Detection of alcoholic impact on visual event related potentials using beta band spectral entropy, repeated measures ANOVA and k-NN classifier

N. Sriraam, T. K.Padma Shri

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

In this paper, the effect of alcohol on visual event related potentials (visual ERPs) of 61-channel electroencephalogram (EEG) is investigated using Spectral Entropy (SE) parameters in beta band (13-28Hz). The Repeated Measures Anova (RMANOVA) test is used to determine within group and between group variability of SE values in (61 channels) various specified regions of the brain during a visual single object recognition task. In order to discriminate visual ERPs produced in alcoholics and controls within the beta band, SE features are applied to a k-NN classifier. RMANOVA tests show that there is little statistical significance (p>0.05) as far as the marginal mean between groups is considered. However, the marginal mean variability within group indicates that there are some selected channels in which the statistical significance is observed (p<0.05). The classification results show that k-NN classification achieves 90.41% accuracy with k=8. It is also observed that the mean of spectral entropy values increase mainly in the left frontal region of the brain in alcoholics as compared to controls.

Original languageEnglish
Title of host publication2016 International Conference on Circuits, Controls, Communications and Computing, I4C 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509053698
DOIs
Publication statusPublished - 27-09-2017
Externally publishedYes
Event2016 International Conference on Circuits, Controls, Communications and Computing, I4C 2016 - Bangalore, India
Duration: 04-10-201606-10-2016

Conference

Conference2016 International Conference on Circuits, Controls, Communications and Computing, I4C 2016
Country/TerritoryIndia
CityBangalore
Period04-10-1606-10-16

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Control and Optimization
  • Computer Networks and Communications
  • Instrumentation

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