Application of SVM in EEG signal classification
支持向量机在脑电信号分类中的应用

At first some energy features related to the frequency bands and special distribution are extracted.Then a classifier based on Support Vector Machines(SVM) was designed.To optimize the classifier,different kernel functions and parameters were discussed.The performance of classifier was compared with a RBF Neural Network classifier.The result indicates that the ideal accuracy can be achieved by the SVM and wavelet energy method in EEG classification.The schizophrenia can be separated from healthy through 16-channel's EEG.The research takes important practical value in the schizophrenic diagnose.