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Reseach Article

Event Evoked Signal Classification in Frequency Domain for Brain Computer Interface

by G. V. Sridhar, P. Mallikarjuna Rao
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 45 - Number 14
Year of Publication: 2012
Authors: G. V. Sridhar, P. Mallikarjuna Rao
10.5120/6852-9434

G. V. Sridhar, P. Mallikarjuna Rao . Event Evoked Signal Classification in Frequency Domain for Brain Computer Interface. International Journal of Computer Applications. 45, 14 ( May 2012), 38-42. DOI=10.5120/6852-9434

@article{ 10.5120/6852-9434,
author = { G. V. Sridhar, P. Mallikarjuna Rao },
title = { Event Evoked Signal Classification in Frequency Domain for Brain Computer Interface },
journal = { International Journal of Computer Applications },
issue_date = { May 2012 },
volume = { 45 },
number = { 14 },
month = { May },
year = { 2012 },
issn = { 0975-8887 },
pages = { 38-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume45/number14/6852-9434/ },
doi = { 10.5120/6852-9434 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:37:37.890341+05:30
%A G. V. Sridhar
%A P. Mallikarjuna Rao
%T Event Evoked Signal Classification in Frequency Domain for Brain Computer Interface
%J International Journal of Computer Applications
%@ 0975-8887
%V 45
%N 14
%P 38-42
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Brain Computer Interface (BCI) is an emerging research area which tries to capture the motor imagery thought process from brain using Electro-encephalogram (EEG) and process the data using signal processing techniques to classify the motor imagery thought process. Physically impaired people without any muscular activity can carry on their day to day operation with the use of BCI as it can be used to control devices including computers using the thoughts of the person. Devices such as wheelchair have been successfully connected to BCI system and these devices can be controlled using thought. In this paper, it is proposed to investigate EEG signals, extract features of motor imagery in the frequency domain using Hilbert transform, compute the maximum and minimum energies and classify the brain signal activity using pattern recognition techniques.

References
  1. T. M. Vaughan et al. , "Brain-computer interface technonolgy: a review of the second international meeting," IEEE Trans. Rehab. Eng. , vol. 11, pp. 94-109, 2003.
  2. W. D. Penny, S. J. Roberts, E. A. Curran, and M. J. Stokes. Eeg-based communication: a pattern recognition approach. IEEE Transactions on Rehabilitation Engeneering, 8(2):214-215, 2000.
  3. S. Sutton, M. Braren, J. Zubin, E. John (1965). Evoked-potential correlates of stimulus uncertainty. Science150(700):1187–1188.
  4. S. Nieuwenhuis, G. Aston-Jones, J. Cohen (2005). Decision making, the P3, and the locus coeruleus-norepinephrine system. Psychological Bulletin 131(4):510–532.
  5. E. Courchesne (1978). Changes in P3 waves with event repetition: Long-term effects on scalp distribution and amplitude. Electroencephalography and Clinical Neurophysiology 45(6):754–766.
  6. G. Pfurtscheller, G. R. Müller-Putz, J. Pfurtscheller, R. Rupp (2005). EEG-Based asynchronous BCI controls functional electrical stimulation in a tetraplegic patient. EURASIP Journal on Applied Signal Processing 2005(19):3152–3155.
  7. G. Pfurtscheller, C. Neuper (2001). Motor imagery and direct brain-computer communication. Proceedings of the IEEE 89(7):1123–1134.
  8. Manling Huang, Pingdong Wu, Ying Liu, Luzheng Bi, Hongwei Chen, "Application and Contrast in Brain-Computer Interface between Hilbert-Huang Transform and Wavelet Transform," icycs, pp. 1706-1710, 2008 The 9th International Conference for Young Computer Scientists, 2008.
  9. Lei Wang; Guizhi Xu; Jiang Wang; Shuo Yang; Weili Yan; "Motor Imagery BCI Research Based on Hilbert-Huang Transform and Genetic Algorithm", Bioinformatics and Biomedical Engineering, May 2011 5th international conference.
  10. Sherwood, J. , and Derakhshani, R. 2009. "On classifiability of wavelet features for EEG based brain-computer interfaces. " International Joint Conference on Neural Networks, Atlanta, GA, June 2009, PP: 2895-2902.
  11. Y. P. A. Yong, N. J. Hurley, and G. C. M. Silvestre. Single-trial EEG classi?cation for brain-computer interfaces using wavelet decomposition. In European Signal Processing Conference, EUSIPCO 2005, 2005.
  12. Lal TN, Schroder M, Hinterberger T, Weston J, Bogdan M, Birbaumer N, Scholkopf B. Support Vector Channel Selection in BCI. IEEE Trans Biomed Eng. 2004;51(6):1003–1010.
  13. H. Lee and S. Choi. "Pca+hmm+svm for eeg pattern classification. " In Proceedings of the Seventh International Symposium on Signal Processing and Its Applications, 2003.
  14. Lotte F, Congedo M, Lecuyer A, Lamarche F and Arnaldi B (2007) "A review of classi?cation algorithms for EEG-based brain–computer interfaces" J. Neural Eng. PP: 1-13.
  15. Guido Dornhege, Benjamin Blankertz, Gabriel Curio, and Klaus-Robert Müller. Boosting bit rates in non-invasive EEG single-trial classifications by feature combination and multi-class paradigms. IEEE Trans. Biomed. Eng. , 51(6):993-1002, June 2004.
  16. Pei, S. -C. ; Jaw, S. -B. ; "Computation of discrete Hilbert transform through fast Hartley transform,"Circuits and Systems, IEEE Transactions on , vol. 36, no. 9, pp. 1251-1252, Sep 1989.
  17. Steven. W. Smith, The Scientist and Engineer's Guide to Digital Signal Processing" 1997.
  18. B. E. Boser, I. Guyon, and V. Vapnik. A training algorithm for optimal margin classifiers. In Proceedings Of The Fifth Annual Workshop On Computational Learning Theory, pages 144-152. ACM Press, 1992.
Index Terms

Computer Science
Information Sciences

Keywords

Brain Computer Interface (bci) Fast Hilbert Transform Support Vector Machine (svm) Pattern Recognition