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
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Identification Number or DOI: doi: 10.1109/TNSRE.2012.2184838
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 classication. In this paper, we propose a hybrid algorithm to improve the classication success rate of MI-based electroencephalogram (EEG) signals in BCIs. The proposed scheme develops a novel cross-correlation based feature extractor, which is aided with a least square support vector machine (LS-SVM) for two-class MI signals recognition. To verify the effectiveness of the proposed classier, we replace the LS-SVM classier by a logistic regression classier and a kernel logistic regression classier, separately, with the same features extracted from the cross-correlation technique for the classication. The proposed approach is tested on datasets, IVa and IVb of BCI Competition III. The performances of those methods are evaluated with classication accuracy through a 10-fold cross-validation procedure. We also assess the performance of the proposed method by comparing it with eight recently reported algorithms. Experimental results on the two datasets show that the proposed LS-SVM classier provides an improvement compared to the logistic regression and kernel logistic regression classiers. The results also indicate that the proposed approach outperforms the most recently reported eight methods and achieves a 7.40% improvement over the best results of the other eight studies.
|Item Type:||Article (Commonwealth Reporting Category C)|
|Additional Information:||Permanent restricted access to published version due to publisher copyright policy.|
|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|
|Deposited On:||25 Oct 2012 15:18|
|Last Modified:||21 Feb 2013 13:21|
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