A study of kernel CSP-based motor imagery brain computer interface classification

Abstract

The Common Spatial Patterns (CSP) method is a widely used spatial filtering technique that can extract discriminative features for Electroencephalogram (EEG)-based brain computer interface (BCI) classification tasks. Since the EEG signal acquired on the scalp is a nonlinear composition of multiple signal and noise sources, in order to characterize the nonlinear data structure, nonlinear CSP methods have been proposed by using the kernel technique. Most kernel CSP methods calculate temporal covariance structure in a kernel feature space that leads to a large kernel matrix with each dimension equal to the number of time points multiplied by the number of classes. In this work, a kernel CSP method exploiting spatial covariance structure in the feature space is developed where the size of kernel matrix is the number of EEG channels, which is usually much less than that of time points. The proposed method was evaluated using motor imagery EEG data. Results indicate that the kernel CSP using spatial analysis can provide comparable performance to the existing methods using temporal analysis with less computational load.