C.D. Lopes, M.A. Zaro, and A.A. Susin (Brazil)
EEG, Artificial Neural Network, Wavelet Transform, Alcoholism.
In this study, Wavelet Transform (WT) is used to analyze EEG data as an input to a feed forward neural network for signal classification of individuals at high risk (HR) for alcoholism. We used two types of mother wavelets for the matheematical processing of EEG data: (a) Biorthogonal (Bior) and (b|) Daubechies (Db). The results show that the wavelet transform can be used to provide a better classification by artificial neural network (ANN). Both ANN, trained with wavelet coefficients of Bior and Db, provided good performances (70%) in the classification task.
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