Classification of ECoG patterns related to finger movements with wavelet based SVM methods

Classification of finger movement related to (electrocorticography) ECoG records is the main purpose of this study. Data set IV presented in BCI Competition IV was used in this study. This data set contains brain signals from three epileptic subjects and the data records consist of both ECoG and electronic glove data. ECoG segments related finger movements were extracted by means of finger movement records generated by electronic glove. Features of segments with different lengths were extracted using wavelets and the channels having high performance were determined. The coefficients were classified with Support Vector Machine (SVM) classifier. The mean performances of three subjects were obtained as follows; classification rate 91.76% for two fingers, classification rate 76.16% for three fingers, classification rate 61.34% for four fingers and classification rate 48.51% for five fingers.