bioRxiv Preprint Server
August 29, 2024
Saketh Malipeddi, Arun Sasidharan, Rahul Venugopal et al.
4 citations
preprint
Advanced meditators from the Isha Yoga tradition show shorter intrinsic neural timescales (INTs) during breath-watching, indicating deidentification with mental contents, and no significant differences in INTs between tasks, indicating non-dual awareness. Shorter INTs correlate with self-reported equanimity. The brain's intrinsic neural timescales may serve as a neural marker of equanimity.
bioRxiv Preprint Server
February 11, 2025
Saketh Malipeddi, Arun Sasidharan, Rahul Venugopal et al.
2 citations
preprint
Meditation alters brain activity, particularly in alpha and theta frequency bands, but most research has focused on average power changes from rest to meditation rather than how quickly these changes emerge. This gap means little is known about the time-to-onset and temporal dynamics of neural shifts during meditation practice.
bioRxiv Preprint Server
September 27, 2022
Sruthi Susan Kuriakose, Aishwarya Swamy, Rahul Venugopal et al.
2 citations
preprint
Meditation proficiency is hard to achieve without feedback because the mind easily wanders. EEG neurofeedback could help by providing real-time assessment. This work proposes a lightweight scheme using an autoencoder model trained on EEG features from long-term meditators. The model runs in real time on short data segments from a few channels, using reconstruction errors or latent variables as feedback parameters to measure meditation ability. However, results show that meditation states overlap substantially in multivariate EEG features and have prominent temporal dynamics, which simple one-class algorithms fail to capture. Multiple improvements to the autoencoder are described to address these issues and enable high-precision neurofeedback protocols.
bioRxiv (Cold Spring Harbor Laboratory)
June 26, 2025
Prakash Shrimali, Arun Sasidharan, Saketh Malipeddi et al.
1 citation
preprint
Meditation involves training attention inward, but the brain activity that distinguishes meditative from non-meditative states across different traditions is not well understood. Analyzing high-density EEG data from 170 participants—121 advanced meditators and 49 controls—across Vipassana, Brahma Kumaris Raja Yoga, Heartfulness, and Isha Yoga traditions, researchers used random forest classifiers to distinguish meditative from non-meditative states with 91% accuracy. Nonlinear features contributed most, indicating a core neurodynamic profile. Classification was higher in advanced meditators (92%) than controls (85%), with different feature importance: nonlinear and aperiodic features dominated in meditators, while oscillatory and timescale features dominated in controls. Each tradition showed distinct neurodynamic profiles, suggesting multiple pathways lead to meditative states.
Communications Biology
June 12, 2026
Saketh Malipeddi, Arun Sasidharan, Bianca Ventura et al.
Advanced meditators from the Isha Yoga tradition report stronger non-dual experiences—where the boundary between self and environment dissolves—during breath-watching meditation compared to novices and meditation-naïve controls. Using EEG-based intrinsic neural timescales (INT), researchers found that across all participants, INTs are longer during internal attention (breath-watching) than during an external cognitive task. However, advanced meditators show similar INT durations between internal and external attention, and this reduced difference correlates with stronger reported non-dual experiences. The findings suggest that similar intrinsic neural timescale durations across internal and external attention may be a neural signature of non-duality.
bioRxiv (Cold Spring Harbor Laboratory)
September 9, 2025
Ratna Jyothi Kakumanu, Ajay Kumar Nair, Arun Sasidharan et al.
preprint
A novel approach using four EEG-based biomarkers—potentials, their first-order derivatives, and phase slip rates derived from each—distinguished novice Vipassana meditators from non-meditator controls. Phase slip rates, discontinuities in instantaneous phase representing cortical phase transitions, were computed from 128-channel EEG data collected during a visual oddball task. Eight novice Vipassana subjects and eight non-meditator controls each completed 50 trials; 44 artifact-free trials per subject were averaged. Spatiotemporal profiles of all four biomarkers differed significantly between groups, and the novice Vipassana subjects showed faster object recognition. The findings suggest phase slip rates offer a promising set of biomarkers for quantitative task-based EEG analyses.
Neuroscience of Consciousness
January 1, 2026
Bianca Ventura, Andrea Buccellato, Saketh Malipeddi et al.
Vipassana and Shoonya meditation produce distinct alpha-band brain dynamics, with differences more apparent in temporal variability than in average activity. Vipassana, involving systematic body scanning, showed lower variability and more regular temporal patterns in both peak frequency and power of alpha oscillations. Shoonya, a non-directed attention practice, exhibited higher variability and irregularity in these measures. The findings suggest that dynamic features of brain activity, such as variability and temporal irregularity, may distinguish contemplative practices better than static averages, and could reflect a shared property of brain and experience.
Biological Psychology
May 1, 2018
Ratna Jyothi Kakumanu, Ajay Kumar Nair, Rahul Venugopal et al.
Vipassana meditation, as taught by S.N. Goenka, involves distinct EEG patterns that vary by technique and practitioner proficiency. Compared to Novices, Senior meditators and Teachers showed greater delta, theta-alpha, and low-gamma power at rest. During concentrative and mindfulness meditation, they exhibited increased low-alpha and low-gamma power; during loving-kindness meditation, increased theta-alpha and low-gamma power. Measures of permutation entropy and Higuchi fractal dimension revealed that only Teachers, not Seniors, displayed consistent increases in network complexity from rest and across meditation states, indicating that high proficiency, not just duration of experience, drives neural complexity changes.