正念振荡：使用机器学习来描述集中注意力冥想期间的系统级电生理活动

IF 4 Q2 NEUROSCIENCES

Biological psychiatry global open science Pub Date : 2024-11-26 DOI:10.1016/j.bpsgos.2024.100423

Noga Aviad , Oz Moskovich , Ophir Orenstein , Etam Benger , Arnaud Delorme , Amit Bernstein

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引用次数: 0

摘要

背景神经电生理学的研究迅速增长，这些研究渴望揭开正念和冥想的“黑盒子”。对传统数据分析方法的依赖阻碍了对冥想状态的神经电生理功能的复杂性、非线性、多维性和系统性的理解。方法为了揭示冥想复杂的系统神经电生理，我们应用机器学习极端梯度增强分类算法和4种互补特征重要性方法，从26名有经验的冥想者在集中注意力冥想和控制走神状态下的脑电图记录中提取表征正念状态的系统脑电图特征。结果该算法对冥想状态和走神状态的分类准确率为83%，受试者工作特征曲线下面积为79%，F1得分为74%。特征重要性技术确定了10个脑电图特征，这些特征与集中注意力冥想期间高频振荡的强度和一致性增加有关，相对于指示的走神状态。结论：这些发现有助于描述冥想所特有的复杂的系统性振荡活动。

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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 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.