Research on the Classification of Brain Function Based on SVM
In order to improve the accuracy in classification of Electroencephalograph (EEG) signals of different brain functions, the research of how to select the suitable classifier is carried out in the paper. Some experiments have been performed to select the suitable kernel function of support vector machine (SVM). Four different kernel functions are put into the comparison and the results show that the Radial Basis Function (RBF) has the highest rate of correct classification (RCC). On the other hand, the EEG signals from different electrodes will lead different classification results. The study of selecting the suitable electrode has been done. It shows that the RCC of the signal which from the electrode near by the area where EEG signal of a certain brain function is generated is much higher than those far from. We increase the dimension of SVM through combine the signal of different channels, which the RCC is very low, to improve the RCC of the signal which far from the area of the certain brain function. The results of our experiments are satisfied. The RCC of the EEG signal can reached to 99％.
Support vector machine(SVM) Kernel function EEG signals Brain function Rate of correct Clasification(RCC)
Xie Song-yun Wang Peng-wei Zhang Hai-jun Zhao Hai-tao
School of Electronics and Information Northwestern Polytechnical University Xian 710072China The First Accessorial Hospital of the Fourth Military Medical University Xian 710072China
国际会议
上海
英文
1931-1934
2008-05-16（万方平台首次上网日期，不代表论文的发表时间）