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�������պ����ѧѧ�� 2005, Vol. 31 Issue (10) :1140-1144    DOI:
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Method of EEG signals classification based on wavelet transform and neural networks
Mao Xia, Meng Qingyu*
School of Electronics and Information Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China

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Abstract�� Electroencephalography (EEG) signals of alcoholic subjects and control subjects were classified by combination of wavelet transforms and neural networks. Classification features were discovered through the EEG data analysis. The frequency bands of EEG signals including classification features were extracted by 1-D wavelet transforms. The decomposed coefficients of wavelet transforms were remained as signals characters to accomplish the length compression of data sequences. Three learning vector quantization (LVQ) networks with same structure corresponding to three kinds of stimulations were built for the predictive classification of the EEG signals. The final classification results were acquired by judge rules. The classification accuracy of experiment EEG signals reach 89%.
Keywords�� electroencephalography   wavelet transforms   neural networks     
Received 2004-11-12;


About author: ë Ͽ(1952-),Ů,�㽭������,����, moukyou@buaa.edu.cn.
ëϿ, ������.����С���任����������Ե��źŷ��෽��[J]  �������պ����ѧѧ��, 2005,V31(10): 1140-1144
Mao Xia, Meng Qingyu.Method of EEG signals classification based on wavelet transform and neural networks[J]  JOURNAL OF BEIJING UNIVERSITY OF AERONAUTICS AND A, 2005,V31(10): 1140-1144
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