International Journal of P2P
Network Trends and Technology

Research Article | Open Access | Download PDF
Volume 2 | Issue 4 | Year 2012 | Article Id. IJPTT-V2I4P402 | DOI : https://doi.org/10.14445/22492615/IJPTT-V2I4P402

An Efficient Classification of Motor Imagery ECOG Signals using Support Vector Machine for Brain Computer Interface

N.Rathipriya, S.Deepajothi, T.Rajendran

Citation :

N.Rathipriya, S.Deepajothi, T.Rajendran, "An Efficient Classification of Motor Imagery ECOG Signals using Support Vector Machine for Brain Computer Interface," International Journal of Computer Trends and Technology (IJCTT), vol. 2, no. 4, pp. 7-10, 2012. Crossref, https://doi.org/10.14445/22492615/IJPTT-V2I4P402

Abstract

Although brain–computer interface (BCI) methods have been evolving quickly in recent decades, there still a number of unsolved difficulties, such as enhancement of motor imagery (MI) classification. The most commonly used signals in BCI investigations is electroencephalography (EEG) recordings. EEG has restricted tenacity and needs extensive training and has restricted stability. Over the past ten years, an expanding number of studies has discovered the use of electrocorticography (ECoG) activity extracting signals from the surface of the mind. ECOG has attracted considerable and expanding interest, because its mechanical characteristics should readily support robust and chronic implementations of BCI systems in humans. In this paper, we suggest a hybrid algorithm to advance the classification achievement rate of MI based electrocorticography (ECoG) in BCIs. To verify the effectiveness of the suggested classifier, we restore the SVM classifier with the identical features extracted from the cross-correlation method for the classification. The performances of those procedures are assessed with classification correctness through a 10-fold cross-validation procedure. Then consider the performance of the suggested procedure by comparing it with existing system.

Keywords

Brain–computer interface (BCI), cross-correlation technique, electrocorticography (ECoG), support vector machine (SVM), motor imagery (MI).

References

[1] R. Caton, “Electrical currents of the brain,” J. Nervous MentalDisease, vol. 2, no. 4, p. 610, 1875.
[2] W. J. Freeman, “Spatial properties of an EEG event in the olfactory bulb and cortex,” Electroencephalogr. Clin. Neurophysiol., vol. 44, no. 5, pp. 586–605, May 1978.
[3] W. J. Freeman andW. Schneider, “Changes in spatial patterns of rabbit olfactory EEG with conditioning to odors,” Psychophysiology, vol. 19,no. 1, pp. 44–56, Jan. 1982.
[4] W. J. Freeman and B. W. van Dijk, “Spatial patterns of visual cortical fast EEG during conditioned reflex in a rhesus monkey,” Brain Res.,vol. 422, no. 2, pp. 267–276, Oct. 1987.
[5] D. J. McFarland and J. R. Wolpaw, “Brain-computer interfaces for communication and control,” Commun. ACM, vol. 54, no. 5, pp. 60–66, 2011.
[6] G. Pfurtscheller, C. Brunner, A. Schlogl, and F. Lopes da Silva, “Mu rhythm (de) synchronization and EEG single-trial classification of different motor imagery tasks,” Neuroimage, vol. 31, no. 1, pp. 153–159,2006.
[7] W. Wu, X. Gao, B. Hong, and S. Gao, “Classifying single-trial EEG during motor imagery by iterative spatio-spectral patterns learning (ISSPL),” IEEE Trans. Biomed. Eng., vol. 55, no. 6, pp. 1733–1743,Jun.2008.
[8] G. M. Hieftje, R. I. Bystroff, and R. Lim, “Application of correlation analysis for signal-to-noise enhancement in flame spectrometry: Use of correlation in determination of rhodium by atomic fluorescence,”Analytical Chem., vol. 45, no. 2, pp. 253–258, 1973.
[9] S. Dutta, A. Chatterjee, and S. Munshi, “An automated hierarchical gait pattern identification tool employing cross-correlation-based feature extraction and recurrent neural network based classification,” Expert Syst., vol. 26, no. 2, pp. 202–217, 2009.
[10] C. M. Gaona, M. Sharma, Z. V. Freudenburg, J. D. Breshears, D.T. Bundy, J. Roland, D. L. Barbour, G. Schalk, and E. C. Leuthardt,“Nonuniform high-gamma (60–500 Hz) power changes dissociatecognitive task and anatomy in human cortex,” J. Neurosci., vol. 31,no. 6, pp. 2091–2100, Feb. 2011.
[11] Dutta, A. Chatterjee, and S. Munshi, “Correlation techniques anleast square support vector machine combine for frequency domain based ECG beat classification,” Med. Eng.Phys., vol. 32, no. 10, pp.1161–1169, Dec. 2010.
[12] S. Chandaka, A. Chatterjee, and S. Munshi, “Cross-correlation aided support vector machine classifier for classification of EEG signals,”Expert Syst. Appl., vol. 36, pp. 1329–1336, 2009.
[13] G. M. Hieftje, R. I. Bystroff, and R. Lim, “Application of correlation analysis for signal-to-noise enhancement in flame spectrometry: Use of correlation in determination of rhodium by atomic fluorescence,” Analytical Chem., vol. 45, no. 2, pp. 253–258, 1973.