Subject-independent Classification of Meditative State from the Resting State using EEG
arXiv Preprint Archive April 25, 2025 Jerrin Thomas Panachakel, G. Pradeep Kumar, Suryaa Seran et al.
Three machine-learning architectures distinguished Rajyoga meditation from resting brain states using EEG data, with the goal of subject-independent classification. The CSP-LDA-LSTM architecture achieved 98.2% accuracy for intra-subject classification, while the SVD-NN architecture reached 96.4% accuracy for inter-subject classification, comparable to the best reported intra-subject results. Both architectures captured subject-invariant EEG features, indicating robustness and ability to generalize across different subjects.