We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
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
  1. S J M Smith, "EEG in the diagnosis, classification, management of patients with epilepsy", J Neurol Neurosurg Psychatry ,Vol. 76 (suppl II), pp. ii2-ii7, 2005.
  2. Ki-Hong Kim, Hong Kee Kim, Jong-Sung Kim, Wookho Son, and Soo-Young Lee, "A biosignal based human interface controlling a power wheelchair for people with motor disabilities", ETRI Journal, Vol. 28, No. 1, 2006.
  3. Corinna Cortes, Vladimir Vapnik, "Support Vector Networks", Machine Learning pp. 273-297, 1995.
  4. Hyeran Byun and Seong-Whan Lee, "Applications of Support Vector Machine for pattern recognition: A survey"
  5. V. David Sanchez, "Advanced Support Vector Machines and Kernel Methods", Neurocomputing, pp. 5-20, 2003.
  6. Ali Shoeb and John Guttag, "Applications of Machine Learning to Epileptic Seizure Detection", 27th International Conference on Machine Learning, Isreal, 2010.
  7. Varun Bajaj and Ram Bilas Pachori, "EEG Signal Classification Using Empirical Mode Decomposition and Support Vector Machine", Advances in Intelligent and Soft Computing,Vol. 131, 2012.
  8. J. R. Panda. , " Classification of EEG signals using wavelet transform and support vector machine for epileptic seizure activity", Proceedings of International Conference on Systems in Medicine and Biology, 2010.
  9. Giovanni Costantini, Daniele Casali, Massimiliano and Todisco, "An SVM based classification method for EEG signals", Latest Trends on Circuits, ISSN:1792-4227, ISBN:978-960-474-198-4.
  10. A. Temko, G. Boylan,W. Marnane and G. Lightbody, "Speech Recognition Features for EEG signal description in detection of Neonatal Seisures", 2009.
  11. M. Murugesan and R. Sukanesh, "Towards detection of brain tumor in EEG signals using SVM", International Journal of Computer Theory and Engineering", Vol. 1, No. 5, 2005.
  12. Olga Sourina and Yisi Liu, "A Fractal based algorithm of emotion recognition from EEG signals using arousal and valence model".
Index Terms

Computer Science
Information Sciences

Keywords

Support Vector Machine Electroencephalography classifier Signal processing