Browse > Article
http://dx.doi.org/10.5392/JKCA.2010.10.6.027

Wavelet-Based Minimized Feature Selection for Motor Imagery Classification  

Lee, Sang-Hong (경원대학교 일반대학원 전자계산학과)
Shin, Dong-Kun (삼육대학교 컴퓨터학부)
Lim, Joon-S. (경원대학교 전자거래학부)
Publication Information
The Journal of the Korea Contents Association / v.10, no.6, 2010 , pp. 27-34 More about this Journal
Abstract
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.
Keywords
Brain-computer Interface; Wavelet Transforms; Feature Extraction; Feature Selection;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 이상홍, 임준식, “간질 분류를 위한 NEWFM 기반의 특징입력 및 퍼지규칙 추출”, 한국인터넷정보학회 논문지, 제10권, 제5호, pp.127-133, 2009.   과학기술학회마을
2 신동근, 장진홍, 이상홍, 임준식, 이정현, “가중퍼지소속함수 기반 신경망과 웨이블릿 변환을 이용한 심실 빈맥/세동 검출”, 한국콘텐츠학회 논문지, 제9권, 제7호, pp.19-26, 2009.
3 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.   DOI   ScienceOn
4 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.   DOI   ScienceOn
5 Minh Hoai Nguyen and Fernando de la Torre, “Optimal feature selection for support vector machines,” Pattern Recognition, Vol.43, pp.584-591, 2010.   DOI   ScienceOn
6 Jose Martinez Sotoca and Filiberto Pla, “Supervised feature selection by clustering using conditional mutual informationbaseddistances,” Pattern Recognition, Vol.43, pp.2068-2081, 2010.   DOI   ScienceOn
7 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.   DOI   ScienceOn
8 이상홍, 임준식, “자동 특징 추출기법에 의한 최소의 주식예측 특징선택”, 한국지능시스템학회 논문지, 제19권, 제2호, pp.206-211, 2009.   과학기술학회마을   DOI
9 G. Pfurtscheller, F. H. Lopes da Silva, “Event-related EEG/MEG synchronization and desynchronization: basic principles,” Clinical Neurophysiology, Vol.110, pp.1842-1857, 1999.   DOI   ScienceOn
10 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.   DOI   ScienceOn
11 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.
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.   DOI   ScienceOn
13 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.   DOI   ScienceOn
14 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.   DOI   ScienceOn
15 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.   DOI   ScienceOn
16 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.   DOI   ScienceOn
17 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.   DOI   ScienceOn
18 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.
19 S. Mallat, “Zero crossings of a wavelet transform,” IEEE Trans. Inf. Theory, Vol.37, No.4, pp.1019-1033, Jul. 1991.   DOI   ScienceOn
20 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.   DOI   ScienceOn
21 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.
22 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.   DOI   ScienceOn
23 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.
24 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.   DOI   ScienceOn
25 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.   DOI   ScienceOn
26 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.   DOI   ScienceOn