesting BFM's potential to serve as an effective strategy for modulating neural response to affective stimuli. Further, Brown et al. ( 2013 ) and Lin et al. ( 2016 ) found that higher levels of dispositional mindfulness were associated with attenuated LPP responses to unpleasant stimuli, highlighting mindfulness's role in modulating affective processing. While these studies have demonstrated that mindfulness meditation practices can modulate neural mechanisms underlying emotion regulation, there are some studies in the literature that present mixed findings. For instance, Egan et al. ( 2018 ) found that task-induced mindfulness heightened LPP amplitudes for both positive and negative stimuli, suggesting increased motivational relevance rather than reduced reactivity. Similarly, Eddy et al. ( 2015 ) observed comparable differences in P300 or LPP responses between mindfulness and control sessions during picture-viewing tasks. Apart from mindfulness, other meditation approaches have also been examined for their effects on emotion processing using the EEG-ERP method. Zhang et al. ( 2022 ) explored ERP responses during Om chanting but found no significant changes in P1 and LPP amplitudes when participants viewed unpleasant images while chanting the sound of ‘Om’ compared to passive viewing. In contrast, Hao et al. ( 2023 ) demonstrated that groups practicing imagination-based loving-kindness meditation (ibLKM) and focused attention meditation (FAM) exhibited significantly higher LPP amplitudes over the central-parietal region in response to painful stimuli compared to a passive control group. While these findings indicate that meditation can influence neural mechanisms underlying emotion regulation, the heterogeneity of results emphasizes the need for further research into the underlying mechanisms across different meditation styles. Given the diversity of meditation approaches, it is crucial to explore other meditation types, especially mantra meditation, which is one of the widely practiced meditation in Eastern traditions for a long time (Acharya, 2000 ). It involves the rhythmic repetition of sacred words or phrases often in Sanskrit to achieve greater awareness (Lynch et al., 2018; Parthasarathi, 2020 ) and has been reported to be associated with improved mental health and stress reduction (Álvarez-Pérez et al., 2022; Lynch et al., 2018; Tseng, 2022 ). Mantras have been an integral part of traditional Indian literature and among the traditional Vedic mantras, one of the most popularly chanted mantras for thousands of years has been the Gayatri Mantra (GM) (Acharya, 2000 ). Gayatri Mantra The Gayatri Mantra (GM) has been argued to be one of the most significant hymns of the Vedas, and has been an integral part of daily practices (sadhana) across various cultures and traditions in the Indian subcontinent for centuries (Acharya, 2003 ). The ancient Indian scripture, Atharvaveda (19-1-71), describes GM as a practice that promotes longevity, wellbeing, and ‘divine brilliance’ (Acharya, 2003 ). Contemporary studies have also reported association of GM practices with improved well-being and quality of life (Thrisna-Dewi et al., 2020 ), reduction in anxiety (Sudha, 2020 ; Ketut-Candrawati et al., 2018 ), and stress (Sharma et al., 2024 ; Sharma and Singh, 2014 ), and improvements in EEG brain waves associated with cognitive function (Thomas and Rao, 2016 ). Despite its widespread practice and cultural significance, neurophysiological research on GM meditation, particularly in the context of emotional processing, remains limited. Therefore, to address this gap in the literature, the present study aimed to investigate the influence of GM meditation on visual ERPs elicited by emotionally arousing stimuli, with a specific focus on modulations in the LPP components. Unlike studies that capture transient state effects during meditation, this study sought to examine enduring trait-related neural changes, reflecting the long-term reorganization of emotional processing associated with sustained GM practice. It was hypothesized that individuals practicing GM meditation would exhibit reduced emotional reactivity, as indexed by lower LPP amplitudes, compared to non-practitioners. Materials and methods Participants The study included 24 healthy male participants from Delhi, aged 25 to 50 years (Mean ± SD = 32.37 ± 8.02), with no history of psychological illness, substance abuse, or intoxication. All participants had normal or corrected-to-normal vision. The participants' educational qualifications varied from ‘High school’ to ‘Postgraduate and above’ levels. Out of 24 participants, 12 reported practicing the Gayatri Mantra on a daily basis, they were classified as practitioners. The remaining participants (n = 12) who did not engage in any Gayatri Mantra practices, not any other mind-body practices and were categorized as non-practitioners. The two groups of practitioners and non-practitioners did not significantly differ in age (p = 0.79), gender, education, or place of residence. Their demographic detail is presented in Table 1 . Participation in this study was voluntary and an informed signed consent was obtained from each participant before data collection. The Ethical approval for the study was obtained from the Ethics Committee of the Indian Institute of Technology, Delhi (No. P021/P074). Table 1 Demographic details of participants S.N Participants characteristics Number of participants (N = 24) 1 Age range 25–50 years 2 Mean age in years (M ± SD) 32.37 ± 8.02 years 3 Gender M = 24, F = 0 4 Profession Students = 17 Employed = 7 5 Educational qualification High School = 1 Senior High School = 1 Graduation = 3 Post Graduation & above = 19 6 Years of experience of GM practices Average = 7.90 years (for N = 12) Beginners ( 1 to 4 years ) = 5 Mid-term practitioners = 3 ( 5 to 9 years ) Senior practitioners = 4 ( 10 years and above) 7 Daily GM practice minutes 10 minutes/day = 9 practitioners 30 minutes/day = 3 practitioners Experimental Procedure The experiment was conducted in a designated EEG recording room with a quiet and isolated environment. Before the beginning of the experiment, participants provided signed consent and completed a demographic questionnaire. They were also screened using a WHO self-report questionnaire to assess mental health issues, including depression, anxiety, and somatoform disorders. All the participants were assessed to be sound and healthy. Additionally, participants completed standard psychometric scales, including the Emotion Regulation Questionnaire (Gross & John, 2003 ), the Emotional Competence Scale (Sharma & Bharadwaj, 2016), the Anashakti (non-attachment) Scale (Singh & Raina, 2015 ), and the Brief Resilience Scale (Smith et al., 2008 ). Neurophysiological assessment was conducted using a Brain-Amp 64-channel EEG (Brain Products GmbH, Germany) with an R-NET cap, following the 10–20 electrode system. The ERP-Late Positive Potential was recorded by a Brain Vision recorder during the presentation of visual emotional stimuli on a computer screen. Stimulus presentation A total of 120 affective pictures from the International Affective Picture System (IAPS; Lang et al., 2005 ) repository were presented in a random order using E-Prime 3.0 software (Psychology Software Tools, Inc) on a 22-inch computer screen kept at a distance of 75 cm from participants. The presented stimulus consisted of 40 positive, 40 negative, and 40 neutral valence pictures. The pictures were chosen based on valence and arousal as per their original data source. The mean and standard deviation of the selected negative pictures were ( M-valence = 2.43, SD-valence = 0.81; M-arousal = 6.26, SD-arousal = 0.68), for positive pictures ( M-valence = 7.10 SD-valence = 0.51; M-arousal = 4.18, SD-arousal = 0.71) and neutral pictures ( M-valence = 5.03, SD-valence = 0.37; M-arousal = 3.06, SD-arousal = 0.79). Each stimulus was presented for 2.5 seconds, followed by a blank screen for 2 seconds and fixation “+” for 1 second. Participants were instructed to observe the stimulus passively and experience the emotions naturally. Analysis of Data from Psychological Scales Self-reported data from standard psychometric scales were analyzed using independent sample t-tests to compare the mean scores of practitioners and non-practitioners on emotion regulation, emotional competence, anasakti (non-attachment), and resilience. The effect size for the group mean differences was calculated using Cohen’s d, with the significance level set at 0.05. The analysis revealed that the practitioners group had higher mean scores on all scales compared to the non-practitioners group. There was a significant group difference on the Emotional Competence scale (p < 0.003, d = 1.348) and the Anasakti (non-attachment) scale (p = 0.043, d = 0.879). However, differences in mean scores on Emotion Regulation Scale (p = 0.18, d = 0.606) and Resilience scale (p = 0.17, d = 0.624) were comparable, though the mean scores for Emotion Regulation (46.92 vs. 41.42) and Resilience (21.25 vs. 19.00) were higher in the practitioners group compared to the non-practitioners group (see Table 2 ). Table 2 Comparison between emotion related variables in practitioners and non-practitioners. Scales and factors Non-Practitioners Practitioners t-value p-value Point estimate of Cohen's d Mean ± SD Mean ± SD Overall Emotional Competence (EC) 90.42 ± 13.92 109.25 ± 14.03 -3.301 0.003 -1.348 EC-ADF 17.00 ± 2.76 20.83 ± 4.44 -2.536 0.019 -1.035 EC-AECE 18.17 ± 3.51 21.42 ± 2.71 -2.537 0.019 -1.036 EC-AFE 16.83 ± 2.85 21.58 ± 3.77 -3.475 0.002 -1.419 EC-ACPE 17.67 ± 3.34 22.00 ± 2.79 -3.447 0.002 -1.407 EC-AEPE 20.75 ± 4.16 23.42 ± 3.29 -1.742 0.095 -0.711 Anasakti (non-attachment) 74.92 ± 6.27 80.08 ± 5.45 -2.153 0.043 -0.879 Overall Emotion-Regulation (ER) 41.42 ± 9.09 46.92 ± 9.06 -1.485 0.152 -0.606 ER-Cognitive reappraisal 26.17 ± 5.81 29.92 ± 6.06 -1.546 0.136 -0.631 ER-Expressive suppression 15.25 ± 4.99 17.00 ± 4.26 -0.923 0.366 -0.618 Resilience (BRS) 19.00 ± 3.95 21.25 ± 3.22 -1.528 0.141 -0.624 Note: ADF = Adequate depth of feeling, AECE = Adequate expression and control of emotions, AFE = Ability to function with emotions, ACPE = Ability to cope with problem emotions, AEPE = Ability to enhance positive emotions. These are sub-factors of Emotional Competence (EC). ERP Analysis EEG data were analyzed using Brain Vision Analyzer 2.2 software (Brain Products GmbH, Germany). Recordings were down-sampled to 256 Hz and bandpass filtered between 0.5 and 30 Hz. Eye blinks were corrected using an ocular correction algorithm, and data were re-referenced to the standard average. Artifact rejection was performed using semi-automatic inspection, removing amplitudes exceeding ± 100 µV. ERP segments were created for positive, negative, and neutral stimuli, with epochs extracted from 500 ms before to 2000 ms after the event, and baseline correction was applied from − 200 ms to the start of the ‘event’ of stimuli presentation. LPP waveforms were computed in the centro-parietal regions, including mid-centroparietal (CPz), the right centro-parietal regions (CP2, CP4, and CP6), and the left centro-parietal regions (CP1, CP3, and CP5). The average ERP for each stimulus type (positive, negative, and neutral) was computed for each participant in both the practitioner and non-practitioner groups. These averaged ERPs were then compared statistically. Statistical analysis of ERP data Statistical analysis was performed using SPSS software version 28.0. The Shapiro-Wilk tests indicated that the data was normally distributed (p > 0.05). Additionally, Mauchly’s test confirmed the assumption of sphericity for both groups (p > 0.05). As a result, repeated measures of ANOVA were conducted to assess the main effect of stimulus valence (positive, negative, neutral) on ERP-LPP amplitudes within the LPP time window of 450–750 ms in centro-parietal regions for both groups. Results Mid-Centroparietal region (CPz), LPP (450–750 ms) The results from the repeated measures ANOVA showed significant main effects of stimulus valence in LPP (450–750 ms) for non-practitioners (F(2,22) = 16.269, p < 0.001, η² = 0.597) and also for practitioners (F(2,22) = 3.934, p = 0.035, η² = 0.263). Post-hoc pairwise stimulus comparison in the non-practitioners group showed that there was a significant difference in elicited LPP amplitudes between neutral and negative stimuli (p = 0.001), positive and negative stimuli (p = 0.007), but no significant difference between positive and neutral stimuli (p = 0.207). Similarly, in the practitioners’ group, significant LPP amplitude differences were found between neutral and negative stimuli (p = 0.037), but no significant differences were seen between other pairs of stimuli (all p > 0.05). The LPP amplitude of the negative stimuli was higher than the positive and neutral stimuli in both groups (see Table 3 ). Furthermore, an analysis comparing practitioners and non-practitioners’ groups using an independent sample t-test revealed that the practitioners group exhibited a reduced LPP amplitude at the mid-centroparietal electrode (CPz) for positive, negative, and neutral stimuli compared to non-practitioners. Additionally, there was a significant difference in the LPP amplitude for negative stimuli between practitioners and non-practitioners (p = 0.043) within the LPP window (450–750 ms), the practitioners group had lower LPP amplitude than non-practitioners as illustrated in Table 3 and Fig. 1 . Table 3 LPP Amplitude difference between two groups on stimulus valence in the Mid-Centroparietal region Time window Stimulus type Non-practitioners M ± SD (µV) Practitioners M ± SD (µV) t-test p-value Mid-Centroparietal (CPz) LPP (450–750 ms) Positive 0.987 ± 0.686 0.509 ± 1.724 0.892 0.382 Neutral 0.400 ± 0.857 -0.181 ± 1.421 1.216 0.237 Negative 1.930 ± 1.071 0.838 ± 1.394 2.152 0.043 Right-Centroparietal-(CP2, CP4, CP6), LPP (450–750 ms) In the Right-Centroparietal region, the RM-ANOVA result showed significant main effects of stimulus valence in the LPP window (450–750 ms) for non-practitioners (F(2,22) = 13.400, p = 0.000, η² = 0.549) and not for practitioners (F(2,22) = 2.119, p = 0.144, η² = 0.162). Pairwise stimulus comparison in the non-practitioners group showed that there was a significant LPP amplitude difference between neutral and negative stimuli (p = 0.001), positive and negative stimuli (p = 0.026), but no significant difference between positive and neutral stimuli (p = 0.241). However, in the practitioners group, no significant differences were found between any pair of stimuli (all p > 0.05). The LPP amplitude of the negative stimuli was higher than the positive and neutral stimuli in both groups (see Table 4 ). The results of the between-group analysis (practitioners vs. non-practitioners) demonstrated that LPP amplitudes for all stimuli (positive, negative, and neutral) were higher in non-practitioners than practitioners. There was no significant difference between the LPP amplitudes of positive, neutral, and negative stimuli between practitioners and non-practitioners (at 0.05, 95% CI). However, a trend toward significant difference in negative stimuli between practitioners and non-practitioners was seen (p = 0.07) at p < 0.1, 90% CI, the larger sample size could have substantiated these significant differences in LPP amplitude. Overall results showed that the practitioners exhibited a reduced LPP amplitude in the right centroparietal region for positive, negative, and neutral stimuli compared to non-practitioners, as illustrated in Table 4 and Fig. 2 . Table 4 LPP Amplitude difference between two groups on stimulus valence in the Right-Centroparietal region. Time window Stimulus type Non-practitioners M ± SD (µV) Practitioners M ± SD (µV) t-test p-value Right-Centroparietal (CP2, CP4, CP6) LPP (450–750 ms) Positive 1.074 ± 0.939 0.578 ± 1.044 1.223 0.234 Neutral 0.584 ± 0.794 0.160 ± 1.609 0.818 0.422 Negative 1.871 ± 1.028 0.863 ± 1.550 1.878 0.073 (p < 0.1) Left-Centroparietal-(CP1, CP3, CP5), LPP (450–750 ms) RM-ANOVA result showed no significant main effects of stimulus valence in the LPP window (450–750 ms) in the left-centroparietal region for non-practitioners (F (2,22) = 2.332, p = 0.121, η² = 0.175) but significant for practitioners (F (2,22) = 3.914, p = 0.035, η² = 0.262). Pairwise stimulus comparison in the non-practitioners group showed no significant difference between any pair of stimuli (all p > 0.05). Similarly, in the practitioners’ group, pairwise comparison did not show significant differences between any pair of stimuli. The LPP amplitude of the negative stimuli was higher than positive and neutral stimuli in both groups. The results of the between-group analysis (practitioners vs. non-practitioners) demonstrated that practitioners exhibited a reduced LPP amplitude in the Left centroparietal region for positive, negative, and neutral stimuli compared to non-practitioners, as presented in Table 5 and Fig. 3 . However, there were no significant differences in LPP amplitude for positive, neutral, and negative stimuli between practitioners and non-practitioners. Table 5 LPP Amplitude difference between two groups on stimulus valence in the Left-Centroparietal region. Time window Stimulus type Non-practitioners M ± SD (µV) Practitioners M ± SD (µV) t-test p-value Left Centroparietal (CP1, CP3, CP5) LPP (450–750 ms) Positive 0.683 ± 0.722 0.403 ± 1.228 0.681 0.503 Neutral 1.053 ± 0.929 1.035 ± 1.109 0.044 0.965 Negative 1.329 ± 0.885 1.148 ± 0.988 0.472 0.642 Associations between LPP amplitudes and emotional measures in practitioners and non-practitioners. Emotional processing was found to be more prominent in the Mid Centroparietal (MCP) and Right Centroparietal (RCP) regions, with negative emotional stimuli eliciting stronger LPP amplitudes compared to positive and neutral stimuli. Therefore, in light of this, a correlation analysis was conducted to examine the relationship between emotional processing and psychometric emotional traits. Pearson correlation coefficients were computed between LPP amplitudes (elicited by negative emotional stimuli) and scores on all four emotional scales: Emotional Competency, Anasakti (Non-Attachment), Resilience, and the Emotion Regulation Questionnaire. Initially, the analysis was performed for all participants collectively across both MCP and RCP regions. Subsequently, the sample was divided into two groups, practitioners and non-practitioners, and separate correlation analyses were conducted for each group in both MCP and RCP regions. First of all, correlation analysis was conducted for all participants to examine whether there is a consistent relationship between LPP amplitudes (elicited by negative emotional stimuli) and emotional traits across the entire sample. The results revealed a consistent pattern of negative correlations, indicating that higher scores on emotional traits were generally associated with lower LPP amplitudes, suggesting reduced neural reactivity to negative stimuli. Further, among the emotional variables, Emotional Competence showed a statistically significant negative correlation in both regions: MCP (r = –.508, p = .011) and RCP (r = –.597, p = .002), highlighting its strong association with attenuated neural responses. While other emotional variables also demonstrated negative correlations, they did not reach statistical significance. Specifically, Anasakti (non-attachment) showed MCP (r = –.387, p = .061) and RCP (r = –.221, p = .299); Resilience showed MCP (r = –.016, p = .943) and RCP (r = –.345, p = .099); and Emotion Regulation showed MCP (r = –.125, p = .559) and RCP (r = –.265, p = .211). These findings underscore the potential role of emotional traits, particularly emotional competence, in modulating neural responses to negative emotional stimuli, and suggest that the cultivation of such emotional traits by contemplative practices may contribute to more adaptive emotional regulation and psychological resilience. Subsequently, separate correlation analyses were conducted for the practitioner and non-practitioner groups in both the MCP and RCP regions to examine whether the practice of GM meditation influences emotional traits and neural responses to negative emotional stimuli. This subgroup analysis aimed to explore the potential modulatory effects of sustained GM meditative practice on emotional processing at the neural level. Mid Centroparietal (MCP) Region : In the Mid Centroparietal (MCP) region, practitioners exhibited a consistent pattern of negative correlations between LPP amplitudes to negative emotional stimuli and all four emotion-related psychological variables: emotional competence ( r = –.574, p = .051), anasakti or non-attachment ( r = –.490, p = .106), resilience ( r = –.227, p = .479), and emotion regulation ( r = –.198, p = .538). Although not all correlations had statistical significance, the direction and strength of these associations suggest a meaningful relationship. Specifically, higher levels of emotional competence and non-attachment among practitioners were associated with lower LPP amplitudes, indicating reduced neural reactivity to negative stimuli. Since the LPP is widely recognized as a neural marker of emotional salience and cognitive-affective engagement, this inverse relationship reflects greater emotional regulation capacity and reduced affective disturbance in response to negative stimuli among practitioners having higher psychological resources. The findings support the notion that long-term GM practice may cultivate emotional resilience and facilitate efficient downregulation of affective responses at a neural level. In contrast, non-practitioners demonstrated a divergent pattern. Correlations between LPP amplitudes and emotional variables in this group were generally weak and inconsistent: emotional competence ( r = –.088, p = .786), anasakti ( r = –.007, p = .982), resilience ( r = .519, p = .084) and emotion regulation ( r = .258, p = .419). Interestingly, the only moderate association observed was a positive correlation between resilience and LPP amplitude, suggesting that higher resilience in non-practitioners may coincide with increased neural engagement with negative stimuli. This could imply a reactive but effortful coping style rather than proactive emotional regulation. Moreover, the near-zero correlation with anasakti points to a lack of emotional detachment, which may contribute to heightened reactivity. Overall, the absence of significant negative correlations suggests that emotional traits in non-practitioners do not strongly buffer against neural responses to negative affect. This stark contrast with practitioners underscores the potential regulatory benefits of sustained GM meditation practice on emotional brain dynamics. Right Centroparietal (RCP) Region : In the Right Centroparietal (RCP) region, notable negative correlations between LPP amplitudes in response to negative stimuli and emotional constructs including: emotional competence ( r = –.523, p = .081), resilience ( r = –.365, p = .244), emotion regulation ( r = –.244, p = .445), and anasakti ( r = –.083, p = .799) were observed. The moderate negative correlation between emotional competence and LPP amplitudes reinforces the idea that practitioners with stronger emotional competence exhibit reduced neural reactivity to negative emotional content. This pattern, though not statistically significant, aligns with the broader trend seen in the MCP region indicating that higher emotional development in practitioners may be associated with reduced affective engagement in emotionally aroused contexts. These findings imply that right centroparietal regions, which are often implicated in attentional and emotional integration, may reflect enhanced cognitive-emotional control developed through sustained meditative practices like GM sadhana. In contrast, non-practitioners presented weak or inconsistent associations across the same emotional variables in the RCP region. Emotional competence ( r = –.500, p = .098) showed a moderate negative correlation, but emotion regulation ( r = –.074, p = .818), anasakti ( r = –.080, p = .806), and resilience ( r = –.159, p = .621) displayed weak and nonsignificant relationships. Interestingly, although the emotional competence–LPP link in non-practitioners mirrors the direction found in practitioners, the overall lack of consistent associations suggests a weaker integration between emotional traits and neural responses. This may reflect an absence of structured emotional regulation training, leading to less coherent or automatic downregulation of negative affect. Altogether, the RCP findings provide further support for the regulatory influence of GM meditation, where practitioners seem to engage more adaptive, less reactive emotional processing, as reflected in their neural findings. Discussion This study studied the impact of sustained GM meditation practices on emotional processing, with a specific focus on modulations in the ERP-LPP components elicited by emotionally arousing stimuli. The LPP has been reported to be a neural marker associated with sustained attention and evaluative processing of emotionally salient stimuli, typically exhibiting larger amplitudes in response to emotionally arousing content, reflecting heightened emotional arousal and increased attentional engagement (Cuthbert et al., 2000 ; Hajcak et al., 2010 ). These findings reveal that GM practitioners exhibited markedly reduced LPP amplitudes across mid-centroparietal, right-centroparietal, and left-centroparietal regions in response to positive, negative, and neutral stimuli compared to non-practitioners. Notably, a significant reduction was observed for negative stimuli at the mid-centroparietal site (CPz) (p < 0.05), and with a trend toward significance in the right-centroparietal region (p < 0.1). These results suggest that GM practice may be associated with the downregulation of neural responses to emotionally arousing stimuli, potentially reflecting reduced emotional reactivity or the use of cognitive reappraisal strategies to attenuate emotional responses. Additionally, self-report assessments showed that GM practitioners scored significantly higher than non-practitioners on standardized measures of emotional competence, emotion regulation, non-attachment (Anasakti), and resilience scale, reflecting greater emotional well-being among practitioners. Further, correlation analysis showed that practitioners of GM showed consistent negative correlations between LPP amplitudes and emotional measures, suggesting better emotional regulation and reduced neural reactivity to negative stimuli, unlike non-practitioners who showed weak or inconsistent patterns in correlation. These findings, combined with attenuated neural responses to emotionally arousing stimuli, suggest that sustained GM practice enhances emotional regulation and reduces emotional reactivity leading towards overall wellbeing. The observed lateralization of LPP reductions, particularly in the right-centroparietal region, aligns with existing literature implicating the right hemisphere in the processing of negative emotions (Davidson, 1995 ; Killgore & Yurgelun-Todd, 2007 ). This lateralization supports the notion that GM meditation may modulate right-hemispheric neural circuits involved in negative emotional processing, thereby contributing to improved emotional regulation. These findings are consistent with previous research on mantra-based meditation practices. For instance, Thomas and Rao ( 2016 ) reported fMRI findings that listening to the GM activated brain regions associated with attention and emotional processing, such as the bilateral superior temporal gyri and right insula, suggesting enhanced neural efficiency in processing emotional information. Similarly, studies on OM mantra meditation have demonstrated increased theta power post-meditation, indicative of reduced cortical arousal and a relaxed mental state, which may contribute to improved emotional regulation (Harne & Hiwale, 2018 ). Moreover, present results align with broader literature on mindfulness and meditation practices on emotional processing. Sobolewski et al. ( 2011 ) found that mindfulness meditators exhibited reduced LPP amplitudes in response to negative stimuli, suggesting diminished emotional reactivity. Brown et al. ( 2013 ) also reported that individuals with higher mindfulness showed smaller LPP amplitudes in response to negative images, indicating reduced emotional reactivity. Lin et al. (2020) further demonstrated that participants engaging in open monitoring meditation exhibited reduced LPP amplitudes in response to negative stimuli, reflecting enhanced emotional regulation induced by meditation practices. The findings of the current study support the notion that regular meditative practices can lead to neurophysiological changes associated with improved emotional regulation. The observed effects found in this study may be partially explained by the impact of mantra chanting on parasympathetic nervous system activation, which fosters a state of relaxation and contributes to mental and emotional wellbeing (Inbaraj et al., 2022 ). Notably, previous neuroimaging research indicates that chanting GM activates brain regions associated with attention and emotion regulation, including the bilateral superior temporal gyri and right insula (Thomas & Rao, 2016 ). These physiological findings are further supported by traditional literature, which describe GM as a powerful tool for promoting mental, emotional, and spiritual well-being (Acharya, 2000 ). According to the yogic perspectives the 24 syllables of the GM are believed to be intertwined in such a way that when chanted generates unique vibratory patterns that stimulate subtle glands and vital energy centers in the body, thereby inducing beneficial psychophysiological changes in mind (Acharya, 2000 ). Together, these perspectives reinforce the potential of sustained GM practice as a holistic intervention for enhancing emotional regulation and psychological health. Despite these promising findings, several limitations are hereby acknowledged in the present study. The cross-sectional design of the study precludes causal inferences regarding the effects of GM meditation on emotional processing. Additionally, the sample size, while adequate for detecting medium effect sizes, may have limited the statistical power to detect smaller effects, particularly in lateral regions. Future longitudinal studies with larger cohorts are warranted to confirm these findings and elucidate the temporal dynamics of neural changes associated with GM meditation. In conclusion, this study has attempted to provide neurophysiological evidence supporting the role of GM meditation in modulating emotional processing, as reflected by reduced LPP amplitudes to emotional stimuli. These findings contribute to the growing body of literature on the benefits of mantra-based meditation practices and underscore the potential of GM meditation as a tool for enhancing emotional regulation and psychological well-being. Declarations Disclosure of potential conflicts of interest: The authors declare that they have no conflicts of interest. They have no financial or non-financial interests that could influence the work reported in this paper. Ethics Approval and Consent to Participate Ethical approval for this study was obtained from the Research Ethics Committee of Indian Institute of Technology, Delhi, India (Ref No. P021/P074). All the participants gave consent for participation through signed documents. Funding Not Applicable. This research did not receive any grants from public, commercial, or nonprofit funding agencies. Data Availability The data are available from the corresponding author on reasonable request. Acknowledgements The authors sincerely acknowledge the support received from the staff and students of the NRCVEE Centre, IIT Delhi, during this study. We extend our special appreciation to Shri Surendra Singh Ji Rawat, Mrs. Niraj, and Ph.D students Mr. Vicky Kachera, Ms. Dimple Bhakti Patel, and Ms. Neha Shekhawat for their dedicated assistance in research conduction. References Acharya, P. S. R. S. (2000). Super science of Gayatri (3rd ed.). Mathura, India: Yug Nirman Yojana Press, Gayatri Tapobhumi. 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Annual Review of Clinical Psychology, 11 , 379–405. https://doi.org/10.1146/annurev-clinpsy-032814-112739 Singh, K., & Raina, M. (2015). Development and validation of a test on Anasakti (non-attachment): An Indian model of well-being. Mental Health, Religion and Culture, 18 , 715–725. https://doi.org/10.1080/13674676.2015.1084612 Smith, B. W., Dalen, J., Wiggins, K., Tooley, E., Christopher, P., & Bernard, J. (2008). The brief resilience scale: Assessing the ability to bounce back. International Journal of Behavioral Medicine, 15 , 194–200. https://doi.org/10.1080/10705500802222972 Sobolewski, A., Holt, E., Kublik, E., & Wróbel, A. (2011). Impact of meditation on emotional processing—a visual ERP study. Neuroscience Research, 71 (1), 44–48. Sudha, R. (2020). Gayathri mantra and social skills training for social anxiety, stress, self-concept, and well-being among school students with learning difficulties. International Journal of Psychosocial Rehabilitation, 24 (3), 1983–2004. https://doi.org/10.37200/IJPR/V24I3/PR200946 Tang, Y. Y., Hölzel, B., & Posner, M. (2015). The neuroscience of mindfulness meditation. Nature Reviews Neuroscience, 16 , 213–225. https://doi.org/10.1038/nrn3916 Thomas, S., & Rao, S. L. (2016). Effect of Gayatri mantra meditation on meditation-naive subjects: An EEG and fMRI pilot study. International Journal of Indian Psychology, 3 (2), 14–18. https://doi.org/10.25215/0302.114 Thrisna-Dewi, N. L. P. T., Arifin, M. T., & Ismail, S. (2020). The influence of Gayatri mantra and emotional freedom technique on quality of life of post-stroke patients. Journal of Multidisciplinary Healthcare, 13 , 909–916. https://doi.org/10.2147/JMDH.S266580 Tseng, A. A. (2022). Scientific evidence of health benefits by practicing mantra meditation: Narrative review. International Journal of Yoga, 15 (2), 89–95. https://doi.org/10.4103/ijoy.ijoy_53_22 Zhang, W., Ouyang, Y., Tang, F., Chen, J., & Li, H. (2019). Breath-focused mindfulness alters early and late components during emotion regulation. Brain and Cognition, 135 , 103585. https://doi.org/10.1016/j.bandc.2019.103585 Zhang, Z., Peng, Y., & Chen, T. (2022). Om chanting modulates the processing of negative stimuli: Behavioral and electrophysiological evidence. Frontiers in Psychology, 13 , 943243. https://doi.org/10.3389/fpsyg.2022.943243 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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13:00:40\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1609055,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6932735/v1/3d807dc9-e737-470a-a515-d0e5511114d4.pdf\"}],\"financialInterests\":\"The authors declare no competing interests.\",\"formattedTitle\":\"\\u003cp\\u003e\\u003cstrong\\u003eNeurophysiological Effects of Gayatri Mantra Meditation on Emotional Processing: An EEG-ERP Study\\u003c/strong\\u003e\\u003c/p\\u003e\",\"fulltext\":[{\"header\":\"INTRODUCTION\",\"content\":\"\\u003cp\\u003eEmotion regulation is a fundamental psychological process that enables individuals to modulate their emotional responses to internal and external stimuli, facilitating adaptive functioning and maintaining psychological wellbeing (Gross, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e1998\\u003c/span\\u003e). Dysregulation of emotional processing, characterized by heightened emotional reactivity or difficulties in managing emotions, and has been implicated in various psychopathological conditions, including anxiety, depression, borderline personality disorder, and post-traumatic stress disorder (PTSD) (Gross \\u0026amp; Mu\\u0026ntilde;oz, \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e1995\\u003c/span\\u003e; Aldao et al., \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e). Further, it has been reported that impaired emotion regulation has been linked to increased vulnerability to stress, cognitive dysfunction, and disruptions in social interactions (Sheppes et al., \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e). These consequences highlight the need for research on self-management interventions that have the potential to strengthen emotional regulation abilities leading towards enhancement of emotional balance and wellbeing. Therefore, research on emotion regulation has grown substantially over the past few decades, with growing attention to evidence-based approaches aimed at enhancing emotional regulation, particularly through mindfulness-based practices (Raugh et al., \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e; Hoge et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003ch3\\u003eNeurophysiological Marker in Emotion Regulation Research\\u003c/h3\\u003e\\n\\u003cp\\u003eWith advancements in neurophysiological tools such as electroencephalography (EEG), and functional magnetic resonance imaging (fMRI), deeper insights into the neural mechanisms underlying emotional processing have become possible to explore (Richter et al., \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Event-related potentials (ERPs) are one of the neurophysiological methods in EEG data analysis that have the potential to assess neural responses to stimuli. Late Positive Potential (LPP) are one of the ERP measures that have been reported to be associated with sustained attention and emotional processing (MacNamara et al., \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). The LPP typically emerges around 400 milliseconds after stimulus onset and sustains throughout stimulus presentation (Hajcak et al., \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e). Enhanced LPP amplitudes are observed in response to emotionally arousing stimuli, reflecting increased emotional engagement, while reduced LPP amplitudes indicate diminished emotional reactivity (Hajcak et al., \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e). This modulation of the LPP has been reported to be linked to both automatic and controlled processing of emotional stimuli, making it a valuable marker for studying emotion regulation (MacNamara et al., \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Hajcak et al., \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eModulation of emotional processing through meditation\\u003c/h2\\u003e \\u003cp\\u003eAmong the various methods for enhancing emotional regulation, meditation has emerged as one of the most promising interventions. Meditation has been reported to modulate neural mechanisms associated with affective processing (Tang et al., \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e). Studies using neurophysiological techniques, such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), have demonstrated that meditation can influence brain activity patterns associated with emotional regulation, leading to enhanced psychological wellbeing (Cahn \\u0026amp; Polich, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). Research has investigated the effects of mindfulness meditation on emotional processing, focusing on its influence on ERP components. For instance, Sobolewski et al. (\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e) observed that long-term mindfulness practitioners exhibited reduced LPP amplitudes in response to negative stimuli, suggesting decreased emotional reactivity in long-term mindfulness practitioners. Similarly, Deng et al. (\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e) found that mindfulness induction in adolescents led to reduced P1 and LPP amplitudes for negative stimuli. Zhang et al. (\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e) has reported that breath-focused mindfulness (BFM) reduces affective modulation of early ERP components (P1, N2) and the late positive potential (LPP) for both pleasant and negative images, suggesting BFM's potential to serve as an effective strategy for modulating neural response to affective stimuli. Further, Brown et al. (\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e) and Lin et al. (\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e) found that higher levels of dispositional mindfulness were associated with attenuated LPP responses to unpleasant stimuli, highlighting mindfulness's role in modulating affective processing. While these studies have demonstrated that mindfulness meditation practices can modulate neural mechanisms underlying emotion regulation, there are some studies in the literature that present mixed findings. For instance, Egan et al. (\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e) found that task-induced mindfulness heightened LPP amplitudes for both positive and negative stimuli, suggesting increased motivational relevance rather than reduced reactivity. Similarly, Eddy et al. (\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e) observed comparable differences in P300 or LPP responses between mindfulness and control sessions during picture-viewing tasks. Apart from mindfulness, other meditation approaches have also been examined for their effects on emotion processing using the EEG-ERP method. Zhang et al. (\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e) explored ERP responses during Om chanting but found no significant changes in P1 and LPP amplitudes when participants viewed unpleasant images while chanting the sound of \\u0026lsquo;Om\\u0026rsquo; compared to passive viewing. In contrast, Hao et al. (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e) demonstrated that groups practicing imagination-based loving-kindness meditation (ibLKM) and focused attention meditation (FAM) exhibited significantly higher LPP amplitudes over the central-parietal region in response to painful stimuli compared to a passive control group. While these findings indicate that meditation can influence neural mechanisms underlying emotion regulation, the heterogeneity of results emphasizes the need for further research into the underlying mechanisms across different meditation styles.\\u003c/p\\u003e \\u003cp\\u003eGiven the diversity of meditation approaches, it is crucial to explore other meditation types, especially mantra meditation, which is one of the widely practiced meditation in Eastern traditions for a long time (Acharya, \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e). It involves the rhythmic repetition of sacred words or phrases often in Sanskrit to achieve greater awareness (Lynch et al., 2018; Parthasarathi, \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e) and has been reported to be associated with improved mental health and stress reduction (\\u0026Aacute;lvarez-P\\u0026eacute;rez et al., 2022; Lynch et al., 2018; Tseng, \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Mantras have been an integral part of traditional Indian literature and among the traditional Vedic mantras, one of the most popularly chanted mantras for thousands of years has been the Gayatri Mantra (GM) (Acharya, \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eGayatri Mantra\\u003c/h3\\u003e\\n\\u003cp\\u003eThe Gayatri Mantra (GM) has been argued to be one of the most significant hymns of the Vedas, and has been an integral part of daily practices (sadhana) across various cultures and traditions in the Indian subcontinent for centuries (Acharya, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2003\\u003c/span\\u003e). The ancient Indian scripture, \\u003cem\\u003eAtharvaveda\\u003c/em\\u003e (19-1-71), describes GM as a practice that promotes longevity, wellbeing, and \\u0026lsquo;divine brilliance\\u0026rsquo; (Acharya, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2003\\u003c/span\\u003e). Contemporary studies have also reported association of GM practices with improved well-being and quality of life (Thrisna-Dewi et al., \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e), reduction in anxiety (Sudha, \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Ketut-Candrawati et al., \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e), and stress (Sharma et al., \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Sharma and Singh, \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e), and improvements in EEG brain waves associated with cognitive function (Thomas and Rao, \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eDespite its widespread practice and cultural significance, neurophysiological research on GM meditation, particularly in the context of emotional processing, remains limited. Therefore, to address this gap in the literature, the present study aimed to investigate the influence of GM meditation on visual ERPs elicited by emotionally arousing stimuli, with a specific focus on modulations in the LPP components. Unlike studies that capture transient state effects during meditation, this study sought to examine enduring trait-related neural changes, reflecting the long-term reorganization of emotional processing associated with sustained GM practice. It was hypothesized that individuals practicing GM meditation would exhibit reduced emotional reactivity, as indexed by lower LPP amplitudes, compared to non-practitioners.\\u003c/p\\u003e\"},{\"header\":\"Materials and methods\",\"content\":\"\\u003cp\\u003e \\u003cstrong\\u003eParticipants\\u003c/strong\\u003e \\u003cp\\u003eThe study included 24 healthy male participants from Delhi, aged 25 to 50 years (Mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD\\u0026thinsp;=\\u0026thinsp;32.37\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8.02), with no history of psychological illness, substance abuse, or intoxication. All participants had normal or corrected-to-normal vision. The participants' educational qualifications varied from \\u0026lsquo;High school\\u0026rsquo; to \\u0026lsquo;Postgraduate and above\\u0026rsquo; levels. Out of 24 participants, 12 reported practicing the Gayatri Mantra on a daily basis, they were classified as practitioners. The remaining participants (n\\u0026thinsp;=\\u0026thinsp;12) who did not engage in any Gayatri Mantra practices, not any other mind-body practices and were categorized as non-practitioners. The two groups of practitioners and non-practitioners did not significantly differ in age (p\\u0026thinsp;=\\u0026thinsp;0.79), gender, education, or place of residence. Their demographic detail is presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. Participation in this study was voluntary and an informed signed consent was obtained from each participant before data collection. The Ethical approval for the study was obtained from the Ethics Committee of the Indian Institute of Technology, Delhi (No. P021/P074).\\u003c/p\\u003e \\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\\u003eDemographic details of participants\\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\\u003eS.N\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eParticipants characteristics\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003eNumber of participants (N\\u0026thinsp;=\\u0026thinsp;24)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAge range\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e25\\u0026ndash;50 years\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMean age in years (M\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e32.37\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8.02 years\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGender\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003eM\\u0026thinsp;=\\u0026thinsp;24, F\\u0026thinsp;=\\u0026thinsp;0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eProfession\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eStudents\\u0026thinsp;=\\u0026thinsp;17\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eEmployed\\u0026thinsp;=\\u0026thinsp;7\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eEducational qualification\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHigh School\\u0026thinsp;=\\u0026thinsp;1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eSenior High School\\u0026thinsp;=\\u0026thinsp;1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGraduation\\u0026thinsp;=\\u0026thinsp;3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePost Graduation \\u0026amp; above =\\u0026thinsp;19\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eYears of experience of GM practices\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003eAverage\\u0026thinsp;=\\u0026thinsp;7.90 years (for N\\u0026thinsp;=\\u0026thinsp;12)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003eBeginners (\\u003cem\\u003e1 to 4 years\\u003c/em\\u003e)\\u0026thinsp;=\\u0026thinsp;5\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003eMid-term practitioners\\u0026thinsp;=\\u0026thinsp;3\\u003c/p\\u003e \\u003cp\\u003e (\\u003cem\\u003e5 to 9 years\\u003c/em\\u003e)\\u003c/p\\u003e \\u003cp\\u003eSenior practitioners\\u0026thinsp;=\\u0026thinsp;4\\u003c/p\\u003e \\u003cp\\u003e (\\u003cem\\u003e10 years and above)\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDaily GM practice minutes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e10 minutes/day\\u0026thinsp;=\\u0026thinsp;9 practitioners\\u003c/p\\u003e \\u003cp\\u003e30 minutes/day\\u0026thinsp;=\\u0026thinsp;3 practitioners\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e\\n\\u003ch3\\u003eExperimental Procedure\\u003c/h3\\u003e\\n\\u003cp\\u003eThe experiment was conducted in a designated EEG recording room with a quiet and isolated environment. Before the beginning of the experiment, participants provided signed consent and completed a demographic questionnaire. They were also screened using a WHO self-report questionnaire to assess mental health issues, including depression, anxiety, and somatoform disorders. All the participants were assessed to be sound and healthy. Additionally, participants completed standard psychometric scales, including the Emotion Regulation Questionnaire (Gross \\u0026amp; John, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2003\\u003c/span\\u003e), the Emotional Competence Scale (Sharma \\u0026amp; Bharadwaj, 2016), the Anashakti (non-attachment) Scale (Singh \\u0026amp; Raina, \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e), and the Brief Resilience Scale (Smith et al., \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e). Neurophysiological assessment was conducted using a Brain-Amp 64-channel EEG (Brain Products GmbH, Germany) with an R-NET cap, following the 10\\u0026ndash;20 electrode system. The ERP-Late Positive Potential was recorded by a Brain Vision recorder during the presentation of visual emotional stimuli on a computer screen.\\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eStimulus presentation\\u003c/strong\\u003e \\u003cp\\u003eA total of 120 affective pictures from the International Affective Picture System (IAPS; Lang et al., \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e) repository were presented in a random order using E-Prime 3.0 software (Psychology Software Tools, Inc) on a 22-inch computer screen kept at a distance of 75 cm from participants. The presented stimulus consisted of 40 positive, 40 negative, and 40 neutral valence pictures. The pictures were chosen based on valence and arousal as per their original data source. The mean and standard deviation of the selected negative pictures were (\\u003cem\\u003eM-valence\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;2.43, \\u003cem\\u003eSD-valence\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.81; \\u003cem\\u003eM-arousal\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;6.26, \\u003cem\\u003eSD-arousal\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.68), for positive pictures (\\u003cem\\u003eM-valence\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;7.10 \\u003cem\\u003eSD-valence\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.51; \\u003cem\\u003eM-arousal\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;4.18, \\u003cem\\u003eSD-arousal\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.71) and neutral pictures (\\u003cem\\u003eM-valence\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;5.03, \\u003cem\\u003eSD-valence\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.37; \\u003cem\\u003eM-arousal\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;3.06, \\u003cem\\u003eSD-arousal\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.79). Each stimulus was presented for 2.5 seconds, followed by a blank screen for 2 seconds and fixation \\u0026ldquo;+\\u0026rdquo; for 1 second. Participants were instructed to observe the stimulus passively and experience the emotions naturally.\\u003c/p\\u003e \\u003c/p\\u003e\\n\\u003ch3\\u003eAnalysis of Data from Psychological Scales\\u003c/h3\\u003e\\n\\u003cp\\u003eSelf-reported data from standard psychometric scales were analyzed using independent sample t-tests to compare the mean scores of practitioners and non-practitioners on emotion regulation, emotional competence, anasakti (non-attachment), and resilience. The effect size for the group mean differences was calculated using Cohen\\u0026rsquo;s d, with the significance level set at 0.05. The analysis revealed that the practitioners group had higher mean scores on all scales compared to the non-practitioners group. There was a significant group difference on the Emotional Competence scale (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.003, d\\u0026thinsp;=\\u0026thinsp;1.348) and the Anasakti (non-attachment) scale (p\\u0026thinsp;=\\u0026thinsp;0.043, d\\u0026thinsp;=\\u0026thinsp;0.879). However, differences in mean scores on Emotion Regulation Scale (p\\u0026thinsp;=\\u0026thinsp;0.18, d\\u0026thinsp;=\\u0026thinsp;0.606) and Resilience scale (p\\u0026thinsp;=\\u0026thinsp;0.17, d\\u0026thinsp;=\\u0026thinsp;0.624) were comparable, though the mean scores for Emotion Regulation (46.92 vs. 41.42) and Resilience (21.25 vs. 19.00) were higher in the practitioners group compared to the non-practitioners group (see Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eComparison between emotion related variables in practitioners and non-practitioners.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eScales and factors\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNon-Practitioners\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePractitioners\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003et-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003ep-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003ePoint estimate of Cohen's d\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOverall Emotional Competence (EC)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e90.42\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;13.92\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e109.25\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;14.03\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-3.301\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.003\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-1.348\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEC-ADF\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e17.00\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.76\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e20.83\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.44\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-2.536\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.019\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-1.035\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEC-AECE\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e18.17\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.51\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e21.42\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.71\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-2.537\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.019\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-1.036\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEC-AFE\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e16.83\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.85\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e21.58\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.77\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-3.475\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.002\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-1.419\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEC-ACPE\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e17.67\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.34\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e22.00\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.79\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-3.447\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.002\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-1.407\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEC-AEPE\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e20.75\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e23.42\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.29\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-1.742\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.095\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.711\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAnasakti (non-attachment)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e74.92\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.27\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e80.08\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5.45\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-2.153\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.043\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.879\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOverall Emotion-Regulation (ER)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e41.42\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e46.92\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.06\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-1.485\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.152\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.606\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eER-Cognitive reappraisal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e26.17\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5.81\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e29.92\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.06\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-1.546\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.136\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.631\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eER-Expressive suppression\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e15.25\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.99\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e17.00\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.26\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.923\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.366\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.618\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eResilience (BRS)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e19.00\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.95\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e21.25\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.22\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-1.528\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.141\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.624\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003e\\u003cem\\u003eNote: ADF\\u0026thinsp;=\\u0026thinsp;Adequate depth of feeling, AECE\\u0026thinsp;=\\u0026thinsp;Adequate expression and control of emotions, AFE\\u0026thinsp;=\\u0026thinsp;Ability to function with emotions, ACPE\\u0026thinsp;=\\u0026thinsp;Ability to cope with problem emotions, AEPE\\u0026thinsp;=\\u0026thinsp;Ability to enhance positive emotions. These are sub-factors of Emotional Competence (EC).\\u003c/em\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eERP Analysis\\u003c/h2\\u003e \\u003cp\\u003eEEG data were analyzed using Brain Vision Analyzer 2.2 software (Brain Products GmbH, Germany). Recordings were down-sampled to 256 Hz and bandpass filtered between 0.5 and 30 Hz. Eye blinks were corrected using an ocular correction algorithm, and data were re-referenced to the standard average. Artifact rejection was performed using semi-automatic inspection, removing amplitudes exceeding\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;100 \\u0026micro;V. ERP segments were created for positive, negative, and neutral stimuli, with epochs extracted from 500 ms before to 2000 ms after the event, and baseline correction was applied from \\u0026minus;\\u0026thinsp;200 ms to the start of the \\u0026lsquo;event\\u0026rsquo; of stimuli presentation. LPP waveforms were computed in the centro-parietal regions, including mid-centroparietal (CPz), the right centro-parietal regions (CP2, CP4, and CP6), and the left centro-parietal regions (CP1, CP3, and CP5). The average ERP for each stimulus type (positive, negative, and neutral) was computed for each participant in both the practitioner and non-practitioner groups. These averaged ERPs were then compared statistically.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eStatistical analysis of ERP data\\u003c/h3\\u003e\\n\\u003cp\\u003eStatistical analysis was performed using SPSS software version 28.0. The Shapiro-Wilk tests indicated that the data was normally distributed (p\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05). Additionally, Mauchly\\u0026rsquo;s test confirmed the assumption of sphericity for both groups (p\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05). As a result, repeated measures of ANOVA were conducted to assess the main effect of stimulus valence (positive, negative, neutral) on ERP-LPP amplitudes within the LPP time window of 450\\u0026ndash;750 ms in centro-parietal regions for both groups.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eMid-Centroparietal region (CPz), LPP (450\\u0026ndash;750 ms)\\u003c/h2\\u003e \\u003cp\\u003eThe results from the repeated measures ANOVA showed significant main effects of stimulus valence in LPP (450\\u0026ndash;750 ms) for non-practitioners (F(2,22)\\u0026thinsp;=\\u0026thinsp;16.269, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001, η\\u0026sup2; = 0.597) and also for practitioners (F(2,22)\\u0026thinsp;=\\u0026thinsp;3.934, p\\u0026thinsp;=\\u0026thinsp;0.035, η\\u0026sup2; = 0.263).\\u003c/p\\u003e \\u003cp\\u003ePost-hoc pairwise stimulus comparison in the non-practitioners group showed that there was a significant difference in elicited LPP amplitudes between neutral and negative stimuli (p\\u0026thinsp;=\\u0026thinsp;0.001), positive and negative stimuli (p\\u0026thinsp;=\\u0026thinsp;0.007), but no significant difference between positive and neutral stimuli (p\\u0026thinsp;=\\u0026thinsp;0.207). Similarly, in the practitioners\\u0026rsquo; group, significant LPP amplitude differences were found between neutral and negative stimuli (p\\u0026thinsp;=\\u0026thinsp;0.037), but no significant differences were seen between other pairs of stimuli (all p\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05). The LPP amplitude of the negative stimuli was higher than the positive and neutral stimuli in both groups (see Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eFurthermore, an analysis comparing practitioners and non-practitioners\\u0026rsquo; groups using an independent sample t-test revealed that the practitioners group exhibited a reduced LPP amplitude at the mid-centroparietal electrode (CPz) for positive, negative, and neutral stimuli compared to non-practitioners. Additionally, there was a significant difference in the LPP amplitude for negative stimuli between practitioners and non-practitioners (p\\u0026thinsp;=\\u0026thinsp;0.043) within the LPP window (450\\u0026ndash;750 ms), the practitioners group had lower LPP amplitude than non-practitioners as illustrated in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eLPP Amplitude difference between two groups on stimulus valence in the Mid-Centroparietal region\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTime window\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eStimulus type\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eNon-practitioners\\u003c/p\\u003e \\u003cp\\u003eM\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD (\\u0026micro;V)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003ePractitioners\\u003c/p\\u003e \\u003cp\\u003eM\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD (\\u0026micro;V)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003et-test\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003ep-value\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMid-Centroparietal (CPz)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eLPP (450\\u0026ndash;750 ms)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePositive\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.987\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.686\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.509\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.724\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.892\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.382\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNeutral\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.400 \\u0026plusmn; 0.857\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.181 \\u0026plusmn; 1.421\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.216\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.237\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNegative\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.930\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.071\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.838 \\u0026plusmn; 1.394\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.152\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.043\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eRight-Centroparietal-(CP2, CP4, CP6), LPP (450\\u0026ndash;750 ms)\\u003c/h2\\u003e \\u003cp\\u003eIn the Right-Centroparietal region, the RM-ANOVA result showed significant main effects of stimulus valence in the LPP window (450\\u0026ndash;750 ms) for non-practitioners (F(2,22)\\u0026thinsp;=\\u0026thinsp;13.400, p\\u0026thinsp;=\\u0026thinsp;0.000, η\\u0026sup2; = 0.549) and not for practitioners (F(2,22)\\u0026thinsp;=\\u0026thinsp;2.119, p\\u0026thinsp;=\\u0026thinsp;0.144, η\\u0026sup2; = 0.162).\\u003c/p\\u003e \\u003cp\\u003ePairwise stimulus comparison in the non-practitioners group showed that there was a significant LPP amplitude difference between neutral and negative stimuli (p\\u0026thinsp;=\\u0026thinsp;0.001), positive and negative stimuli (p\\u0026thinsp;=\\u0026thinsp;0.026), but no significant difference between positive and neutral stimuli (p\\u0026thinsp;=\\u0026thinsp;0.241).\\u003c/p\\u003e \\u003cp\\u003eHowever, in the practitioners group, no significant differences were found between any pair of stimuli (all p\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05). The LPP amplitude of the negative stimuli was higher than the positive and neutral stimuli in both groups (see Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe results of the between-group analysis (practitioners vs. non-practitioners) demonstrated that LPP amplitudes for all stimuli (positive, negative, and neutral) were higher in non-practitioners than practitioners. There was no significant difference between the LPP amplitudes of positive, neutral, and negative stimuli between practitioners and non-practitioners (at 0.05, 95% CI). However, a trend toward significant difference in negative stimuli between practitioners and non-practitioners was seen (p\\u0026thinsp;=\\u0026thinsp;0.07) at p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.1, 90% CI, the larger sample size could have substantiated these significant differences in LPP amplitude. Overall results showed that the practitioners exhibited a reduced LPP amplitude in the right centroparietal region for positive, negative, and neutral stimuli compared to non-practitioners, as illustrated in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eLPP Amplitude difference between two groups on stimulus valence in the Right-Centroparietal region.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTime window\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eStimulus type\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eNon-practitioners\\u003c/p\\u003e \\u003cp\\u003eM\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD (\\u0026micro;V)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003ePractitioners\\u003c/p\\u003e \\u003cp\\u003eM\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD (\\u0026micro;V)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003et-test\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003ep-value\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRight-Centroparietal\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003e(CP2, CP4, CP6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003eLPP (450\\u0026ndash;750 ms)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePositive\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.074\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.939\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.578\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.044\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.223\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.234\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNeutral\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.584\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.794\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.160\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.609\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.818\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.422\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNegative\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.871\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.028\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.863\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.550\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.878\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.073 (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eLeft-Centroparietal-(CP1, CP3, CP5), LPP (450\\u0026ndash;750 ms)\\u003c/h2\\u003e \\u003cp\\u003eRM-ANOVA result showed no significant main effects of stimulus valence in the LPP window (450\\u0026ndash;750 ms) in the left-centroparietal region for non-practitioners (F (2,22)\\u0026thinsp;=\\u0026thinsp;2.332, p\\u0026thinsp;=\\u0026thinsp;0.121, η\\u0026sup2; = 0.175) but significant for practitioners (F (2,22)\\u0026thinsp;=\\u0026thinsp;3.914, p\\u0026thinsp;=\\u0026thinsp;0.035, η\\u0026sup2; = 0.262).\\u003c/p\\u003e \\u003cp\\u003ePairwise stimulus comparison in the non-practitioners group showed no significant difference between any pair of stimuli (all p\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05). Similarly, in the practitioners\\u0026rsquo; group, pairwise comparison did not show significant differences between any pair of stimuli. The LPP amplitude of the negative stimuli was higher than positive and neutral stimuli in both groups.\\u003c/p\\u003e \\u003cp\\u003eThe results of the between-group analysis (practitioners vs. non-practitioners) demonstrated that practitioners exhibited a reduced LPP amplitude in the Left centroparietal region for positive, negative, and neutral stimuli compared to non-practitioners, as presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e. However, there were no significant differences in LPP amplitude for positive, neutral, and negative stimuli between practitioners and non-practitioners.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eLPP Amplitude difference between two groups on stimulus valence in the Left-Centroparietal region.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTime window\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eStimulus type\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eNon-practitioners\\u003c/p\\u003e \\u003cp\\u003eM\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD (\\u0026micro;V)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003ePractitioners\\u003c/p\\u003e \\u003cp\\u003eM\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD (\\u0026micro;V)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003et-test\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003ep-value\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eLeft Centroparietal\\u003c/b\\u003e (CP1, CP3, CP5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003eLPP (450\\u0026ndash;750 ms)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePositive\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.683\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.722\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.403\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.228\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.681\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.503\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNeutral\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.053\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.929\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.035\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.109\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.044\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.965\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNegative\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.329\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.885\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.148\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.988\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.472\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.642\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eAssociations between LPP amplitudes and emotional measures in practitioners and non-practitioners.\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eEmotional processing was found to be more prominent in the Mid Centroparietal (MCP) and Right Centroparietal (RCP) regions, with negative emotional stimuli eliciting stronger LPP amplitudes compared to positive and neutral stimuli. Therefore, in light of this, a correlation analysis was conducted to examine the relationship between emotional processing and psychometric emotional traits. Pearson correlation coefficients were computed between LPP amplitudes (elicited by negative emotional stimuli) and scores on all four emotional scales: Emotional Competency, Anasakti (Non-Attachment), Resilience, and the Emotion Regulation Questionnaire. Initially, the analysis was performed for all participants collectively across both MCP and RCP regions. Subsequently, the sample was divided into two groups, practitioners and non-practitioners, and separate correlation analyses were conducted for each group in both MCP and RCP regions.\\u003c/p\\u003e \\u003cp\\u003eFirst of all, correlation analysis was conducted for all participants to examine whether there is a consistent relationship between LPP amplitudes (elicited by negative emotional stimuli) and emotional traits across the entire sample. The results revealed a consistent pattern of negative correlations, indicating that higher scores on emotional traits were generally associated with lower LPP amplitudes, suggesting reduced neural reactivity to negative stimuli.\\u003c/p\\u003e \\u003cp\\u003eFurther, among the emotional variables, Emotional Competence showed a statistically significant negative correlation in both regions: MCP (r = \\u0026ndash;.508, p\\u0026thinsp;=\\u0026thinsp;.011) and RCP (r = \\u0026ndash;.597, p\\u0026thinsp;=\\u0026thinsp;.002), highlighting its strong association with attenuated neural responses. While other emotional variables also demonstrated negative correlations, they did not reach statistical significance. Specifically, Anasakti (non-attachment) showed MCP (r = \\u0026ndash;.387, p\\u0026thinsp;=\\u0026thinsp;.061) and RCP (r = \\u0026ndash;.221, p\\u0026thinsp;=\\u0026thinsp;.299); Resilience showed MCP (r = \\u0026ndash;.016, p\\u0026thinsp;=\\u0026thinsp;.943) and RCP (r = \\u0026ndash;.345, p\\u0026thinsp;=\\u0026thinsp;.099); and Emotion Regulation showed MCP (r = \\u0026ndash;.125, p\\u0026thinsp;=\\u0026thinsp;.559) and RCP (r = \\u0026ndash;.265, p\\u0026thinsp;=\\u0026thinsp;.211). These findings underscore the potential role of emotional traits, particularly emotional competence, in modulating neural responses to negative emotional stimuli, and suggest that the cultivation of such emotional traits by contemplative practices may contribute to more adaptive emotional regulation and psychological resilience.\\u003c/p\\u003e \\u003cp\\u003eSubsequently, separate correlation analyses were conducted for the practitioner and non-practitioner groups in both the MCP and RCP regions to examine whether the practice of GM meditation influences emotional traits and neural responses to negative emotional stimuli. This subgroup analysis aimed to explore the potential modulatory effects of sustained GM meditative practice on emotional processing at the neural level.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eMid Centroparietal (MCP) Region\\u003c/b\\u003e: In the Mid Centroparietal (MCP) region, practitioners exhibited a consistent pattern of negative correlations between LPP amplitudes to negative emotional stimuli and all four emotion-related psychological variables: emotional competence (\\u003cem\\u003er\\u003c/em\\u003e = \\u0026ndash;.574, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;.051), anasakti or non-attachment (\\u003cem\\u003er\\u003c/em\\u003e = \\u0026ndash;.490, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;.106), resilience (\\u003cem\\u003er\\u003c/em\\u003e = \\u0026ndash;.227, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;.479), and emotion regulation (\\u003cem\\u003er\\u003c/em\\u003e = \\u0026ndash;.198, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;.538). Although not all correlations had statistical significance, the direction and strength of these associations suggest a meaningful relationship. Specifically, higher levels of emotional competence and non-attachment among practitioners were associated with lower LPP amplitudes, indicating reduced neural reactivity to negative stimuli. Since the LPP is widely recognized as a neural marker of emotional salience and cognitive-affective engagement, this inverse relationship reflects greater emotional regulation capacity and reduced affective disturbance in response to negative stimuli among practitioners having higher psychological resources. The findings support the notion that long-term GM practice may cultivate emotional resilience and facilitate efficient downregulation of affective responses at a neural level. In contrast, non-practitioners demonstrated a divergent pattern. Correlations between LPP amplitudes and emotional variables in this group were generally weak and inconsistent: emotional competence (\\u003cem\\u003er\\u003c/em\\u003e = \\u0026ndash;.088, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;.786), anasakti (\\u003cem\\u003er\\u003c/em\\u003e = \\u0026ndash;.007, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;.982), resilience (\\u003cem\\u003er\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;.519, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;.084) and emotion regulation (\\u003cem\\u003er\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;.258, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;.419). Interestingly, the only moderate association observed was a positive correlation between resilience and LPP amplitude, suggesting that higher resilience in non-practitioners may coincide with \\u003cb\\u003eincreased\\u003c/b\\u003e neural engagement with negative stimuli. This could imply a reactive but effortful coping style rather than proactive emotional regulation. Moreover, the near-zero correlation with anasakti points to a lack of emotional detachment, which may contribute to heightened reactivity. Overall, the absence of significant negative correlations suggests that emotional traits in non-practitioners do not strongly buffer against neural responses to negative affect. This stark contrast with practitioners underscores the potential regulatory benefits of sustained GM meditation practice on emotional brain dynamics.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eRight Centroparietal (RCP) Region\\u003c/b\\u003e: In the Right Centroparietal (RCP) region, notable negative correlations between LPP amplitudes in response to negative stimuli and emotional constructs including: emotional competence (\\u003cem\\u003er\\u003c/em\\u003e = \\u0026ndash;.523, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;.081), resilience (\\u003cem\\u003er\\u003c/em\\u003e = \\u0026ndash;.365, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;.244), emotion regulation (\\u003cem\\u003er\\u003c/em\\u003e = \\u0026ndash;.244, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;.445), and anasakti (\\u003cem\\u003er\\u003c/em\\u003e = \\u0026ndash;.083, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;.799) were observed. The moderate negative correlation between emotional competence and LPP amplitudes reinforces the idea that practitioners with stronger emotional competence exhibit reduced neural reactivity to negative emotional content. This pattern, though not statistically significant, aligns with the broader trend seen in the MCP region indicating that higher emotional development in practitioners may be associated with reduced affective engagement in emotionally aroused contexts. These findings imply that right centroparietal regions, which are often implicated in attentional and emotional integration, may reflect enhanced cognitive-emotional control developed through sustained meditative practices like GM sadhana. In contrast, non-practitioners presented weak or inconsistent associations across the same emotional variables in the RCP region. Emotional competence (\\u003cem\\u003er\\u003c/em\\u003e = \\u0026ndash;.500, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;.098) showed a moderate negative correlation, but emotion regulation (\\u003cem\\u003er\\u003c/em\\u003e = \\u0026ndash;.074, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;.818), anasakti (\\u003cem\\u003er\\u003c/em\\u003e = \\u0026ndash;.080, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;.806), and resilience (\\u003cem\\u003er\\u003c/em\\u003e = \\u0026ndash;.159, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;.621) displayed weak and nonsignificant relationships. Interestingly, although the emotional competence\\u0026ndash;LPP link in non-practitioners mirrors the direction found in practitioners, the overall lack of consistent associations suggests a weaker integration between emotional traits and neural responses. This may reflect an absence of structured emotional regulation training, leading to less coherent or automatic downregulation of negative affect. Altogether, the RCP findings provide further support for the regulatory influence of GM meditation, where practitioners seem to engage more adaptive, less reactive emotional processing, as reflected in their neural findings.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThis study studied the impact of sustained GM meditation practices on emotional processing, with a specific focus on modulations in the ERP-LPP components elicited by emotionally arousing stimuli. The LPP has been reported to be a neural marker associated with sustained attention and evaluative processing of emotionally salient stimuli, typically exhibiting larger amplitudes in response to emotionally arousing content, reflecting heightened emotional arousal and increased attentional engagement (Cuthbert et al., \\u003cspan class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e; Hajcak et al., \\u003cspan class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003cp\\u003eThese findings reveal that GM practitioners exhibited markedly reduced LPP amplitudes across mid-centroparietal, right-centroparietal, and left-centroparietal regions in response to positive, negative, and neutral stimuli compared to non-practitioners. Notably, a significant reduction was observed for negative stimuli at the mid-centroparietal site (CPz) (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), and with a trend toward significance in the right-centroparietal region (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.1). These results suggest that GM practice may be associated with the downregulation of neural responses to emotionally arousing stimuli, potentially reflecting reduced emotional reactivity or the use of cognitive reappraisal strategies to attenuate emotional responses. Additionally, self-report assessments showed that GM practitioners scored significantly higher than non-practitioners on standardized measures of emotional competence, emotion regulation, non-attachment (Anasakti), and resilience scale, reflecting greater emotional well-being among practitioners. Further, correlation analysis showed that practitioners of GM showed consistent negative correlations between LPP amplitudes and emotional measures, suggesting better emotional regulation and reduced neural reactivity to negative stimuli, unlike non-practitioners who showed weak or inconsistent patterns in correlation. These findings, combined with attenuated neural responses to emotionally arousing stimuli, suggest that sustained GM practice enhances emotional regulation and reduces emotional reactivity leading towards overall wellbeing.\\u003c/p\\u003e\\n\\u003cp\\u003eThe observed lateralization of LPP reductions, particularly in the right-centroparietal region, aligns with existing literature implicating the right hemisphere in the processing of negative emotions (Davidson, \\u003cspan class=\\\"CitationRef\\\"\\u003e1995\\u003c/span\\u003e; Killgore \\u0026amp; Yurgelun-Todd, \\u003cspan class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e). This lateralization supports the notion that GM meditation may modulate right-hemispheric neural circuits involved in negative emotional processing, thereby contributing to improved emotional regulation.\\u003c/p\\u003e\\n\\u003cp\\u003eThese findings are consistent with previous research on mantra-based meditation practices. For instance, Thomas and Rao (\\u003cspan class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e) reported fMRI findings that listening to the GM activated brain regions associated with attention and emotional processing, such as the bilateral superior temporal gyri and right insula, suggesting enhanced neural efficiency in processing emotional information. Similarly, studies on OM mantra meditation have demonstrated increased theta power post-meditation, indicative of reduced cortical arousal and a relaxed mental state, which may contribute to improved emotional regulation (Harne \\u0026amp; Hiwale, \\u003cspan class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003cp\\u003eMoreover, present results align with broader literature on mindfulness and meditation practices on emotional processing. Sobolewski et al. (\\u003cspan class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e) found that mindfulness meditators exhibited reduced LPP amplitudes in response to negative stimuli, suggesting diminished emotional reactivity. Brown et al. (\\u003cspan class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e) also reported that individuals with higher mindfulness showed smaller LPP amplitudes in response to negative images, indicating reduced emotional reactivity. Lin et al. (2020) further demonstrated that participants engaging in open monitoring meditation exhibited reduced LPP amplitudes in response to negative stimuli, reflecting enhanced emotional regulation induced by meditation practices. The findings of the current study support the notion that regular meditative practices can lead to neurophysiological changes associated with improved emotional regulation.\\u003c/p\\u003e\\n\\u003cp\\u003eThe observed effects found in this study may be partially explained by the impact of mantra chanting on parasympathetic nervous system activation, which fosters a state of relaxation and contributes to mental and emotional wellbeing (Inbaraj et al., \\u003cspan class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Notably, previous neuroimaging research indicates that chanting GM activates brain regions associated with attention and emotion regulation, including the bilateral superior temporal gyri and right insula (Thomas \\u0026amp; Rao, \\u003cspan class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). These physiological findings are further supported by traditional literature, which describe GM as a powerful tool for promoting mental, emotional, and spiritual well-being (Acharya, \\u003cspan class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e). According to the yogic perspectives the 24 syllables of the GM are believed to be intertwined in such a way that when chanted generates unique vibratory patterns that stimulate subtle glands and vital energy centers in the body, thereby inducing beneficial psychophysiological changes in mind (Acharya, \\u003cspan class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e). Together, these perspectives reinforce the potential of sustained GM practice as a holistic intervention for enhancing emotional regulation and psychological health.\\u003c/p\\u003e\\n\\u003cp\\u003eDespite these promising findings, several limitations are hereby acknowledged in the present study. The cross-sectional design of the study precludes causal inferences regarding the effects of GM meditation on emotional processing. Additionally, the sample size, while adequate for detecting medium effect sizes, may have limited the statistical power to detect smaller effects, particularly in lateral regions. Future longitudinal studies with larger cohorts are warranted to confirm these findings and elucidate the temporal dynamics of neural changes associated with GM meditation.\\u003c/p\\u003e\\n\\u003cp\\u003eIn conclusion, this study has attempted to provide neurophysiological evidence supporting the role of GM meditation in modulating emotional processing, as reflected by reduced LPP amplitudes to emotional stimuli. These findings contribute to the growing body of literature on the benefits of mantra-based meditation practices and underscore the potential of GM meditation as a tool for enhancing emotional regulation and psychological well-being.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eDisclosure of potential conflicts of interest:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no conflicts of interest. They have no financial or non-financial interests that could influence the work reported in this paper.\\u003c/p\\u003e\\n\\u003ch3\\u003e\\u003cstrong\\u003eEthics Approval and Consent to Participate\\u003c/strong\\u003e\\u003c/h3\\u003e\\n\\u003cp\\u003eEthical approval for this study was obtained from the Research Ethics Committee of Indian Institute of Technology, Delhi, India (Ref No. P021/P074). All the participants gave consent for participation through signed documents.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot Applicable.\\u0026nbsp;This research did not receive any grants from public, commercial, or nonprofit funding agencies.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData Availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe data are available from the corresponding author on reasonable request.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors sincerely acknowledge the support received from the staff and students of the NRCVEE Centre, IIT Delhi, during this study. We extend our special appreciation to Shri Surendra Singh Ji Rawat, Mrs. Niraj, and Ph.D students Mr. Vicky Kachera, Ms. Dimple Bhakti Patel, and Ms. Neha Shekhawat for their dedicated assistance in research conduction.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n \\u003cli\\u003eAcharya, P. S. R. S. (2000). \\u003cem\\u003eSuper science of Gayatri\\u003c/em\\u003e (3rd ed.). Mathura, India: Yug Nirman Yojana Press, Gayatri Tapobhumi.\\u003c/li\\u003e\\n \\u003cli\\u003eAcharya, P. S. R. S. (2003). \\u003cem\\u003eGayatri Mahavigyan\\u003c/em\\u003e. Mathura, India: Yug Nirman Yojana Press, Gayatri Tapobhumi.\\u003c/li\\u003e\\n \\u003cli\\u003eAldao, A., Nolen-Hoeksema, S., \\u0026amp; Schweizer, S. (2010). 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Immediate effects of OM chanting on heart rate variability measures compared between experienced and inexperienced yoga practitioners. \\u003cem\\u003eInternational Journal of Yoga, 15\\u003c/em\\u003e(1), 52\\u0026ndash;58. https://doi.org/10.4103/ijoy.IJOY_79_21\\u003c/li\\u003e\\n \\u003cli\\u003eKetut-Candrawati, S. A., Dwidiyanti, M., \\u0026amp; Widyastuti, R. H. (2018). Effects of mindfulness with Gayatri mantra on decreasing anxiety in the elderly. \\u003cem\\u003eHolistic Nursing and Health Science, 1\\u003c/em\\u003e(1), 35. https://doi.org/10.14710/hnhs.1.1.2018.35-45\\u003c/li\\u003e\\n \\u003cli\\u003eKillgore, W. D., \\u0026amp; Yurgelun-Todd, D. A. (2007). The right-hemisphere and valence hypotheses: could they both be right (and sometimes left)?. \\u003cem\\u003eSocial cognitive and affective neuroscience\\u003c/em\\u003e, \\u003cem\\u003e2\\u003c/em\\u003e(3), 240-250.\\u003c/li\\u003e\\n \\u003cli\\u003eLang, P. J., Bradley, M. M., \\u0026amp; Cuthbert, B. N. (2005). 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Breath-focused mindfulness alters early and late components during emotion regulation. \\u003cem\\u003eBrain and Cognition, 135\\u003c/em\\u003e, 103585. https://doi.org/10.1016/j.bandc.2019.103585\\u003c/li\\u003e\\n \\u003cli\\u003eZhang, Z., Peng, Y., \\u0026amp; Chen, T. (2022). Om chanting modulates the processing of negative stimuli: Behavioral and electrophysiological evidence. \\u003cem\\u003eFrontiers in Psychology, 13\\u003c/em\\u003e, 943243. https://doi.org/10.3389/fpsyg.2022.943243\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"Indian Institute of Technology Delhi\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Gayatri Mantra, Meditation, Emotion Regulation, Wellbeing, EEG, Event Related Potential\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6932735/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6932735/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThis study has studied the impact of Gayatri Mantra (GM) meditation on emotional processing using event-related potentials (ERPs) and self-report measures. Twenty-four healthy male participants (M\\u0026thinsp;=\\u0026thinsp;32.37, SD\\u0026thinsp;=\\u0026thinsp;8.02 years) voluntarily participated in this study. Based on their self-reported daily routine practices, participants were categorized into two groups: meditation practitioners (n\\u0026thinsp;=\\u0026thinsp;12) and non-practitioners (n\\u0026thinsp;=\\u0026thinsp;12), who did not engage in any mind-body practices. All participants completed standardized psychometric assessments, including the Emotional Competence Scale, Emotion Regulation Questionnaire, Anashakti (non-attachment) Scale, and Brief Resilience Scale. EEG-ERP data were recorded using a 64-channel EEG during passive viewing of 120 affective images (40 positive, 40 negative, 40 neutral) from the International Affective Picture System (IAPS) repository. Results revealed significantly reduced ERP-Late Positive Potential (LPP) amplitudes in practitioners across mid, right and left-centroparietal sites, with the most significant reduction at CPz in response to negative stimuli (p\\u0026thinsp;=\\u0026thinsp;0.043) and near significance at right centroparietal regions (p\\u0026thinsp;=\\u0026thinsp;0.073). Self-report data revealed that practitioners demonstrated significantly greater Emotional Competence (p\\u0026thinsp;=\\u0026thinsp;0.003) and Anasakti (non-attachment) (p\\u0026thinsp;=\\u0026thinsp;0.043), along with higher scores on emotion regulation and resilience, suggesting a consistent trend toward enhanced emotional well-being. These findings suggest that sustained long-term GM meditation practice is associated with enhanced emotional regulation, reduced neural reactivity to affective stimuli, and improved emotional wellbeing. The combined neurophysiological and psychological evidence underscores the potential of GM meditation in cultivating emotional resilience and affective balance for overall wellbeing.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Neurophysiological Effects of Gayatri Mantra Meditation on Emotional Processing: An EEG-ERP Study\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-06-24 12:36:34\",\"doi\":\"10.21203/rs.3.rs-6932735/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"06942021-557c-47f6-b57c-d841e380c324\",\"owner\":[],\"postedDate\":\"June 24th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":50311522,\"name\":\"Cognitive Neuroscience\"},{\"id\":50311523,\"name\":\"Psychology\"}],\"tags\":[],\"updatedAt\":\"2025-06-24T12:36:34+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-06-24 12:36:34\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6932735\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6932735\",\"identity\":\"rs-6932735\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}