한국콘텐츠학회논문지 (The Journal of the Korea Contents Association)

  • 제10권6호
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  • Pages.27-34
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  • 2010
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  • 1598-4877(pISSN)
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  • 2508-6723(eISSN)
한국콘텐츠학회 (The Korea Contents Association)
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운동 형상 분류를 위한 웨이블릿 기반 최소의 특징 선택

Wavelet-Based Minimized Feature Selection for Motor Imagery Classification

  • 이상홍 (경원대학교 일반대학원 전자계산학과) ;
  • 신동근 (삼육대학교 컴퓨터학부) ;
  • 임준식 (경원대학교 전자거래학부)
  • 투고 : 2010.04.28
  • 심사 : 2010.05.31
  • 발행 : 2010.06.28
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초록

본 논문은 가중 퍼지소속함수 기반 신경망(neural network with weighted fuzzy membership functions, NEWFM)과 웨이블릿 기반의 특징 추출기법을 사용하여 왼쪽 또는 오른쪽의 운동 형상을 분류하는 방안을 제안하고 있다. 초기 특징을 추출하기 위해서 첫 번째 단계에서 웨이블릿 변환(wavelet transforms)을 이용하여 뇌파(electroencephalogram, EEG) 신호로부터 웨이블릿 계수들을 추출하였다. 두 번째 단계에서는 첫 번째 단계에서 추출한 웨이블릿 계수들을 통계적인 방법인 주파수 분포와 주파수 변동량을 이용하여 60개의 초기 특징을 추출하였다. 이들 60개의 초기 특징은 NEWFM에서 제공하는 비중복면적 분산 측정법에 의해 중요도가 가장 낮은 특징을 하나씩 제거되면서 정확도가 가장 높은 6개의 최소 특징을 선택되었다. 이들 6개의 최소 특징을 NEWFM의 입력으로 사용하여 86.43%의 정확도를 구하였다.

This paper presents a methodology for classifying left and right motor imagery using a neural network with weighted fuzzy membership functions (NEWFM) and wavelet-based feature extraction. Wavelet coefficients are extracted from electroencephalogram(EEG) signal by wavelet transforms in the first step. In the second step, sixty numbers of initial features are extracted from wavelet coefficients by the frequency distribution and the amount of variability in frequency distribution. The distributed non-overlap area measurement method selects the minimized number of features by removing the worst input features one by one, and then minimized six numbers of features are selected with the highest performance result. The proposed methodology shows that accuracy rate is 86.43% with six numbers of features.

키워드

참고문헌

  1. Qi Xu, Hui Zhou, Yongji Wang, and Jian Huang, “Fuzzy support vector machine for classification of EEG signals using wavelet-based features,” Medical Engineering & Physics, Vol.31, pp.858-865, 2009.
  2. Wei-Yen Hsu and Y. N. Sun, “EEG-based motor imagery analysis using weighted wavelet transform features,” Journal of Neuroscience Methods, Vol.176, pp.310-318, 2009. https://doi.org/10.1016/j.jneumeth.2008.09.014
  3. Wu Ting, Yan Guo-zheng, Yang Bang-hua, and Sun Hong, “EEG feature extraction based on wavelet packet decomposition for brain computer interface,” Measurement, Vol.41, pp.618-625, 2008. https://doi.org/10.1016/j.measurement.2007.07.007
  4. George Townsend and Yi Feng, “Using phase information to reveal the nature of event-related desynchronization,” Biomedical Signal Processing and Control, Vol.3, pp.192-202, 2008. https://doi.org/10.1016/j.bspc.2008.01.003
  5. Mingjun Zhong, Fabien Lotte, Mark Girolami, and Anatole Lecuyer, “Classifying EEG for brain computer interfaces using Gaussian processes,” Pattern Recognition Letters, Vol.29, pp.354-359, 2008. https://doi.org/10.1016/j.patrec.2007.10.009
  6. Baharan Kamousi, Zhongming Liu, and Bin He, “Classification of Motor Imagery Tasks for Brain-Computer Interface Applications by Means of Two Equivalent Dipoles Analysis,” IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, Vol.13, pp.166-171, 2005. https://doi.org/10.1109/TNSRE.2005.847386
  7. J. S. Lim, “Finding Features for Real-Time Premature Ventricular Contrac-tion Detection Using a Fuzzy Neural Network System,” IEEE TRANSACTIONS ON NEURAL NETWORKS, Vol.20, pp.522-527, 2009. https://doi.org/10.1109/TNN.2008.2012031
  8. J. S. Lim, “Finding Fuzzy Rules by Neural Network with Weighted Fuzzy Membership Function,” International Journal of Fuzzy Logic and Intelligent Systems, Vol.4, No.2, pp.211-216, 2004. https://doi.org/10.5391/IJFIS.2004.4.2.211
  9. J. S. Lim, D. Wang, Y.-S. Kim, and S. Gupta, “A neuro-fuzzy approach for diagnosis of antibody deficiency syndrome,” Neurocomputing, Vol.69, pp.969-974, 2006. https://doi.org/10.1016/j.neucom.2005.06.009
  10. J. S. Lim, T-W Ryu, H-J Kim, and S. Gupta, “Feature Selection for Specific Antibody Deficiency Syndrome by Neural Network with Weighted Fuzzy Membership Functions,” LNCS 3614, pp.811-820, 2005.
  11. S. Mallat, “Zero crossings of a wavelet transform,” IEEE Trans. Inf. Theory, Vol.37, No.4, pp.1019-1033, Jul. 1991. https://doi.org/10.1109/18.86995
  12. Abdulhamit Subasi, “EEG signal classification using wavelet feature extraction and a mixture of expert model,” Expert Systems with Applications, Vol.32, pp.1084-1093, 2007. https://doi.org/10.1016/j.eswa.2006.02.005
  13. A. Kandaswamy, C. Sathish Kumar, Rm. Pl. Ramanathan, S. Jayaraman, and N. Malmurugan, “Neural classification of lung sounds using wavelet coefficients,” Computers in Biology and Medicine, Vol.34, pp.523-537, 2004. https://doi.org/10.1016/S0010-4825(03)00092-1
  14. G. Tzanetakis, G. Essl, P. Cook, “Audio analysis using the discrete wavelet transform, in: C.E.D’ Attellis, V.V. Kluev, N. Mastorakis (Eds.),” Mathematics and Simlulation with Biological Economical and Musicoacoustical Applications, WSES Press, New York, pp.318-323, 2001.
  15. S. H. Lee and J. S. Lim, “KOSPI time series analysis using neural net-work with weighted fuzzy membership functions,” KES-AMSTA(LNAI 4953), pp.53-62, 2008.
  16. Abdulhamit Subasi, “EEG signal classification using wavelet feature extraction and a mixture of expert model,” Expert Systems with Applications, Vol.32, pp.1084-1093, 2007. https://doi.org/10.1016/j.eswa.2006.02.005
  17. H. Adeli, Z. Zhou, and N. Dadmehr, “Analysis of EEG records in an epileptic patient using wavelet transform,” Journal of Neuroscience Methods, Vol.123, pp.69-87, 2003. https://doi.org/10.1016/S0165-0270(02)00340-0
  18. Yi Hong, Sam Kwong, Yuchou Chang, and Qingsheng Ren, “Unsupervised feature selection using clustering ensembles and population based incremental learning algorithm,” Pattern Recognition, Vol.41, pp.2742-2756, 2008. https://doi.org/10.1016/j.patcog.2008.03.007
  19. Minh Hoai Nguyen and Fernando de la Torre, “Optimal feature selection for support vector machines,” Pattern Recognition, Vol.43, pp.584-591, 2010. https://doi.org/10.1016/j.patcog.2009.09.003
  20. Jose Martinez Sotoca and Filiberto Pla, “Supervised feature selection by clustering using conditional mutual informationbaseddistances,” Pattern Recognition, Vol.43, pp.2068-2081, 2010. https://doi.org/10.1016/j.patcog.2009.12.013
  21. Patricia E.N. Lutu and Andries P. Engelbrecht, “A decision rule-based method for feature selection in predictive data mining,” Expert Systems with Applications, Vol.37, pp.602-609, 2010. https://doi.org/10.1016/j.eswa.2009.06.031
  22. G. Pfurtscheller, F. H. Lopes da Silva, “Event-related EEG/MEG synchronization and desynchronization: basic principles,” Clinical Neurophysiology, Vol.110, pp.1842-1857, 1999. https://doi.org/10.1016/S1388-2457(99)00141-8
  23. G. Pfurtscheller, C. Brunner, A. Schlogl, and F. H. Lopes da Silva, “Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks,” NeuroImage, Vol.31, pp.153-159, 2006. https://doi.org/10.1016/j.neuroimage.2005.12.003
  24. 이상홍, 임준식, “자동 특징 추출기법에 의한 최소의 주식예측 특징선택”, 한국지능시스템학회 논문지, 제19권, 제2호, pp.206-211, 2009. https://doi.org/10.5391/JKIIS.2009.19.2.206
  25. 이상홍, 임준식, “간질 분류를 위한 NEWFM 기반의 특징입력 및 퍼지규칙 추출”, 한국인터넷정보학회 논문지, 제10권, 제5호, pp.127-133, 2009.
  26. 신동근, 장진홍, 이상홍, 임준식, 이정현, “가중퍼지소속함수 기반 신경망과 웨이블릿 변환을 이용한 심실 빈맥/세동 검출”, 한국콘텐츠학회 논문지, 제9권, 제7호, pp.19-26, 2009.

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