Prediction of cognitive states using MEG and Blind Source Separation

The present study investigates the predictability of a subject's state based on the classification of the underlying brain activity recorded via magnetoencephalography (MEG). We use Second Order Blind Identification (SOBI) to reduce the high dimensionality of MEG sensors into a smaller number of task-related components. A classification of distinct cognitive states is then achieved by feeding the spectral power of these components into a Support Vector Machine (SVM). We tested this approach on data from one subject during a visuomotor control experiment and found that our method outperforms classification based on the spectral powers computed directly from the MEG sensor array. Our findings suggest that combining SOBI and SVM may provide a reliable classifier for the prediction of cognitive states in MEG.