Discriminative dictionary learning for EEG signal classification in Brain-computer interface

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Modern Brain-computer interface (BCI) technique is essentially based on the classification of the brain signals. The sparse representation classification (SRC) method has been studied for classifying EEG signals of the motor imagery based BCI. The dictionary used in the SRC method is the simple combination of feature vectors which are extracted from the EEG signal-trials by common spatial pattern (CSP) algorithm. In this paper, we propose a method to learn a new dictionary with smaller size and more discriminative ability for the classification. The proposed method, discriminative dictionary learning (DDL), is based on minimizing an objective function containing a reconstructive term and a discriminative term. We apply an iterative scheme to the optimization and transform it to a series of mixed ℓ1-ℓ2 optimizations, which are solved based on separable surrogate functions (SSF) technique. We evaluate the proposed method using the dataset from BCI competition III. The experimental results show that the proposed method outperforms the SRC method.