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Enhancing Performance of SVM-Based Brain-Computer Interface Systems

The performance of a brain-computer interface (BCI) system depends on data pre-processing,
 feature extraction and classification methods used to build the classifier in that BCI system. Support
 vector machine (SVM) has been proven as a powerful classifier for solving BCI classification problems,
 however current SVM methods are sensitive to noisy data or outliers. In this paper, we propose a new
 fuzzy approach to SVM-based BCI systems to enhance their classification performance. The proposed fuzzy
 SVM method has been successfully applied to other pattern classification problems. The data set III for
 motor imagery problem in BCI Competition II is used to evaluate our proposed method. Experimental
 results show that our proposed method can improve classification accuracy in experiments performed on
 a single data set with cross validation test and on two separate training and test data sets.