Cart (Loading....) | Create Account
Close category search window
 

EEG based detection of alcoholics using spectral entropy with neural network classifiers

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Shri, P.T.K. ; SCSVMV Univ., Kanchi, India ; Sriraam, N.

This paper suggests the application of gamma band spectral entropy for the detection of alcoholics. First, the gamma sub band signals (30-50Hz) are extracted using an elliptic band pass filter of sixth order to extract the visually evoked potentials (VEP) signals. Prior to filtering, thresholds of 100μv are applied to the electroencephalogram (EEG) recordings in order to remove eye blink artefact. The power spectral densities (PSD's) of the gamma band are calculated using Periodogram and the gamma band spectral entropies are determined. These spectral entropy coefficients in the gamma band are used as features to classify the control subjects from their alcoholic counterparts using multilayer perceptron-back propagation (MLP-BP) and probabilistic neural network(PNN) classifiers. From the experimental study, it can be concluded that the PNN classifier performs better with a classification accuracy of ~99% (for a spread factor of <; 1) than MLP classifier.

Published in:

Biomedical Engineering (ICoBE), 2012 International Conference on

Date of Conference:

27-28 Feb. 2012

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.