Modeling and Prediction of EEG Signal Using Support Vector Machine
Minfen Shen, Chun-Hao Lin, Jialiang Huang, Yanxun Li · 2008
Electroencephalogram (EEG) is widely regarded as chaotic signal. Modeling and prediction of EEG signals is important for many applications. The methods using support vectors machine (SVM) based on the structure risk minimization provides us an effective way of learning machine. The performance of SVM is much better than the traditional learning machine. Now the SVM is used in classification and regression. 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 predicting the signals. The local method is presented for improving the speed of the prediction of EEG signals. The simulation results show that the training of the local-SVM obtains a good behavior. In addition, the local SVM method significantly improves the prediction precision.