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Support Vector Machine Technique for EEG Signals
International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 63 - Number 13
Year of Publication: 2013
|
10.5120/10523-5503 |
P Bhuvaneswari and Satheesh J Kumar. Article: Support Vector Machine Technique for EEG Signals. International Journal of Computer Applications 63(13):1-5, February 2013. Full text available. BibTeX
@article{key:article,
author = {P. Bhuvaneswari and J. Satheesh Kumar},
title = {Article: Support Vector Machine Technique for EEG Signals},
journal = {International Journal of Computer Applications},
year = {2013},
volume = {63},
number = {13},
pages = {1-5},
month = {February},
note = {Full text available}
}
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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