Classification Method of EEG Signals Based on Wavelet Neural Network

摘要：

A new wavelet neural network （ WNN ） is constructed combining wavelet transform and neural network theory to classify electroencephalogram (EEG) signals. The new WNN takes nonlinear mother wavelet as neuron instead of traditional nonlinear sigmoid function. It owns the merits of good generalization ability and high converging speed. In addition, multi-resolution and self-adaptation are also its advantages. Experimental results have shown that our method performs well for the classification of mental tasks from EEG data compared with the approaches based on traditional neural network. It can provide a new way for the EEG automation classification when the EEG is used as input signal to a brain computer interface (BCI).

关键词： Classification method Electroencephalogram Wavelet neural network

作者: Sun Hongyu Xiang Yang Guo Yinjing

作者单位: School of Electronics and Information Engineering Tongji University Shang Hai,China College of Infor School of Electronics and Information Engineering Tongji University Shang Hai,China College of Information and Electrical Engineering Shandong University of Science and Technology Qing

会议类型: 国际会议

会议名称: The 3rd International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2009)(第三届生物信息与生物医学工程国际会议)

会议地点: 北京

会议语种:英文

页码: 1-4

在线出版日期: 2009-06-11（万方平台首次上网日期，不代表论文的发表时间）