Cross-Subject Mindfulness Meditation EEG Decoding
2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering October 25, 2023 Angeliki-Ilektra Karaiskou, C. Varon, K. Alaerts et al.
Decoding meditation states from EEG signals across different people remains challenging due to individual brain differences. This work compares feature engineering methods with a deep learning approach (EEGNet) for cross-subject mindfulness meditation decoding, using EEG recordings from novice and expert meditators. Riemannian Space Data Alignment (RSDA) was applied session-wise and per subject to reduce variability. EEGNet applied to RSDA-aligned signals achieved the highest decoding performance with the shortest time segments, effectively extracting relevant features for meditation state classification. These findings suggest that EEGNet can enable more effective, calibration-free neurofeedback applications for meditation.