VEP optimal channel selection using genetic algorithm for neural network classification of alcoholics

Ramaswamy Palaniappan, Paramesran Raveendran, Sigeru Omatu

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44 Citations (Scopus)


In this letter, neural networks (NNs) classify alcoholics and nonalcoholics using features extracted from visual evoked potential (VEP). A genetic algorithm (GA) is used to select the minimum number of channels that maximize classification performance. GA population fitness is evaluated using fuzzy ARTMAP (FA) NN, instead of the widely used multilayer perceptron (MLP). MLP, despite its effective classification, requires long training time (on the order of 103 times compared to FA). This causes it to be unsuitable to be used with GA, especially for on-line training. It is shown empirically that the optimal channel configuration selected by the proposed method is unbiased, i.e., it is optimal not only for FA but also for MLP classification. Therefore, it is proposed that for future experiments, these optimal channels could be considered for applications that involve classification of alcoholics.

Original languageEnglish
Pages (from-to)486-491
Number of pages6
JournalIEEE Transactions on Neural Networks
Issue number2
Publication statusPublished - Mar 2002
Externally publishedYes


  • Alcoholism
  • Digital filter
  • Fuzzy ARTMAP (FA)
  • Multilayer perceptron (MLP)
  • Visual evoked potential (VEP)

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