Projective dictionary pair learning for EEG signal classification in brain computer interface applications

Electroencephalogram (EEG) based brain-computer interface (BCI) is a useful communication tool between human brain and external devices. Accurate and effective EEG classification plays an important role in performance of BCI applications. In this paper, we propose a dictionary pair learning (DPL) method for EEG signal classification. In this method, we can learn a dictionary without costly L0 and L1 calculation and sparse coefficients have been calculated by linear projection instead of nonlinear sparse coding. We analyzed the performance of new method using EEG data from IIIa and IVa databases of BCI competition III. Experimental results showed that proposed method provides higher classification performance compared with other dictionary learning methods such as label consistent K Singular value decomposition (LC-KSVD). Based on our results, accuracy rates are as follows: 81.25%, 100%, 60.2%, 83.04% and 79.37% for subjects aa, al, av, aw and ay, respectively from IVa database. Also, the average accuracy rate of 85.7% has been achieved for two-class classification of IIIa database. Using Dictionary learning for EEG classification.Using analysis-synthesis dictionaries to avoid calculate L0 or L1.Superior performance compared to existing methods.