Meditation styles are highly discriminable from EEG at the subject level with limited generalization across the population: a machine-learning study

Year: 2026

Authors: Hayat S., Goretti F., Fabbri R., Noferini C., Cravero E., Mori P., Scaglione A., Pavone F.S.

Autors Affiliation: Univ Campus Biomed Roma, Rome, Italy; European Lab Nonlinear Spect LENS, Florence, Italy; Natl Res Council INO CNR, Natl Inst Opt, Sesto Fiorentino, Italy; Univ Florence, Dept Phys & Astron, Florence, Italy; Fdn Ric Contemplativa ETS, Castellina Marittima, Italy.

Abstract: Meditation has been associated with improvements in attention, emotional regulation, and mental wellbeing, motivating increasing interest in objective methods for assessing meditative states. In this study, we investigate whether EEG-based machine learning can reliably distinguish between multiple meditation styles and mind-wandering states. EEG data were recorded from experienced meditators performing three meditation styles, Shamatha, Vipassana, and Metta, together with an eyes-closed mind-wandering condition. EEG signals were preprocessed to remove artifacts, and features were extracted from frequency, time-frequency, and time domains. Classification was evaluated using both intra-subject and inter-subject strategies with multiple machine learning classifiers. Results demonstrate high intra-subject classification accuracy across meditation-vs.-mind-wandering and meditation-style comparisons, indicating strongly discriminative subject-specific neural signatures. In contrast, inter-subject performance decreased substantially, particularly for distinguishing meditation styles, suggesting considerable inter-individual variability in meditation-related EEG patterns. Furthermore, temporal analysis revealed that classification performance increases over time in some comparisons, particularly in VIP/MW, suggesting that the neural distinctions between certain meditation states may become more pronounced over time. Additionally, t-SNE visualization showed clear within-subject clustering but increased overlap across subjects, explaining the reduced inter-subject generalization. Overall, these findings highlight the potential of EEG-based machine learning for personalized assessment and monitoring of meditative states while emphasizing the challenges of developing subject-independent meditation classification systems.

Journal/Review: FRONTIERS IN ARTIFICIAL INTELLIGENCE

Volume: 9      Pages from: 1876322-1  to: 1876322-15

More Information: The author(s) declared that financial support was received for this work and/or its publication. This project received funding from the Bank Foundation Fondazione Cassa di Risparmio di Firenze grant Human Brain Optical Mapping (Grant recipient: FP, Grant number: CUP G95F21000150007). This research was supported by the Italian National Recovery and Resilience Plan (NRRP), M4C2, funded by the European Union – NextGenerationEU Project IR0000011, CUP B51E22000150006, EBRAINS-Italy and Project IR0000023, CUP B53C22001810006, SEE-LIFE.
KeyWords: classification; EEG; feature extraction; machine learning; meditation; Shamatha; Vipassana; Metta
DOI: 10.3389/frai.2026.1876322