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Related Experiment Video

Updated: Jul 7, 2026

Chronic Intermittent Ethanol Vapor Exposure Paired with Two-Bottle Choice to Model Alcohol Use Disorder

Chronic Intermittent Ethanol Vapor Exposure Paired with Two-Bottle Choice to Model Alcohol Use Disorder

Published on: June 23, 2023

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

R Palaniappan¹, P Raveendran, S Omatu

¹Fac. of Eng., Malaya Univ., Kuala Lumpur.

IEEE Transactions on Neural Networks

|February 5, 2008

Summary

This study uses neural networks to classify alcoholics and non-alcoholics based on visual evoked potential (VEP) data. A genetic algorithm (GA) efficiently identifies optimal VEP channels for accurate classification.

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

Neuroscience
Machine Learning
Biomedical Engineering

Background:

Alcoholism diagnosis relies on subjective assessments and lengthy evaluations.
Objective biomarkers for alcoholism detection are needed for early intervention.
Visual evoked potentials (VEPs) offer a potential objective measure.

Purpose of the Study:

To develop an efficient method for classifying alcoholics and non-alcoholics using VEP features.
To optimize the selection of VEP channels for improved classification accuracy.
To compare the training efficiency of different neural network models for this task.

Main Methods:

Feature extraction from visual evoked potential (VEP) signals.
Application of neural networks (NNs), specifically fuzzy ARTMAP (FA), for classification.
Utilization of a genetic algorithm (GA) for optimal VEP channel selection.

Main Results:

The genetic algorithm successfully identified a minimal set of VEP channels for classification.
Fuzzy ARTMAP (FA) demonstrated significantly faster training times compared to multilayer perceptron (MLP).
The selected optimal channels proved effective for both FA and MLP classifiers, indicating unbiased selection.

Conclusions:

A GA-FA approach efficiently selects optimal VEP channels for classifying alcoholics.
FA offers a computationally efficient alternative to MLP for GA-based VEP analysis.
The identified optimal VEP channels hold promise for future alcoholism classification applications.