Modeling and Prediction of EEG Signal Using Support Vector Machine

摘要：

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.

关键词： support vector machine prediction EEG local method

作者: Minfen Shen ChunHao Lin Jialiang Huang Yanxun Li

作者单位: College of Engineering, Shantou University Shantou, China

会议类型: 国际会议

会议名称: The 2nd International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2008)(第二届生物信息与生物医学工程国际会议)

会议地点: 上海

会议语种:英文

页码: 1988-1991

在线出版日期: 2008-05-16（万方平台首次上网日期，不代表论文的发表时间）