Motor imagery based BCI classification via sparse representation of EEG signals

Electroencephalogram (EEG) based brain-computer interface (BCI) provides a new communication and control channel for people with severe motor disabilities. Motor imagery based sensorimotor rhythm (SMR) analysis is one of the widely used methods in the BCI field. However, these motor imagery signals are very noisy and strongly depends on subjects. Therefore, it is difficult to classify them and thus more powerful classification methods are needed. In this paper, we propose a new classification method based on sparse representation of EEG signals and ell-1 minimization. Using Mu and/or Beta rhythm as EEG features, we evaluate the performance of the proposed method with four data sets. Moreover, we make performance comparison with the linear discriminant analysis (LDA), another classification method. From the results, our proposed method shows the better classification accuracy.