Similar States, Different Paths: Neurodynamics of diverse meditation techniques
Prakash Shrimali, Arun Sasidharan, Saketh Malipeddi, Bianca Ventura, Rahul Venugopal, Ajay Kumar Nair, Ravindra P. Nagendra, Bindu M. Kutty, Georg Northoff
bioRxiv (Cold Spring Harbor Laboratory) June 26, 2025 preprint DOI: 10.1101/2025.06.20.660652 via OpenAlex
Summary
Meditation involves diverse practices that focus attention inward, unlike externally oriented tasks. Analyzing EEG data from 170 participants (121 advanced meditators, 49 controls) across four traditions—Vipassana, Brahma Kumaris Raja Yoga, Heartfulness, and Isha Yoga—revealed that nonlinear brain features best distinguished meditative from non-meditative states, with 91% classification accuracy. Advanced meditators showed higher accuracy (92%) than controls (85%), and each tradition displayed unique neurodynamic profiles, indicating multiple pathways to meditative states.
Study at a glance
| Design | observational study |
|---|---|
| Sample size | 170 |
| Population | advanced meditators and controls across four meditation traditions |
| Key finding | Nonlinear EEG features most strongly distinguished meditative from non-meditative states, with distinct neurodynamic profiles across traditions and higher classification accuracy in advanced meditators. |
Abstract
Abstract Meditation encompasses diverse practices that train attention inward, in contrast to externally oriented task states. However, the neurodynamic features distinguishing meditative states from non-meditative states across traditions remain unclear. We analyzed high-density EEG data (N=170; 121 advanced meditators, 49 controls) across four traditions: Vipassana, Brahma Kumaris Raja Yoga, Heartfulness, and Isha Yoga. EEG features spanned oscillatory, aperiodic, nonlinear, and timescale components. Using random forest classifiers, we distinguished meditative from non-meditative states with robust classification performance (91%). Nonlinear features contributed the most, suggesting a core neurodynamic profile. Classification performance was higher in advanced meditators (92%) than in controls (85%), with distinct feature importance: nonlinear and aperiodic features dominated in meditators, and oscillatory and timescale features in controls. Each tradition showed distinct neurodynamic profiles, indicating technique-specific constellations. Our findings revealed shared yet distinct neurodynamic signatures across meditation techniques, suggesting that multiple neurodynamic pathways lead to meditative states.