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.
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.
International Journal of Yoga
January 1, 2022
Ashwini S Savanth, P A Vijaya, Ajay Kumar Nair et al.
Long-term Rajayoga meditators from the Brahma Kumaris tradition show distinct brain functional connectivity patterns compared to short-term practitioners, even while performing a non-meditative task. Using task-based fMRI data, graph-theoretical measures of functional connectivity (adjacency matrices, global efficiency, local efficiency) were calculated from 132 brain regions. Machine learning classifiers—especially decision tree, random forest, and gradient boosted tree—achieved over 84% test accuracy in distinguishing long-term (mean 13,596 hours of practice) from short-term (mean 1,095 hours) meditators. These findings suggest that extensive meditation practice produces lasting changes in brain network organization that persist outside of meditation.
Progress in Brain Research
January 1, 2019
Ratna Jyothi Kakumanu, Ajay Kumar Nair, Arun Sasidharan et al.
Long-term Vipassana meditation practice, both in duration and quality, is linked to graded differences in brain activity during a cognitive task. After an hour of meditation, three groups of practitioners—novices, seniors, and teachers—performed a gamified oddball task while EEG was recorded. All groups performed well and showed similar overall brain-wave patterns, but more experienced meditators exhibited reduced theta synchrony, enhanced alpha desynchronization, and lesser theta-alpha coherence. Teachers differed most from novices, with seniors forming an intermediate group. The findings suggest that both the quantity and quality of meditation influence EEG dynamics during cognitive processing, and that meditating before a task can amplify these state-trait effects.
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.