Fuzzy support vector machine for classification of EEG signals using wavelet-based features
Q Xu, H Zhou, Y Wang, J Huang�- Medical engineering & physics, 2009 - Elsevier
Translation of electroencephalographic (EEG) recordings into control signals for brain–
computer interface (BCI) systems needs to be based on a robust classification of the various
types of information. EEG-based BCI features are often noisy and likely to contain outliers.
This contribution describes the application of a fuzzy support vector machine (FSVM) with a
radial basis function kernel for classifying motor imagery tasks, while the statistical features
over the set of the wavelet coefficients were extracted to characterize the time–frequency�…
computer interface (BCI) systems needs to be based on a robust classification of the various
types of information. EEG-based BCI features are often noisy and likely to contain outliers.
This contribution describes the application of a fuzzy support vector machine (FSVM) with a
radial basis function kernel for classifying motor imagery tasks, while the statistical features
over the set of the wavelet coefficients were extracted to characterize the time–frequency�…
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