Electroencephalogram signal classification for brain computer interfaces using wavelets and support vector machines
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چکیده
An electroencephalogram (EEG) signal classification procedure for use in real-time synchronous brain computer interfaces (BCI)is proposed. The features used to perform the classification consist in the coefficients of a discrete wavelet transform (DWT) computed for each trial. A support vector machine (SVM) algorithm has been applied to classify the resultant feature vectors. Some experimental results obtained from the experimental application of the proposed procedure to the classification of two mental states are presented.
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تاریخ انتشار 2007