Title: Identification of motor imagery tasks through CC-LR algorithm in brain computer interface

Authors: Siuly; Yan Li; Peng Wen

Addresses: Department of Mathematics and Computing, University of Southern Queensland, Toowoomba, QLD 4350, Australia; Centre for Systems Biology, University of Southern Queensland, Toowoomba, QLD 4350, Australia ' Department of Mathematics and Computing, University of Southern Queensland, Toowoomba, QLD 4350, Australia; Centre for Systems Biology, University of Southern Queensland, Toowoomba, QLD 4350, Australia ' Faculty of Engineering and Surveying, University of Southern Queensland, Toowoomba, QLD 4350, Australia; Centre for Systems Biology, University of Southern Queensland, Toowoomba, QLD 4350, Australia

Abstract: This study focuses on the identification of Motor Imagery (MI) tasks for the development of Brain Computer Interface (BCI) technologies combining Cross-Correlation and Logistic Regression (CC-LR) techniques. The proposed method is tested on two benchmark data sets, IVa and IVb of BCI Competition III, and the performance is evaluated through a 3-fold cross-validation procedure. The experimental outcomes are compared with two recently reported algorithms, R-Common Spatial Pattern (CSP) with aggregation and Clustering Technique (CT)-based Least Square Support Vector Machine (LS-SVM) and also other four algorithms using data set IVa. The results demonstrate that our proposed method results in an improvement of at least 3.47% compared with the existing methods tested.

Keywords: BCI; brain computer interface; EEG; electroencephalograms; motor imagery tasks; cross-correlation; logistic regression; feature extraction; task identification; bioinformatics; common spatial patterns; clustering; least squares SVM; support vector machines; SVM; LS-SVM.

DOI: 10.1504/IJBRA.2013.052447

International Journal of Bioinformatics Research and Applications, 2013 Vol.9 No.2, pp.156 - 172

Received: 22 Jun 2011
Accepted: 14 Jul 2011
Published online: 06 Sep 2014 *

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