Feature extraction for multi-class BCI using EEG coherence

We propose a feature extraction method for multi-class electroencephalographic (EEG) signals based on their pairwise coherences. The coherence provides a sense of the brain's connectivity, and it is relevant as different regions of the brain must communicate between each other for the integration of sensory information. In our case, the process of feature selection is optimized in the sense that only those statistically significant and potentially discriminative coherences at a specific frequency are used, which results in a feature vector of reduced-dimension. Next, those features are classified through Mahalanobis distance classifier and the performance is evaluated by the kappa coefficient. The proposed EEG coherence selection and classification method can provide good efficiency rates, and with the advantage of selecting an optimal combination of features without the need of prior knowledge about the mental task. We demonstrate the applicability of the proposed method through numerical examples using real EEG data from cognitive tasks.