A single session of EEG-neurofeedback training can increase the occurrence of non-harmonic alpha-theta brainwave patterns during focused attention meditation, an effect that persists after training ends. Thirty participants received 25 minutes each of experimental training (auditory feedback when non-harmonic alpha-theta patterns were detected) and sham training (unrelated feedback). The increase in these brainwave patterns occurred across the whole scalp, not just at the training site, and was linked to improvements in depressive mood. People with higher depressive mood at the start showed stronger training effects. The findings suggest neurofeedback could help train brainwave patterns relevant to meditation, especially for those with depressive symptoms.
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.