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Reseach Article

Support Vector Machine Technique for EEG Signals

by P. Bhuvaneswari, J. Satheesh Kumar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 63 - Number 13
Year of Publication: 2013
Authors: P. Bhuvaneswari, J. Satheesh Kumar
10.5120/10523-5503

P. Bhuvaneswari, J. Satheesh Kumar . Support Vector Machine Technique for EEG Signals. International Journal of Computer Applications. 63, 13 ( February 2013), 1-5. DOI=10.5120/10523-5503

@article{ 10.5120/10523-5503,
author = { P. Bhuvaneswari, J. Satheesh Kumar },
title = { Support Vector Machine Technique for EEG Signals },
journal = { International Journal of Computer Applications },
issue_date = { February 2013 },
volume = { 63 },
number = { 13 },
month = { February },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume63/number13/10523-5503/ },
doi = { 10.5120/10523-5503 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:14:12.590490+05:30
%A P. Bhuvaneswari
%A J. Satheesh Kumar
%T Support Vector Machine Technique for EEG Signals
%J International Journal of Computer Applications
%@ 0975-8887
%V 63
%N 13
%P 1-5
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Support Vector Machine (SVM) is one of the popular Machine Learning techniques for classifying the Electroencephalography (EEG) signals based on the neuronal activity of the brain. EEG signals are represented into high dimensional feature space for analyzing the brain activity. Kernel functions are helpful for efficient implementation of non linear mapping. This paper gives an overview of classification techniques available in Support Vector Machine. This paper also focus role of SVM on EEG signal analysis.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Support Vector Machine Electroencephalography classifier Signal processing