A Sparse Common Spatial Pattern Algorithm for Brain-Computer Interface

Common spatial pattern (CSP) algorithm and principal component analysis (PCA) are two commonly used key techniques for EEG component selection and EEG feature extraction for EEG-based braincomputer interfaces (BCIs). However, both the ordinary CSP and PCA algorithms face a loading problem, i.e., their weights in linear combinations are non-zero. This problem makes a BCI system easy to be over-fitted during training process, because not all of the information from EEG data are relevant to the given tasks. To deal with the loading problem, this paper proposes a spare CSP algorithm and introduces a sparse PCA algorithm to BCIs. The performance of BCIs using the proposed sparse CSP and sparse PCA techniques is evaluated on a motor imagery classification task and a vigilance estimation task. Experimental results demonstrate that the BCI system with sparse PCA and sparse CSP techniques are superior to that using the ordinary PCA and CSP algorithms.