International Journal of Fuzzy Logic and Intelligent Systems

  • 제10권1호
  • /
  • Pages.59-64
  • /
  • 2010
  • /
  • 1598-2645(pISSN)
  • /
  • 2093-744X(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
권호 표지
DOI QR코드

DOI QR Code

Membership Function-based Classification Algorithms for Stability improvements of BCI Systems

  • Yeom, Hong-Gi (School of Electrical and Electronics Engineering, ChungAng University) ;
  • Sim, Kwee-Bo (School of Electrical and Electronics Engineering, ChungAng University)
  • 투고 : 2009.12.07
  • 심사 : 2010.01.30
  • 발행 : 2010.03.25
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

To improve system performance, we apply the concept of membership function to Variance Considered Machines (VCMs) which is a modified algorithm of Support Vector Machines (SVMs) proposed in our previous studies. Many classification algorithms separate nonlinear data well. However, existing algorithms have ignored the fact that probabilities of error are very high in the data-mixed area. Therefore, we make our algorithm ignore data which has high error probabilities and consider data importantly which has low error probabilities to generate system output according to the probabilities of error. To get membership function, we calculate sigmoid function from the dataset by considering means and variances. After computation, this membership function is applied to the VCMs.

키워드

참고문헌

  1. 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, pp. 1144-1147, 2009.
  2. B. M. Belgacem, “Speech Recognition for Disabilities People”, Information and Communication Technologies, 2006. ICTTA "06. 2nd, Vol. 1, pp.864 – 869, 2006. https://doi.org/10.1109/ICTTA.2006.1684487
  3. H.K Ekenel, J. Stallkamp, H. Gao, M. Fischer and R. Stiefelhagen, “Face Recognition for Smart Interactions”, IEEE International Conference on Multimedia and Expo, Vol. 2-5 July pp.1007 – 1010, 2007.
  4. S. W. Lee, “Automatic Gesture Recognition for Intelligent Human-Robot Interaction”, 7th International Conference on Automatic Face and Gesture Recognition, Vol. 2-6 April 2006. pp. 645 – 650, 2006.
  5. O. D. Richard, E. H. Peter and G. S. David, Pattern classification, Wiley, New York, Second edition, 2001.
  6. D. E. Rumelhart, G. E. Hintont and R. J. Williams, “Learning representations by back-propagating errors”, Nature, Vol.323 9 Oct. 1986.
  7. V. N. Vapnik, “An Overview of Statistical Learning Theory”, IEEE Transactions on neural networks, Vol. 10, No. 5, Sep. 1999.
  8. C. CORTES and V. VAPNIK, “Support-vector networks”, Machine Learning, 20, pp. 273–297, 1995.
  9. B. Schoölkopf, K. K. Sung, C. J. C. Burges, F. Girosi, P. Niyogi, T. Poggio, and V. Vapnik, “Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers”, IEEE Transactions on signal processing, Vol. 45, No. 11, Nov, 1997.
  10. L. J. Cao and F. E. H. Tay, “Support Vector Machine With Adaptive Parameters in Financial Time Series Forecasting”, IEEE Transactions on neural networks, Vol. 14, No. 6, Nov, 2003.
  11. FU, Y., SHEN, R., and LU, H. “Watermarking scheme based on support vector machine for colour images”, Electronics Letters, 40, (16), pp. 986–987, 2004. https://doi.org/10.1049/el:20040600
  12. D. ANQUITA, S. RIDELLA and S. ROVETTA, “Circuital implementation of support vector machines”, Electronics Letters, 34, (16), pp. 1596–1597, 1998 https://doi.org/10.1049/el:19981092
  13. H. G. Yeom and K. B. Sim, “m-Variance Considered Machines: Membership Function based Classification Algorithms", The 10th International Symposium on Advanced Intelligent Systems (ISIS2009), pp. 165-168, August 17-19, 2009.
  14. J. Lehtonen, P. Jylänki, L. Kauhanen, and M. Sama, “Online Classification of Single EEG Trials During Finger Movements”, IEEE Transaction on Biomedical Engineering, Vol. 55, No. 2, Frb. 2008.
  15. L. Kauhanen, P. Jylanki, J. Lehtonen, P. Rantanen, H. Alaranta and M. Sams, "EEG-Based Brain-Computer Interface for Tetraplegics," Hindawi Publishing Corporation Computational Intelligence and Neuroscience Vol. 2007, Article ID 23864, 11pages doi:10.1155/2007/23864.
  16. L. Citi, R. Poli, C. Cinel, and F. Sepulveda, "P300-Based BCI Mouse With Genetically-Optimized Analogue Control," IEEE Trans. On Neural System and Rehabilitation Engineering, Vol. 16, No. 1, pp. 51-61, Feb. 2008. https://doi.org/10.1109/TNSRE.2007.913184
  17. Gernot R. Müller-Putz, Reinhold Scherer, Gert Pfurtscheller, Rudiger Rupp, "EEG-based neuroprosthesis control: A step towards clinical practice", Neuroscience Letters, 382, pp. 169-174, 2005. https://doi.org/10.1016/j.neulet.2005.03.021
  18. F. Cincotti, F. Aloise, S. Bufalari, G. Schalk, G. Oriolo, A, et al., “Non-Invasive Brain-Computer Interface System to Operate Assistive Device”, Proceedings of the 29th Annual International Conference of the IEEE EMBS Cite Internationale, Lyne, France, Aug. 23-26, 2007.
  19. J. R.Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, T. M. Vaughan, “Brain–computer interfaces for communication and control”, Clinical Neurophysiology,Vol.113, Issue 6, pp.767 June 2002. https://doi.org/10.1016/S1388-2457(02)00057-3
  20. J. J. Daly, J. R. Wolpaw, “Brain-computer interfaces in neurological rehabilitation”, Lancet Neurol. Vol. 7, pp. 1032-1043, 2008. https://doi.org/10.1016/S1474-4422(08)70223-0