Decoding Depth of Meditation: Electroencephalography Insights From Expert Vipassana Practitioners
Biological Psychiatry Global Open Science October 17, 2024 Nicco Reggente, Christian Kothe, Tracy Brandmeyer et al. 15 citations
Decoding self-reported meditative depth from EEG recordings is feasible. Expert Vipassana meditators (34 people) reported their depth on a 1–5 scale during two sessions, using either traditional probing or a novel spontaneous emergence method. Machine learning models fused spatial, spectral, and connectivity information from theta, alpha, and gamma bands to predict depth across unseen sessions. The spontaneous emergence method produced more frequent reports and correlated better with post-session outcomes than probing. No single EEG channel or default mode network region captured the complex neural dynamics; multivariate patterns were necessary. The findings suggest potential improvements for neurofeedback in meditation.