Sparse Csp Algorithm Via Joint Spatio-Temporal Filtering

Common spatial pattern (CSP) is widely used in motor imagery classification tasks. Classical CSP depends only on spatial filters. To improve its performance, a novel and efficient spatio-temporal filtering strategy is proposed in this paper to extract discriminative features. Common temporal filters are shared among all the spatial channels, so as to reduce the overfitting risk in the case of a small sample size. An efficient alternating optimization algorithm is also developed to optimize coefficients of spatial and temporal filters. To alleviate adverse effects of noise and artifacts and improve implementation efficiency, an \ell_{1}-norm-based sparsity regularization term is further introduced. The resulting problem is tackled by the reweighting technique. The effectiveness of the proposed algorithm is validated by the experiments using open datasets of BCI Competition.