Spatio-temporally regularized common spatial patterns (STR-CSP) for single-trial EEG classification

Common Spatial Patterns (CSP) is among the most popular spatial filtering methods used in motor imagery based brain-computer interfaces (BCIs). In this paper, a novel algorithm termed spatio-temporally regularized common spatial patterns (STR-CSP) is proposed to enhance CSP such that discriminative spatio-temporal EEG patterns can be extracted. Unlike most previous CSP-based spatio-temporal filtering algorithms in which spatial and temporal filters are optimized in a sequential manner, STR-CSP optimizes spatio-temporal filters simultaneously under a unified criterion with analytical solutions. Moreover, the algorithm enables us to alleviate the over-fitting problem by L2-norm regularization. Successful application to the analysis of a BCI competition EEG data set comprising 18 subsets demonstrates the superiority of STR-CSP over contemporary methods.