Classification of Mental Task EEG Signals Using Wavelet Packet Entropy and SVM

摘要：

This paper address on the classification of mental task EEG signals, which is one of the key issues of Brain-Computer Interface (BCI). We proposed a method using wavelet packet entropy and Support Vector Machine (SVM). First, we apply 7 levels wavelet packet decomposition to each channel of EEG with db4. After extraction four spectrum bands ( δ, θ,α,β ), an entropy algorithm was performed on each bands. The resulting entropy vectors are then used as inputs to SVM to train and test. We test the method on EEG signals during 5 mental tasks collected by 2 subjects. The accuracy on 2-class calssification for subject 1 is averaged 93.0％, and 87.5％ for subject 2. The results also show that our method outperforms the classical methods for multi-class problems.

关键词： EEG mental task wavelet packet entropy SVM classification

作者: Li Zhiwei Shen Minfen

作者单位: Key Lab of Digital Signal and Image Processing of Guangdong Province,Shantou University 515063 China

会议类型: 国际会议

会议名称: 第八届国际电子测量与仪器学术会议(Proceedings of 2007 8th International Conference on Electronic Measurement & Instruments)

会议地点: 西安

会议语种:英文

在线出版日期: 2007-08-16（万方平台首次上网日期，不代表论文的发表时间）