Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Optimal EEG Channel Selection by Genetic Algorithm and Binary PSO based on a Support Vector Machine

Support Vector Machine 기반 Genetic Algorithm과 Binary PSO를 이용한 최적의 EEG 채널 선택 기법

  • Kim, Jun Yeup (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)
  • 김준엽 (중앙대학교 전자전기공학부) ;
  • 박승민 (중앙대학교 전자전기공학부) ;
  • 고광은 (중앙대학교 전자전기공학부) ;
  • 심귀보 (중앙대학교 전자전기공학부)
  • Received : 2013.01.07
  • Accepted : 2013.03.13
  • Published : 2013.06.01
Download PDF
⟨ Previous Next ⟩

Abstract

BCI (Brain-Computer Interface) is a system that transforms a subject's brain signal related to their intention into a control signal by classifying EEG (electroencephalograph) signals obtained during the imagination of movement of a subject's limbs. The BCI system allows us to control machines such as robot arms or wheelchairs only by imaging limbs. With the exact same experiment environment, activated brain regions of each subjects are totally different. In that case, a simple approach is to use as many channels as possible when measuring brain signals. However the problem is that using many channels also causes other problems. When applying a CSP (Common Spatial Pattern), which is an EEG extraction method, many channels cause an overfitting problem, and in addition there is difficulty using this technique for medical analysis. To overcome these problems, we suggest an optimal channel selection method using a BPSO (Binary Particle Swarm Optimization), BPSO with channel impact factor, and GA. This paper examined optimal selected channels among all channels using three optimization methods and compared the classification accuracy and the number of selected channels between BPSO, BPSO with channel impact factor, and GA by SVM (Support Vector Machine). The result showed that BPSO with channel impact factor selected 2 fewer channels and even improved accuracy by 10.17~11.34% compared with BPSO and GA.

Keywords

References

  1. J. Wolpaw, N. Birbaumer, W. Heetderks, D. McFarland, P. Peckham, G. Schalk, E. Donchin, L. Quatrano, C. Robinson, and T. Vaughan, "Brain-computer interface technology: A review of the First International Meeting," IEEE Trans. Rehab. Eng., vol. 8, no. 2, pp. 164-173, 2000. https://doi.org/10.1109/TRE.2000.847807
  2. C. Guger, R. Leeb, D. Friedman, V. Vinayagamoorthy, G. Edlinger, and M. Slater, "Controlling virtual environments by thoughts," Clinical Neurophysiology, vol. 118. no. 4, pp. e36, 2007.
  3. A. Schlogl, F. Lee, H. Bischof, and G. Pfurtscheller, "Characterization of four-class motor imagery EEG data for the BCI-competition 2005," Neural Engineering, vol. 2, pp. 14-22, 2005. https://doi.org/10.1088/1741-2560/2/4/L02
  4. R. W. Homan, J. Herman, and P. Purdy, "Cerebral location of international 10-20 system electrode placement," Electroencephalogr. Clin. Neurophysiol., vol. 66, no. 4, pp. 376-382, 1987. https://doi.org/10.1016/0013-4694(87)90206-9
  5. C. Guger, R. Leeb, D. Friedman, V. Vinayagamoorthy, G. Edlinger, and M. Slater, "Controlling virtual environments by thoughts," Clinical Neurophysiology, vol. 118. no. 4, pp. e36, 2007.
  6. C. J. C. Burges, "A tutorial on support vector machines for pattern recognition," Data Mining and Knowledge Discovery, vol. 2, pp. 121-167, 1998. https://doi.org/10.1023/A:1009715923555
  7. J. Kennedy and R. C. Eberhart, "Particle swarm optimization," Proc. of IEEE International Conference on Neural Networks, Piscataway, NJ, pp. 1942-1948, 1995.
  8. J. Y. Kim, S. M. Park, K. E. Ko, and K. B. Sim, "Swarm control of distributed autonomous robot system based artificial immune system using PSO," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 18, no. 5, pp. 465-470, May 2012. https://doi.org/10.5302/J.ICROS.2012.18.5.465
  9. H. S. Son, J. B. Park, K. and Y. H. Joo, "The reduction methodology of external noise with segmentalized PSO-FCM: Its application to phased conversion of the radar system on board," Jounal of Institute of Contro, Robotics and Systems (in Korean), vol. 18, no. 7, pp. 638-643, Jul. 2012. https://doi.org/10.5302/J.ICROS.2012.18.7.638
  10. J. Kennedy and R. C. Eberhart, "A discrete binary version of the particle swarm algorithm," IEEE Int. Conf. on Systems, Man and Cybernetics, vol. 5, pp. 4104-4108, 1997.
  11. Andries p.Engelblrecht, Computational Intelligence An Introduction Second Edition, John Wiley & Sons, Ltd. 2007.
  12. G. Pfurtscheller and F. H. Lopes da Silva, "Event-related EEG/MEG synchronization and desynchronization: basic principles," Clin. Neurophysiol., vol. 110, no. 11, pp. 1842-1857, 1999. https://doi.org/10.1016/S1388-2457(99)00141-8
  13. J. Y. Kim, S. M. Park, K. E. Ko, and K. B. Sim, "Optimal EEG channel selection using BPSO with channel impact factor," Proc. of KIIS Fall Conference(in Korean), vol. 22, no. 2, pp. 35-36, 2012.
  14. A. Schlögl, F. Lee, H. Bischof, and G. Pfurtscheller, "Characterization of four-class motor imagery EEG data for the BCI-competition 2005," Neural Engineering, vol. 2, pp. 14-22, 2005. https://doi.org/10.1088/1741-2560/2/4/L02
  15. T. N. Lal, M. Schroder, T. Hinterberger, J. Weston, M. Bogdan, N. Birbaumer, and B. Scholkopf, "Support vector channel selection in BCI," IEEE Trans. Biomed. Engineering, vol. 51, no. 6, pp. 1003-1010, 2004. https://doi.org/10.1109/TBME.2004.827827

Cited by

  1. Brain Wave Characteristic Analysis by Multi-stimuli with EEG Channel Grouping based on Binary Harmony Search vol.19, pp.8, 2013, https://doi.org/10.5302/J.ICROS.2013.13.1915