Multi-frequency band common spatial pattern with sparse optimization in Brain-Computer Interface

In motor imagery-based Brain Computer Interfaces (BCIs), Common Spatial Pattern (CSP) algorithm is widely used for extracting discriminative patterns from the EEG signals. However, the CSP algorithm is known to be sensitive to noise and artifacts, and its performance greatly depends on the operational frequency band. To address these issues, this paper proposes a novel Sparse Multi-Frequency Band CSP (SMFBCSP) algorithm optimized using a mutual information-based approach. Compared to the use of the cross-validation-based method which finds the regularization parameters by trial and error, the proposed mutual information-based approach directly computes the optimal regularization parameters such that the computational time is substantially reduced. The experimental results on 11 stroke patients showed that the proposed SMFBCSP significantly outperformed three existing algorithms based on CSP, sparse CSP and filter bank CSP in terms of classification accuracy.