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

Palaniappan, Ramaswamy, Raveendran, P., Omatu, S. (2002) VEP optimal channel selection using genetic algorithm for neural network classification of alcoholics. IEEE Transactions on Neural Networks, 13 (2). pp. 486-491. ISSN 1045-9227. (doi:10.1109/72.991435) (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) (KAR id:70764)

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. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1109/72.991435

Abstract

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.

Item Type: Article
DOI/Identification number: 10.1109/72.991435
Additional information: Unmapped bibliographic data: LA - English [Field not mapped to EPrints] J2 - IEEE Trans Neural Networks [Field not mapped to EPrints] AD - Department of Electrical and Telecommunication, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia [Field not mapped to EPrints] AD - Department of Computer and System Science, College of Engineering, University of Osaka Prefecture, Sakai, Osaka 593, Japan [Field not mapped to EPrints] DB - Scopus [Field not mapped to EPrints] M3 - Article [Field not mapped to EPrints]
Uncontrolled keywords: Alcoholism, Digital filter, Fuzzy ARTMAP (FA), Multilayer perceptron (MLP), Visual evoked potential (VEP), Bioelectric potentials, Digital filters, Fuzzy sets, Genetic algorithms, Interfaces (computer), Multilayer perceptron (MLP), Visual evoked potential (VEP), Neural networks
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Data Science
Depositing User: Palaniappan Ramaswamy
Date Deposited: 15 Dec 2018 20:38 UTC
Last Modified: 30 May 2019 08:30 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/70764 (The current URI for this page, for reference purposes)
Palaniappan, Ramaswamy: https://orcid.org/0000-0001-5296-8396
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