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Please use this identifier to cite or link to this item: http://dspace.unimap.edu.my:80/xmlui/handle/123456789/20725

Title: EEG based detection of alcoholics using spectral entropy with neural network classifiers
Authors: Padma Shri, T. K.
Sriraam, N.
???metadata.dc.contributor.url???: padma.shri@manipal.edu
Keywords: Alcoholics;Classifier;Electroencephalogram (EEG);Gamma band;Neural network;Spectral entropy;Visually evoked potentials (VEP)
Issue Date: 27-Feb-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: p. 89-93
Series/Report no.: Proceedings of the International Conference on Biomedical Engineering (ICoBE 2012)
Abstract: 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.
Description: Link to publisher's homepage at http://ieeexplore.ieee.org/
URI: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6178961
ISBN: 978-145771989-9
Appears in Collections:Conference Papers

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