Reggente Nicco, Kothe Christian, Brandmeyer Tracy, Hanada Grant, Simonian Ninette, Mullen Sean, Mullen Tim
Institute for Advanced Consciousness Studies, Santa Monica, California.
Intheon, San Diego, California.
Biol Psychiatry Glob Open Sci. 2024 Oct 16;5(1):100402. doi: 10.1016/j.bpsgos.2024.100402. eCollection 2025 Jan.
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).
Expert Vipassana meditators ( = 34) participated in 2 separate sessions. Participants reported their meditative depth on a personally defined 1 to 5 scale using both traditional probing and a novel spontaneous emergence method. EEG activity and effective connectivity in theta, alpha, and gamma bands were 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 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.
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.
冥想练习已显示出诸多心理和生理益处，但捕捉不同冥想深度的神经关联仍具有挑战性。在本研究中，我们旨在使用脑电图（EEG）对专家冥想者自我报告的随时间变化的冥想深度进行解码。
34名内观冥想专家参加了2次独立的 sessions。参与者使用传统探测法和一种新颖的自发出现法，在个人定义的1至5级量表上报告他们的冥想深度。使用theta、alpha和gamma波段的EEG活动及有效连接性，通过机器学习/深度学习来预测冥想深度，包括一种融合源活动和连接性信息的新方法。
我们在解码跨未见 sessions 的自我报告冥想深度方面取得了显著的准确率。与传统探测法相比，自发出现法产生了更好的解码性能，并且与 session 后结果测量的相关性更强。通过一种融合空间、频谱和连接性信息的新型机器学习方法实现了最佳性能。传统的EEG通道级方法和预先选定的默认模式网络区域在捕捉与不同冥想深度相关的复杂神经动力学方面表现欠佳。
本研究证明了使用EEG解码个人定义的冥想深度的可行性。研究结果突出了冥想过程中神经活动的复杂、多变量性质，并引入自发出现作为一种生态有效且干扰性较小的经验采样方法。这些结果对推进神经反馈技术和增进我们对冥想练习的理解具有启示意义。
