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

摘要

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 Ilia and IVa databases of BCI competition III. Experimental results showed that proposed method provides higher classification per: formance 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 7937% 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. (C) 2016 Elsevier B.V. All rights reserved.