Comparisons between motor area EEG and all-channels EEG for two algorithms in motor imagery task classification

Siuly, and Li, Yan and Wen, Peng (Paul) (2014) Comparisons between motor area EEG and all-channels EEG for two algorithms in motor imagery task classification. Biomedical Engineering: Applications, Basis and Communications, 26 (3). pp. 1-10. ISSN 1016-2372

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Abstract

This article reports on a comparative study to identify electroencephalography (EEG) signals during motor imagery (MI) for motor area EEG and all-channels EEG in the brain–computer interface (BCI) application. In this paper, we present two algorithms: CC-LS-SVM and CC-LR for MI tasks classfication. The CC-LS-SVM algorithm combines the
cross-correlation (CC) technique and the least square support vector machine (LS-SVM). The CC-LR algorithm assembles the CC technique and binary logistic regression (LR) model. These two algorithms are implemented on the motor area EEG and the all-channels EEG to investigate how well they perform and also to test which area EEG is better for the MI classification. These two algorithms are also compared with some existing methods which reveal their competitive performance during classification. Results on both datasets, IVa and IVb from BCI Competition III, show that the CC-LS-SVM algorithm performs better than the CC-LR algorithm on both the motor area EEG and the all-channels EEG. The results also demonstrate that the CC-LS-SVM algorithm performs much better for the all-channels EEG than for the motor area EEG. Furthermore, the LS-SVM-based approach can correctly identify the discriminative MI tasks, demonstrating the algorithm's superiority in classfication performance over some existing methods.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2014 National Taiwan University. Accepted version deposited in accordance with the copyright policy of the publisher.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 08 Jan 2014 03:31
Last Modified: 28 Apr 2017 00:50
Uncontrolled Keywords: EEG; brain-computer interface; cross-correlation; electroencephalogram; least square support vector machine; logistic regression; motor imagery
Fields of Research : 10 Technology > 1004 Medical Biotechnology > 100402 Medical Biotechnology Diagnostics (incl. Biosensors)
11 Medical and Health Sciences > 1103 Clinical Sciences > 110320 Radiology and Organ Imaging
09 Engineering > 0903 Biomedical Engineering > 090399 Biomedical Engineering not elsewhere classified
Socio-Economic Objective: B Economic Development > 86 Manufacturing > 8615 Instrumentation > 861502 Medical Instruments
Identification Number or DOI: 10.4015/S1016237214500409
URI: http://eprints.usq.edu.au/id/eprint/24437

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