Share This Article:

Comparison of SVM and ANN for classification of eye events in EEG

Abstract Full-Text HTML Download Download as PDF (Size:1311KB) PP. 62-69
DOI: 10.4236/jbise.2011.41008    6,269 Downloads   13,568 Views   Citations


The eye events (eye blink, eyes close and eyes open) are usually considered as biological artifacts in the electroencephalographic (EEG) signal. One can con-trol his or her eye blink by proper training and hence can be used as a control signal in Brain Computer Interface (BCI) applications. Support vector ma-chines (SVM) in recent years proved to be the best classification tool. A comparison of SVM with the Artificial Neural Network (ANN) always provides fruitful results. A one-against-all SVM and a multi-layer ANN is trained to detect the eye events. A com-parison of both is made in this paper.

Cite this paper

Singla, R. , Chambayil, B. , Khosla, A. and Santosh, J. (2011) Comparison of SVM and ANN for classification of eye events in EEG. Journal of Biomedical Science and Engineering, 4, 62-69. doi: 10.4236/jbise.2011.41008.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Sanei, S. and Chambers, J.A. (2007) EEG signal processing. John Wiley & Sons Ltd., Chichester.
[2] Rajasekaran, S., Vijayalakshmi Pai, G.A. (2008) Neural networks, fuzzy logic and genetic algorithms: synthesis and applications. Prentice-Hall of India Private Limted, New Delhi.
[3] Singla, R. and Gupta, B. (2008) Brain initiated interaction. Journal of Biomedical Science and Engineering, 1, 170-172. doi:10.4236/jbise.2008.13028
[4] Manoilov, P. (2006) EEG eye-blinking artifacts power spectrum analysis. Proceedings of International Conference on Computer Systems and Technologies, Bulgaria, 15-16 June 2006, IIIA. 3-1-IIIA.3-5.
[5] Sovierzoski, M.A., Argoud, F.I.M. and de Azevedo, F.M. (2008) Identifying eye blinks in EEG signal analysis. Proceedings of the 5th International Conference on Information Technology and Application in Biomedicine, 30-31 May 2008, Shenzhen, China, 406-409.
[6] Manolakis, D.G., Ingle, V.K. and Kogon, S.M. (2005) Statistical and adaptive signal processing: Spectral estimation, signal modeling, adaptive filtering and array processing. Artech House Publishers, London.
[7] Haykin, S. (1998) Neural networks: A comprehensive foundation. Prentice Hall, New Jersey.
[8] Cristianini, N. and Shawe-Taylor, J. (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge.
[9] Vapnik, V. (1998) Statistical Learning Theory. John Wiley & Sons, Chichester.
[10] Xie, S.-Y., Wang, P.-W., Zhang, H.-J. and Zhao, H.-T. (2008) Research on the classification of brain function based on SVM. The 2nd International Conference on Bioinformatics and Biomedical Engineering, 16-18 May 2008, 1931-1934.

comments powered by Disqus

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.