Decoding Depth of Meditation: Electroencephalography Insights From Expert Vipassana Practitioners
Nicco Reggente, Christian Kothe, Tracy Brandmeyer, Grant Hanada, Ninette Simonian, Sean P. Mullen, Tim Mullen
Biological Psychiatry Global Open Science October 17, 2024 DOI: 10.1016/j.bpsgos.2024.100402 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational study Peer reviewed |
|---|---|
| Sample size | 34 |
| Population | Expert Vipassana meditators |
| Duration | 2 separate sessions |
| Topics | Meditation |
| Keywords | Electroencephalography Decoding methods Speech recognition Cognitive psychology Telecommunications Archaeology |
| Citations | 15 |
| Key findings | Self-reported meditative depth can be decoded from EEG using multivariate machine learning, with the spontaneous emergence method outperforming traditional probing. |
Abstract
Meditation practices have demonstrated numerous psychological and physiological benefits, but capturing the neural correlates of varying meditative depths remains challenging. In this study, we aimed to decode self-reported time-varying meditative depth in expert practitioners using electroencephalography (EEG). Expert Vipassana meditators ( n = 34) participated in 2 separate sessions. Participants reported their meditative depth on a personally defined 1 to 5 scale using both traditional probing and a novel spontaneous emergence method. EEG activity and effective connectivity in theta, alpha, and gamma bands were used to predict meditative depth using machine/deep learning, including a novel method that fused source activity and connectivity information. We achieved significant accuracy in decoding self-reported meditative depth across unseen sessions. The spontaneous emergence method yielded improved decoding performance compared with traditional probing and correlated more strongly with postsession outcome measures. Best performance was achieved by a novel machine learning method that fused spatial, spectral, and connectivity information. Conventional EEG channel-level methods and preselected default mode network regions fell short in capturing the complex neural dynamics associated with varying meditation depths. This study demonstrates the feasibility of decoding personally defined meditative depth using EEG. The findings highlight the complex, multivariate nature of neural activity during meditation and introduce spontaneous emergence as an ecologically valid and less obtrusive experiential sampling method. These results have implications for advancing neurofeedback techniques and enhancing our understanding of meditative practices. We investigated meditative depth in expert Vipassana practitioners ( n = 34) using 64-channel EEG and bioperipheral monitoring. Participants rated their meditation depth (1–5) during eyes-closed sessions using either probed reporting or a novel spontaneous emergence method. Despite the absence of univariate EEG correlates, we successfully decoded meditative depth both continuously and binarily (high vs. low) during ecological meditation. The spontaneous emergence method yielded more frequent depth reports and correlated better with established postsession outcome measures than probed reporting. These findings demonstrate the feasibility of tracking meditative states using multivariate EEG patterns and suggest potential improvements for neurofeedback in meditation practices.
Comparable studies
Other observational and cohort studies on meditation, most cited first.
| Study | Year | Design | Participants |
|---|---|---|---|
| Using Self-Report Assessment Methods to Explore Facets of Mindfulness Undergraduate students | 2006 | Observational cohort | |
| Effectiveness of a meditation-based stress reduction program in the treatment of anxiety disorders Patients with generalized anxiety disorder or panic disorder with or without agoraphobia | 1992 | Observational cohort | n = 22 |
| Meditation experience is associated with increased cortical thickness Participants with extensive Insight meditation experience and matched controls | 2005 | Observational cohort | n = 20 |
| Meditation experience is associated with differences in default mode network activity and connectivity Experienced meditators and matched meditation-naive controls | 2011 | Observational cohort | |
| Long-term meditators self-induce high-amplitude gamma synchrony during mental practice Long-term Buddhist practitioners and controls | 2004 | Observational cohort |
Citations in the library
Cited by 11
- Using Electroencephalography to Advance Mindfulness Science: A Survey of Emerging Methods and Approaches. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging April 1, 2025
- Toward a neuroscience of consciousness using advanced meditation. Neuroscience & Biobehavioral Reviews February 1, 2026
- Decoding Mindfulness With Multivariate Predictive Models. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging April 1, 2025
- Decoding the Self: Single-Trial Prediction of Self-Boundary Meditation States From Magnetoencephalography Recordings. Human Brain Mapping 2026
- The Neuroscience of Religious and Spiritual Practices: A Systematic Review of Neurotheological Evidence Brain and Behavior August 30, 2026
and 6 more in the library
Cites 71
- The benefits of being present: Mindfulness and its role in psychological well-being. Journal of Personality and Social Psychology 2003
- Using Self-Report Assessment Methods to Explore Facets of Mindfulness Assessment January 27, 2006
- Meditation Programs for Psychological Stress and Well-being JAMA Internal Medicine January 6, 2014
- The toronto mindfulness scale: Development and validation Journal of Clinical Psychology 2006
- Meditation experience is associated with differences in default mode network activity and connectivity Proceedings of the National Academy of Sciences November 23, 2011
and 66 more in the library