EEG-based meditation decoding: tackling subject variability with spatial and temporal alignment.
Journal of Neural Engineering December 22, 2025 Angeliki-Ilektra Karaiskou, Carolina Varon, Cem Ates Musluoglu et al.
Combining spatial and spectral alignment of EEG signals improves the classification of meditation versus rest states in new subjects without retraining. The Riemannian Space Data Alignment (RSDA) method adjusts brain activity patterns across electrodes, while Convolutional Monge Mapping Normalization (CMMN) aligns brain rhythms across frequencies. Together, they raised leave-one-subject-out classification accuracy to 66.6%, compared to 55.7% for non-aligned data and 59.6% for z-score normalization alone. Theta, Alpha, and Beta frequency bands contributed consistently, and Frontopolar and Temporal brain regions were key for distinguishing mental states. This approach offers a practical step toward calibration-free neurofeedback systems.