Title :
Classification Method of EEG Signals Based on Wavelet Neural Network
Author :
Sun Hongyu ; Xiang Yang ; Guo Yinjing
Author_Institution :
Sch. of Electron. & Inf. Eng., Tongji Univ., Shanghai, China
Abstract :
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).
Keywords :
brain-computer interfaces; electroencephalography; medical signal processing; neural nets; neurophysiology; signal classification; signal resolution; wavelet transforms; EEG signal classification; brain computer interface; electroencephalogram; mental task classification; neural network theory; nonlinear mother wavelet; signal multiresolution; wavelet neural network; wavelet transform; Artificial neural networks; Biological neural networks; Brain computer interfaces; Educational institutions; Electrodes; Electroencephalography; Humans; Neural networks; Signal processing; Wavelet transforms;
Conference_Titel :
Bioinformatics and Biomedical Engineering , 2009. ICBBE 2009. 3rd International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-2901-1
Electronic_ISBN :
978-1-4244-2902-8
DOI :
10.1109/ICBBE.2009.5162503