Using genetic algorithm to identify the discriminatory subset of multi-channel spectral bands for visual response

In this paper, we propose a technique that uses genetic algorithm (GA) with Fuzzy ARTMAP (FA) classifier to identify the discriminatory subset of the feature set for classification of alcoholics and non-alcoholics using brain rhythm extracted during visual stimulus. In the experimental study, the feature set consists of seven spectral power ratios extracted from 61 visual evoked potential (VEP) channels. The seven spectral bands of VEP signals in the range of 2-50 Hz are extracted using constant gain and uniform bandwidth infinite impulse response (IIR) band-pass filters. Spectral power in these bands are obtained using Parseval's time-frequency energy equivalence theorem. The spectral power ratio for each band is obtained by dividing the spectral power of the band with the total spectral power of the channel. Classification experiments using FA and multilayer perceptron-backpropagation (MLP-BP) classifiers are carried out to confirm that the identified spectral power ratios and channels using the proposed technique are discriminatory. The classification results show that the difference of VEP signals between alcoholics and non-alcoholics can be observed using two spectral power ratios in gamma band (37-50 Hz) extracted from seven channels. This fact indicates that gamma band spectral power could be used to show evidence on the lasting effects of long-term use of alcohol on visual response though the studied alcoholics have been abstinent for a minimum period of 1 month.