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

Palaniappan, Ramaswamy, Paramesran, R. (2002) Using genetic algorithm to identify the discriminatory subset of multi-channel spectral bands for visual response. Applied Soft Computing, 2 (1). pp. 48-60. ISSN 1568-4946. (doi:10.1016/S1568-4946(02)00028-5) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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Official URL
https://doi.org/10.1016/S1568-4946(02)00028-5

Abstract

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.

Item Type: Article
DOI/Identification number: 10.1016/S1568-4946(02)00028-5
Additional information: Unmapped bibliographic data: LA - English [Field not mapped to EPrints] J2 - Appl. Soft Comput. J. [Field not mapped to EPrints] AD - Dept. of Elec. and Telecommunication, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia [Field not mapped to EPrints] DB - Scopus [Field not mapped to EPrints] M3 - Article [Field not mapped to EPrints]
Uncontrolled keywords: Alcoholics, Feature reduction, Fuzzy ARTMAP, Gamma band, Genetic algorithm, IIR digital filter, Multilayer perceptron-backpropagation, Visual evoked potential, Alcohols, Backpropagation, Bandwidth, Brain, Set theory, Theorem proving, Alcoholics, Feature reduction, Fuzzy ARTMAP, Gamma band, IRR digital filter, Multilayer perceptron-backpropogation, Visual evoked potential, Genetic algorithms
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Data Science
Depositing User: Palaniappan Ramaswamy
Date Deposited: 15 Dec 2018 20:15 UTC
Last Modified: 30 May 2019 08:29 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/70760 (The current URI for this page, for reference purposes)
Palaniappan, Ramaswamy: https://orcid.org/0000-0001-5296-8396
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