ArticleBiological psychiatry global open science2025
Oscillating Mindfully: Using Machine Learning to Characterize Systems-Level Electrophysiological Activity During Focused Attention Meditation.
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 6 papers.
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Who cites it
6 citing papers in PubMed.
- Meditation styles are highly discriminable from EEG at the subject level with limited generalization across the population: a machine-learning study.Frontiers in artificial intelligence · 2026Article
- Neurophysiological and autonomic patterns associated with heartfulness and bell meditation: a three-arm exploratory study.Frontiers in psychology · 2026Article
- Introduction to the Special Issue on Mindfulness: Measurement, Methods, Mechanisms, & Mental Health.Biological psychiatry global open science · 2025Article
- 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
- Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: There has been rapid growth of neuroelectrophysiological studies that aspire to uncover the "black box" of mindfulness and meditation. Reliance on traditional data analysis methods hinders understanding of the complex, nonlinear, multidimensional, and systemic nature of the functional neuroelectrophysiology of meditation states. Methods: Thus, to reveal the complex systemic neuroelectrophysiology of meditation, we applied a machine learning extreme gradient boosting classification algorithm and 4 complementary feature importance methods to extract systemic electroencephalography features characterizing mindful states from electroencephalography recorded during a focused attention meditation and a control mind-wandering state among 26 experienced meditators. Results: The algorithm classified meditation versus mind-wandering states with 83% accuracy, with an area under the receiver operating characteristic curve of 79% and F1 score of 74%. Feature importance techniques identified 10 electroencephalography features associated with increased power and coherence of high-frequency oscillations during focused attention meditation relative to an instructed mind-wandering state. Conclusions: The findings help delineate the complex systemic oscillatory activity that characterizes meditation.
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Registered trials
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