Mutual information-based optimization of sparse spatio-spectral filters in brain–computer interface

Recently, neuro-rehabilitation based on brain–computer interface (BCI) has been considered one of the important applications for BCI. A key challenge in this system is the accurate and reliable detection of motor imagery. In motor imagery-based BCIs, the common spatial patterns (CSP) algorithm is widely used to extract discriminative patterns from electroencephalography signals. However, the CSP algorithm is sensitive to noise and artifacts, and its performance depends on the operational frequency band. To address these issues, this paper proposes a novel optimized sparse spatio-spectral filtering (OSSSF) algorithm. The proposed OSSSF algorithm combines a filter bank framework with sparse CSP filters to automatically select subject-specific discriminative frequency bands as well as to robustify against noise and artifacts. The proposed algorithm directly selects the optimal regularization parameters using a novel mutual information-based approach, instead of the cross-validation approach that is computationally intractable in a filter bank framework. The performance of the proposed OSSSF algorithm is evaluated on a dataset from 11 stroke patients performing neuro-rehabilitation, as well as on the publicly available BCI competition III dataset IVa. The results show that the proposed OSSSF algorithm outperforms the existing algorithms based on CSP, stationary CSP, sparse CSP and filter bank CSP in terms of the classification accuracy, and substantially reduce the computational time of selecting the regularization parameters compared with the cross-validation approach.