Improving the separability of motor imagery EEG signals using a cross correlation-based least square support vector machine for brain–computer interface

Siuly, and Li, Yan (2012) Improving the separability of motor imagery EEG signals using a cross correlation-based least square support vector machine for brain–computer interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 20 (4). pp. 526-538. ISSN 1534-4320

Abstract

Although brain–computer interface (BCI) techniques have been developing quickly in recent decades, there still exist a number of unsolved problems, such as improvement of motor imagery (MI) signal classi


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to published version due to publisher copyright policy.
Depositing User: Mrs . Siuly
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 25 Oct 2012 05:18
Last Modified: 21 Jul 2014 05:31
Uncontrolled Keywords: brain–computer interface (BCI); cross-correlation technique; electroencephalogram (EEG); feature extraction; kernel logistic regression; least square support vector machine (LS-SVM); logistic regression; motor imagery (MI)
Fields of Research (FOR2008): 08 Information and Computing Sciences > 0806 Information Systems > 080602 Computer-Human Interaction
10 Technology > 1004 Medical Biotechnology > 100402 Medical Biotechnology Diagnostics (incl. Biosensors)
09 Engineering > 0903 Biomedical Engineering > 090399 Biomedical Engineering not elsewhere classified
Socio-Economic Objective (SEO2008): E Expanding Knowledge > 97 Expanding Knowledge > 970110 Expanding Knowledge in Technology
Identification Number or DOI: doi: 10.1109/TNSRE.2012.2184838
URI: http://eprints.usq.edu.au/id/eprint/21578

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