A Novel Approach for Analyzing EEG Signal Based on SVM
Min-fen Shen, Qiong Zhang, Lanxin Lin, L. Charles Sun · 2016
Accurate modeling of Electroencephalography (EEG) signals is an important problem in clinical diagnosis of brain diseases. The method using support vectors machine (SVM) based on the structure risk minimization provides us an effective way of learning machine. But solving the quadratic programming problem for training SVM becomes a bottle-neck of using SVM because of the long time of SVM training. In this paper, a local-SVM method is proposed for modeling EEG signals. The local method is presented for improving the speed of the prediction of EEG signals. The experimental results show that the training of the local-SVM obtains a good behavior.