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
Official URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arn...
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. |
| Faculty / Department / School: | Historic - Faculty of Sciences - Department of Maths and Computing |
| Date Deposited: | 25 Oct 2012 05:18 |
| Last Modified: | 13 Jun 2016 05:06 |
| 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 : | 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: | E Expanding Knowledge > 97 Expanding Knowledge > 970110 Expanding Knowledge in Technology |
| Identification Number or DOI: | 10.1109/TNSRE.2012.2184838 |
| URI: | http://eprints.usq.edu.au/id/eprint/21578 |
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