Heartbeat-Evoked Potentials Track Depth of Meditation
Poster No:
1927
Submission Type:
Abstract Submission
Authors:
Nicco Reggente1, Mihir Nath1
Institutions:
1Institute for Advanced Consciousness Studies, Santa Monica, CA
First Author:
Co-Author:
Introduction:
The relationship between meditation and interoception-our ability to sense and interpret internal bodily signals-has emerged as a central focus in contemplative neuroscience. While experienced meditators report enhanced meta-cognitive awareness of bodily signals, the precise mechanisms underlying this enhancement remain unclear. Heartbeat-evoked potentials (HEPs), which index the brain's processing of cardiac signals, have been linked to emotional regulation, optimal engagement states, and meditation-induced changes in neural activity, with experienced meditators showing distinct HEP patterns even at rest, suggesting enduring adaptations in interoceptive processing. This study investigates whether HEPs can serve as a personalized, objective, real-time marker of meditative depth in experienced Vipassana practitioners, potentially illuminating how sustained states of enhanced brain-body integration contribute to meditation's therapeutic effects.
Methods:
EEG and ECG data from 30 experienced Vipassana practitioners (mean experience: 16.15 years; 6.53 days/week practice; 82.38 cumulative retreat days) were collected across two sessions (≥1 week apart, 160 total minutes of meditation), where participants continuously reported their meditative depth on a scale of 1-5. EEG data were segmented into epochs time-locked to the R-peak of the ECG. Each epoch spanned from -200 ms to +800 ms relative to the R-peak. The EEG data were preprocessed through band-pass filtering (0.5-30 Hz), automated artifact rejection, and manual correction via independent component analysis (ICA). Baseline correction was applied using the pre-R-peak interval (-200 to 0 ms). HEP amplitudes across depths were compared using one-way ANOVAs, with Tukey's HSD for pairwise analysis. Cluster-based permutation tests (1000 permutations) controlled for multiple comparisons across channels and time points, with FDR correction (p < 0.05). We calculated the HEP range as the difference in mean HEP amplitude (channel C3, 144-288 ms) between high (4, 5) and low (1, 2) meditative depths for each participant and analyzed its predictive value on self-reported outcomes using mixed linear models (MLM), with FDR correction applied for significance.
Results:
Our findings demonstrate remarkable efficacy of HEPs in detecting meditative depth changes. Heart evoked potential amplitudes in the 170-300ms post r-peak window increased with self-reported depth, most prominently in channel 'C3' (p = 2.22E-119, η2 = 5.55) . These features proved highly reliable, enabling 91% accuracy in classifying high (4, 5) versus low (1, 2) depths and a 0.63 mean absolute error in predicting depth on the 1-5 scale for unseen trials. HEP range emerged as a meaningful predictor of both subjective and psychophysiological outcomes. A larger HEP range correlated with greater Decentering (Toronto Mindfulness Scale; Lau et al., 2006, Coef = 6.79, p = 1.25E-06), reduced Mood Disturbance (Profile of Mood States; McNair, 1992, Coef = -2.221, p = 0.035), increased Vigor (Coef = 2.252, p = 3.84E-23), and decreased Fatigue (Coef = -4.205, p = 2.28E-02). It also linked to a decrease in Personal Self and an increase in Transpersonal Self (Meditation Depth Index; Piron, 2022, Coef = -0.315, p = 6.93E-03; Coef = 0.65, p = 1.00E-138), indicating deeper meditative states and enhanced transpersonal awareness.
·Heartbeat evoked potential amplitude as a function of self-reported meditative depth where t=0 is the heartbeat.
·Mean HEP amplitude as a function of self-reported meditative depth from 148.9ms to 288ms post heartbeat for Channel C3.
Conclusions:
These results highlight the potential of HEPs as a robust neurophysiological marker for real-time meditation depth, offering insights into the interplay between heart-brain dynamics and contemplative practices. Furthermore, the ability of HEPs to objectively track meditative depth has significant implications for meditation neurofeedback, enabling personalized feedback to optimize practice and enhance emotional and physical well-being. By advancing the understanding of meditation's neurophysiological underpinnings, this work paves the way for the development of innovative tools to support meditation-based interventions.
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making
Modeling and Analysis Methods:
Classification and Predictive Modeling 2
EEG/MEG Modeling and Analysis
Novel Imaging Acquisition Methods:
EEG 1
Keywords:
Electroencephaolography (EEG)
ELECTROPHYSIOLOGY
Machine Learning
1|2Indicates the priority used for review
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Provide references using APA citation style.
McNair, D. M. (1992). Profile of Mood States. Educational and Industrial Testing Service.
Piron, H. (2022). Meditation Depth Questionnaire (MEDEQ) and Meditation Depth Index (MEDI). In O. N. Medvedev, C. U. Krägeloh, R. J. Siegert, & N. N. Singh (Eds.), Handbook of Assessment in Mindfulness Research. Springer, Cham. https://doi.org/10.1007/978-3-030-77644-2_41-1