Canonical correlation approach to common spatial patterns

Abstract

Common spatial patterns (CSPs) are a way of spatially filtering EEG signals to increase the discriminability between the filtered variance/power between the two classes. The proposed canonical correlation approach to CSP (CCACSP) utilizes temporal information in the time series, in addition to exploiting the covariance structure of the different classes, to find filters which maximize the bandpower difference between the classes. We show with simulated data, that the unsupervised canonical correlation analysis (CCA) algorithm is better able to extract the original class-discriminative sources than the CSP algorithm in the presence of large amounts of additive Gaussian noise (while the CSP algorithm is better at very low noise levels) and that our CCACSP algorithm is a hybrid, yielding good performance at all noise levels. Finally, experiments on data from the BCI competitions confirm the effectiveness of the CCACSP algorithm and a merged CSP/CCACSP algorithm (mCCACSP).