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EEG Classification based on Machine Learning Techniques

by Farid Ali Mousa, Reda A. El-Khoribi, Mahmoud E. Shoman
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 128 - Number 4
Year of Publication: 2015
Authors: Farid Ali Mousa, Reda A. El-Khoribi, Mahmoud E. Shoman
10.5120/ijca2015906515

Farid Ali Mousa, Reda A. El-Khoribi, Mahmoud E. Shoman . EEG Classification based on Machine Learning Techniques. International Journal of Computer Applications. 128, 4 ( October 2015), 22-27. DOI=10.5120/ijca2015906515

@article{ 10.5120/ijca2015906515,
author = { Farid Ali Mousa, Reda A. El-Khoribi, Mahmoud E. Shoman },
title = { EEG Classification based on Machine Learning Techniques },
journal = { International Journal of Computer Applications },
issue_date = { October 2015 },
volume = { 128 },
number = { 4 },
month = { October },
year = { 2015 },
issn = { 0975-8887 },
pages = { 22-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume128/number4/22861-2015906515/ },
doi = { 10.5120/ijca2015906515 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:20:32.887004+05:30
%A Farid Ali Mousa
%A Reda A. El-Khoribi
%A Mahmoud E. Shoman
%T EEG Classification based on Machine Learning Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 128
%N 4
%P 22-27
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The main issue to build applicable Brain-Computer Interfaces is the capability to classify the electroencephalograms (EEG). During the last decade, researchers developed lots of interests in this field. The purpose behind this research is to improve a model for EEG signals analysis. Filtration of EEG Signals is essential to remove artifacts. Otherwise, wavelet transform was used to extract features. Mean, Maximum, Minimum and Standard Deviations values of wavelet coefficients for the EEG signals were chosen as a feature vector. This paper compares the classification results by the use of Neural Network, K-Nearest Neighbor and Support Vector Machine classifiers. It has been illustrated from results that the K-Nearest Neighbor classifier outperforms a better performance than Neural Network and Support Vector Machine.

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Index Terms

Computer Science
Information Sciences

Keywords

Brain Computer Interface Support Vector Machine Neural Network K-Nearest Neighbor Wavelet Transform EEG.

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