Decoding Depth of Meditation: EEG Insights from Expert Vipassana Practitioners
Nicco Reggente, Christian Kothe, Tracy Brandmeyer, Grant Hanada, Ninette Simonian, Sean P. Mullen, Tim Mullen
January 31, 2024 preprint DOI: 10.31234/osf.io/7c3er (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Experimental study |
|---|---|
| Sample size | 34 |
| Population | Expert Vipassana meditators |
| Duration | Two separate sessions |
| Topics | Meditation |
| Keywords | Electroencephalography Decoding methods Cognitive psychology Speech recognition Telecommunications Archaeology |
| Citations | 4 |
| Key findings | EEG activity and effective connectivity can decode self-reported meditative depth in expert Vipassana meditators, with a novel 'spontaneous emergence' reporting method and a fusion-based machine learning approach yielding the best performance. |
Abstract
Meditation practices have demonstrated numerous psychological and physiological benefits, yet capturing the neural correlates of varying meditative depths remains challenging. This study aimed to decode self-reported time-varying meditative depth in expert practitioners using EEG. Expert Vipassana meditators (n=34) participated in two separate sessions. Participants reported their meditative depth on a personally defined 1-5 scale using both traditional probing and a novel "spontaneous emergence" method. EEG activity and effective connectivity in theta, alpha, and gamma bands was 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 to traditional probing and correlated more strongly with post-session outcome measures. Best performance was achieved by a novel machine learning method which fused spatial, spectral, and connectivity information. Conventional EEG channel-level methods and pre-selected 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.
Comparable studies
Other experimental studies on meditation, most cited first.
| Study | Year | Design | Participants |
|---|---|---|---|
| The benefits of being present: Mindfulness and its role in psychological well-being. Cancer patients (clinical intervention study) | 2003 | Mixed methods: scale development, correlational, quasi-experimental, laboratory, experience-sampling, and clinical intervention study | |
| Loving-kindness meditation increases social connectedness. | 2008 | Controlled laboratory experiment | |
| Mindfulness and mind-wandering: Finding convergence through opposing constructs. | 2012 | Two studies: correlational study and experimental study | |
| Mental Training Enhances Attentional Stability: Neural and Behavioral Evidence | 2009 | Experimental study | |
| Mindfulness meditation and substance use in an incarcerated population. Incarcerated population | 2006 | Quasi-experimental with treatment-as-usual control condition |
Citations in the library
Cited by 3
- Using Electroencephalography to Advance Mindfulness Science: A Survey of Emerging Methods and Approaches. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging April 1, 2025
- Decoding Mindfulness With Multivariate Predictive Models. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging April 1, 2025
- Exploring EEG biomarkers in meditation, yoga, and mantra practices: a review of neural correlates and methodologies Neural Computing and Applications March 5, 2026
Cites 63
- Using Self-Report Assessment Methods to Explore Facets of Mindfulness Assessment January 27, 2006
- 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
- Long-term meditators self-induce high-amplitude gamma synchrony during mental practice Proceedings of the National Academy of Sciences November 8, 2004
- Loving-kindness and compassion meditation: potential for psychological interventions. Clinical Psychology Review July 26, 2011
and 58 more in the library