Separable common spatio-spectral pattern algorithm for classification of EEG signals

This paper proposes a novel method for extraction of discriminant spatio-spectral EEG features in motor imagery brain-computer interfaces. Considering a heteroscedastic binary classification setup, this method extracts the spatio-spectral features whose variance is maximized for one brain task and minimized for the other task. Therefore, our method can be considered as a spatio-spectral generalization of the conventional common spatial patterns (CSP) algorithm. In comparison to the similar solutions in the literature, such as filter-bank CSP (FBCSP) method, the proposed method benefits from joint processing of both spatial and spectral features, which improves the overall performance of the BCI while reducing its computational cost. Furthermore, our algorithm provides a simple measure that allows for ranking the discriminant power of extracted spatio-spectral features, which is not possible in FBCSP method. The experimental results demonstrate that the proposed method outperforms FBCSP for both raw EEG and preprocessed EEG data.