An optimized feature selection and classification method for using electroencephalographic coherence in brain-computer interfaces

We propose a method to use electroencephalographic (EEG) coherences as features in a brain-computer interface (BCI). 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 an optimized linear discriminant, where the best discriminating hyperplanes are selected such that the area under the receiver operating characteristics (ROC) curve is maximized. Overall, the proposed EEG coherence selection and classification method can provide efficiency rates similar to those obtained with other methods in BCI, but with the advantage of blindly selecting and optimal combination of features out of all the possible pairwise coherences. We demonstrate the applicability of the proposed method through numerical examples using real data from motor and cognitive tasks.