Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Orthonormal Polynomial based Optimal EEG Feature Extraction for Motor Imagery Brain-Computer Interface

  • Chum, Pharino (School of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Park, Seung-Min (School of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Ko, Kwang-Eun (School of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Sim, Kwee-Bo (School of Electrical and Electronics Engineering, Chung-Ang University)
  • Received : 2012.11.10
  • Accepted : 2012.12.03
  • Published : 2012.12.25
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Abstract

In this paper, we explored the new method for extracting feature from the electroencephalography (EEG) signal based on linear regression technique with the orthonormal polynomial bases. At first, EEG signals from electrodes around motor cortex were selected and were filtered in both spatial and temporal filter using band pass filter for alpha and beta rhymic band which considered related to the synchronization and desynchonization of firing neurons population during motor imagery task. Signal from epoch length 1s were fitted into linear regression with Legendre polynomials bases and extract the linear regression weight as final features. We compared our feature to the state of art feature, power band feature in binary classification using support vector machine (SVM) with 5-fold cross validations for comparing the classification accuracy. The result showed that our proposed method improved the classification accuracy 5.44% in average of all subject over power band features in individual subject study and 84.5% of classification accuracy with forward feature selection improvement.

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Acknowledgement

Supported by : 한국연구재단

References

  1. Pfurtscheller G. and Neuper C., "Motor imagery and direct brain-computer communication," Proceedings of the IEEE, vol. 89, pp. 1123-1134, 2001. https://doi.org/10.1109/5.939829
  2. Lotte F. and Congedo M., "Evolution of brain-computer interfaces: going beyond classic motor physiology," Journal of neural, vol. 27, pp. 1-21, 2007.
  3. Baudry CT. and Bertrand O. "Oscillatory gamma activity in humans and its role in object representation," Trends in cognitive sciences, vol. 3, pp. 151-162, 1999. https://doi.org/10.1016/S1364-6613(99)01299-1
  4. Pfurtscheller G., Neuper C., Flotzinger D, Pregenzer M., "EEG-based discrimination between imagination of right and left hand movement," Electroencephalography and clinical Neurophysiology, vol. 103, pp. 642-651, 1997. https://doi.org/10.1016/S0013-4694(97)00080-1
  5. Pregenzer M., Pfurtscheller G., "Frequency component selection for an EEG-based brain to computer interface," IEEE Transactions on Rehabilitation Engineering, vol. 7, pp. 413-419, 1999. https://doi.org/10.1109/86.808944
  6. Neuper C., Scherer R., Reiner M., Pfurtscheller G., "Imagery of motor actions: differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG," Brain research. Cognitive brain research, vol. 25, pp. 668-677, 2005. https://doi.org/10.1016/j.cogbrainres.2005.08.014
  7. Hasan B., "Multi-Objective Particle Swarm Optimization for Channel Selection in Brain-Computer Interfaces," The UK Workshop on Computational Intelligence, pp. 2-7, 2009.
  8. Abramowitz, M. and Stegun, I. A. (Eds.), Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables, Dover, pp. 331-339 and 771-802, New York, 1972.
  9. Yaser S. Abu-Mostafa, Mlik Magdon-Ismail and Hsuan-Tien Lin, Learning from data: A short course, AMLbook.com, 2012.
  10. Isabelle Guyon, Steve Gunn, Masoud Nikravesh and Lotfi A. Zadeh, Feature Extarction: Foundation and Application, Springer, 2006.
  11. B.Blankertz,"BCI Competition III", 2004[Online]. Available: http://www.bbci.de/competition/iii/
  12. B.Blankertz,"BCI Competition IV", 2008[Online]. Available: http://bbci.de/competition/iv/