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

Ameri, Rasool and Pouyan, Ali and Abolghasemi, Vahid (2016) 'Projective dictionary pair learning for EEG signal classification in brain computer interface applications.' Neurocomputing, 218. pp. 382-389. ISSN 0925-2312

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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.

Item Type: Article
Uncontrolled Keywords: Brain computer interface; Electroencephalogram; Dictionary pair learning; Sparse representation classification
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 23 Apr 2020 13:45
Last Modified: 15 Jan 2022 01:28

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