Research Article
Support Vector Machine Technique for EEG Signals
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 63 - Issue 13 |
| Published: February 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 },
year = { 2013 },
volume = { 63 },
number = { 13 },
pages = { 1-5 },
doi = { 10.5120/10523-5503 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2013
%A P. Bhuvaneswari
%A J. Satheesh Kumar
%T Support Vector Machine Technique for EEG Signals%T
%J International Journal of Computer Applications
%V 63
%N 13
%P 1-5
%R 10.5120/10523-5503
%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.
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