Meditation Styles Are Highly Discriminable from EEG at the Subject Level With Limited Generalization Across the Population: A Machine-Learning Study
bioRxiv (Cold Spring Harbor Laboratory) May 19, 2026 Saqib Hayat, Francesco Goretti, Rachele Fabbri et al.
EEG-based machine learning can reliably distinguish between three meditation styles (Shamatha, Vipassana, and Metta) and mind-wandering in experienced meditators when models are trained on an individual's own data, achieving high intra-subject accuracy. However, performance drops substantially when models are applied across different people, especially for distinguishing meditation styles, due to large inter-individual variability in meditation-related EEG patterns. Neural distinctions between meditation states become more pronounced over time. These findings support personalized EEG-based assessment of meditative states but highlight the difficulty of creating generalizable, subject-independent classification systems.