Oscillating Mindfully: Using Machine Learning to Characterize Systems-Level Electrophysiological Activity During Focused Attention Meditation
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 ne...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Biological Psychiatry Global Open Science |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667174324001368 |
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| author | Noga Aviad Oz Moskovich Ophir Orenstein Etam Benger Arnaud Delorme Amit Bernstein |
| author_facet | Noga Aviad Oz Moskovich Ophir Orenstein Etam Benger Arnaud Delorme Amit Bernstein |
| author_sort | Noga Aviad |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a2265f8a4a7d411cac29a557fc9f0172 |
| institution | Kabale University |
| issn | 2667-1743 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Biological Psychiatry Global Open Science |
| spelling | doaj-art-a2265f8a4a7d411cac29a557fc9f01722025-01-24T04:45:53ZengElsevierBiological Psychiatry Global Open Science2667-17432025-03-0152100423Oscillating Mindfully: Using Machine Learning to Characterize Systems-Level Electrophysiological Activity During Focused Attention MeditationNoga Aviad0Oz Moskovich1Ophir Orenstein2Etam Benger3Arnaud Delorme4Amit Bernstein5Observing Minds Laboratory, School of Psychological Science, University of Haifa, Haifa, IsraelNYX Technologies, Haifa, IsraelNYX Technologies, Haifa, IsraelRachel and Selim Benin School of Computer Science and Engineering, Hebrew University of Jerusalem, Jerusalem, IsraelSwartz Center for Computational Neuroscience, University of California, San Diego, La Jolla, California; Centre de Recherche Cerveau et Cognition, Toulouse III University, Toulouse, FranceObserving Minds Laboratory, School of Psychological Science, University of Haifa, Haifa, Israel; Center for Healthy Minds, Department of Psychology, University of Wisconsin-Madison, Madison, Wisconsin; Address correspondence to Amit Bernstein, Ph.D.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.http://www.sciencedirect.com/science/article/pii/S2667174324001368Complex systemsEEGMachine learningMeditationMindfulness |
| spellingShingle | Noga Aviad Oz Moskovich Ophir Orenstein Etam Benger Arnaud Delorme Amit Bernstein Oscillating Mindfully: Using Machine Learning to Characterize Systems-Level Electrophysiological Activity During Focused Attention Meditation Biological Psychiatry Global Open Science Complex systems EEG Machine learning Meditation Mindfulness |
| title | Oscillating Mindfully: Using Machine Learning to Characterize Systems-Level Electrophysiological Activity During Focused Attention Meditation |
| title_full | Oscillating Mindfully: Using Machine Learning to Characterize Systems-Level Electrophysiological Activity During Focused Attention Meditation |
| title_fullStr | Oscillating Mindfully: Using Machine Learning to Characterize Systems-Level Electrophysiological Activity During Focused Attention Meditation |
| title_full_unstemmed | Oscillating Mindfully: Using Machine Learning to Characterize Systems-Level Electrophysiological Activity During Focused Attention Meditation |
| title_short | Oscillating Mindfully: Using Machine Learning to Characterize Systems-Level Electrophysiological Activity During Focused Attention Meditation |
| title_sort | oscillating mindfully using machine learning to characterize systems level electrophysiological activity during focused attention meditation |
| topic | Complex systems EEG Machine learning Meditation Mindfulness |
| url | http://www.sciencedirect.com/science/article/pii/S2667174324001368 |
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