Kernel Based CSP Algorithm for Classifying Multi-class EEG Data by Combining K-means Clustering and Nystrom Approximation

Abstract

Because Common Spatial Pattern (CSP) algorithm in brain-computer interface assumes that the relation between the observed EEG signal and the brain source signal is strictly linear and very limited nonlinear kernel functions exist, both linear CSP and nonlinear kernel CSP can not characterize the brain pattern accurately. In this paper, a novel hybrid kernel CSP algorithm is proposed in order to improve the performance of pattern classification, which is based on the combination of linear and nonlinear kernel function. K-means clustering and Nystrom approximation were used to relieve the computational complexity and the requirement for memory in eigenvalue decomposition of kernel matrix. The band pass filtered EEG data were clustered for dimensionality reduction and then Nystrom approximation was performed to ensure the validity of low rank decomposition of kernel matrix in a high dimensional feature space. The performance of the algorithm was tested on a four-class dataset and compared with that of linear and kernel CSP. Using a pairwise classification strategy, the proposed algorithm achieved superior performance in terms of classification accuracy.