ArticleBiological psychiatry global open science2025
Decoding Depth of Meditation: Electroencephalography Insights From Expert Vipassana Practitioners.
Article in Biological psychiatry global open science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Neuroscience of Religious and Spiritual Practices: A Systematic Review of Neurotheological Evidence.Brain and behavior · 2026Pooled it
- Cortical neuron classes and recursive curvature collapse: a neurobiological model of conscious dynamics.Theory in biosciences = Theorie in den Biowissenschaften · 2026Article
- Non-duality in brain and experience of advanced meditators-key role for intrinsic neural timescales.Communications biology · 2026Article
- Alpha and theta audiovisual interventions in a reflective chamber demonstrate acute effects on stress and burnout.NPJ digital medicine · 2026Article
- Decoding the Self: Single-Trial Prediction of Self-Boundary Meditation States From Magnetoencephalography Recordings.Human brain mapping · 2026Article
- Meditation-Specific Neural Predictors of State Mindfulness During Eyes Open Meditation.Mindfulness · 2026Article
- Introduction to the Special Issue on Mindfulness: Measurement, Methods, Mechanisms, & Mental Health.Biological psychiatry global open science · 2025Article
- Using Electroencephalography to Advance Mindfulness Science: A Survey of Emerging Methods and Approaches.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2025Review
- Introduction to the Special Issue on Cognitive Neuroscience of Mindfulness.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2025Article
- Decoding Mindfulness With Multivariate Predictive Models.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2025Review
- Determining the depth of meditation through frontal alpha asymmetry.Frontiers in human neuroscience · 2025Article
- Article
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Authors and funding
7 authors.
Funding
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Abstract
Background: 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). Methods: Expert Vipassana meditators ( Results: 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. Conclusions: 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.
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