Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Performance Improvements of Brain-Computer Interface Systems based on Variance-Considered Machines

Variance-Considered Machine에 기반한 Brain-Computer Interface 시스템의 성능 향상

  • 염홍기 (중앙대학교 전자전기공학부) ;
  • 심귀보 (중앙대학교 전자전기공학부)
  • Received : 2009.11.14
  • Accepted : 2010.01.25
  • Published : 2010.02.25
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Abstract

This paper showed the possibilities of performance improvement of Brain-Computer Interface (BCI) decreasing classification error rates of EEG signals by applying Variance-Considered Machine (VCM) which proposed in our previous study. BCI means controlling system such as computer by brain signals. There are many factors which affect performances of BCI. In this paper, we used suggested algorithm as a classification algorithm, the most important factor of the system, and showed the increased correct rates. For the experiments, we used data which are measured during imaginary movements of left hand and foot. The results indicated that superiority of VCM by comparing error rates of the VCM and SVM. We had shown excellence of VCM with theoretical results and simulation results. In this study, superiority of VCM is demonstrated by error rates of real data.

본 논문에서는 선행 연구를 통해 제안하였던 새로운 분류 알고리즘인 Variance Considered Machines (VCM)을 통해 EEG 신호의 분류 에러율을 감소시킴으로 Brain-Computer Interface (BCI)의 성능향상 가능성을 보였다. BCI란 뇌파를 통해 컴퓨터와 같은 시스템을 제어하는 것으로 BCI의 인식률에 영향을 미치는 것에는 많은 요소가 있지만 본 논문에서는 그 중에서도 가장 중요한 분류 알고리즘을 제안된 알고리즘을 통해 인식률을 향상하는 것을 보였다. 이를 위해 피험자가 가상으로 왼쪽 손과 다리를 움직이는 상상을 한 데이터를 기존에 가장 많이 사용되고 있는 분류 알고리즘인 SVM과 제안된 VCM으로 분류하여 인식률을 비교하였다. 기존 연구를 통해 VCM의 우수성을 이론적 결과와 시뮬레이션 결과로 보였다면 본 논문에서는 실제 데이터를 통한 실험을 통해 인식률 향상을 보였다.

Keywords

References

  1. Laura Kauhanen, Pasi Jylanki, janne Lehtonen, Pekka Rantanen, Hannu Alaranta, and Mikko Sams, EEG-Based Brain-Computer Interface for Tetraplegics, Hindawi Publishing Corporation Computational Intelligence and Neuroscience Volume 2007, Article ID 23864, 11pages doi:10.1155/2007/23864.
  2. L. Citi, R. Poli, C. Cinel, and F. Sepulveda, "P300-Based BCI Mouse With Genetically-Optimized Analogue Control," IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 16, No. 1, Feb. 2008.
  3. G. Dornhege, J. R. Millan, T. Hinterberger, D. J. McFarland, and K. Muller, Toward Brain-Computer Interfacing, MIT press, 2007.
  4. 박현욱 외 21명, 뇌신경정보학연구사업; 뇌기능 측정 및 분석 시스템, 한국과학기술원, 2004.
  5. O. D. Richard, E. H. Peter and G. S. David, Pattern classification, New York, Wiley, 2nd ed., pp. 3-42, 2001.
  6. Fischer and R. Stiefelhagen, "Face Recognition for Smart Interactions," IEEE Int. Conf. Multimedia and Expo, vol. 2-5, pp.1007 – 1010. 2007.
  7. M. C. Erik and M. M. Trivedi, "Head Pose Estimation in Computer Vision: A Survey," IEEE Trans. Pattern Analysis & Machine Intelligence, vol. 31 no 4, pp. 607-626, Apr 2009. https://doi.org/10.1109/TPAMI.2008.106
  8. S. W. Lee, "Automatic Gesture Recognition for Intelligent Human-Robot Interaction," 7th Int. Conf. Automatic Face and Gesture Recognition, 2006. FGR 2006, vol. 2-6, pp. 645 – 650.
  9. H. G. Yeom and K. B. Sim, "ERS and ERD Analysis during the Imaginary Movement of Arms," Int. conf. Control, Automation and Systems, in COEX, Seoul, Korea 2008, pp. 2476-2480.
  10. A. Chakraborty, A. Konar, U. K. Chakraborty, A. Chatterjee, "Emotion Recognition From Facial Expressions and Its Control Using Fuzzy Logic," IEEE Trans. Sys. Man and Cybernetics, Part A: Systems and Humans, vol. 39, no. 4, pp. 726 – 743, July 2009.
  11. A. A. Michael et al., The Handbook of Brain Theory and Neural Networ, MIT press, 1998.
  12. C. CORTES and V. VAPNIK, Support-vector networks, Machine Learning, pp.273–297,1995. https://doi.org/10.1007/BF00994018
  13. H. G. Yeom, I. H. Jang and K. B. Sim, "Variance Considered Machines: Modification of Optimal Hyperplanes in Support Vector Machines," IEEE Int. Symp. Industrial Electronics, Seoul, Korea, July, 2009, pp. 1144-1147.
  14. H. G. Yeom, I. H. Jang, and K. B. Sim, "A Novel Classification Algorithm for Reduction of Classification Errors : Variance-Considered Machine," submitted to International Journal of Control, Automaion, and Systems.
  15. http://bbci.de/competition/iv
  16. B. Blankertz, G. Dornhege, M. Krauledat, K. R. Müller, and G. Curio, "The non-invasive Berlin Brain-Computer Interface: Fast acquisition of effective performance in untrained subjects," NeuroImage, Vol. 37, No. 2, pp.539-550, 2007. https://doi.org/10.1016/j.neuroimage.2007.01.051
  17. 염홍기, 심귀보, "Variance-Considered Machine을 이용한 상상 움직임의 EEG 신호 분류", 2009년도 한국지능시스템학회 추계학술대회 학술발표 논문집, Vol.19, No.2, pp.147-150, 2009.

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