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

Authors:
/ Siuly;Yan Li;Peng Wen
Affiliations:
Department of Mathematics and Computing, University of Southern Queensland, Toowoomba, QLD 4350, Australia/ Centre for Systems Biology, University of Southern Queensland, Toowoomba, QLD 4350, Aust ...;Department of Mathematics and Computing, University of Southern Queensland, Toowoomba, QLD 4350, Australia/ Centre for Systems Biology, University of Southern Queensland, Toowoomba, QLD 4350, Aust ...;Faculty of Engineering and Surveying, University of Southern Queensland, Toowoomba, QLD 4350, Australia/ Centre for Systems Biology, University of Southern Queensland, Toowoomba, QLD 4350, Austral ...
Venue:
International Journal of Bioinformatics Research and Applications
Year:
2013

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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.