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http://dx.doi.org/10.5391/JKIIS.2011.21.6.786

Optimal EEG Feature Extraction using DWT for Classification of Imagination of Hands Movement  

Chum, Pharino (Chung-Ang University, School of Electrical and Electronics Engineering)
Park, Seung-Min (Chung-Ang University, School of Electrical and Electronics Engineering)
Ko, Kwang-Eun (Chung-Ang University, School of Electrical and Electronics Engineering)
Sim, Kwee-Bo (Chung-Ang University, School of Electrical and Electronics Engineering)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.21, no.6, 2011 , pp. 786-791 More about this Journal
Abstract
An optimal feature selection and extraction procedure is an important task that significantly affects the success of brain activity analysis in brain-computer interface (BCI) research area. In this paper, a novel method for extracting the optimal feature from electroencephalogram (EEG) signal is proposed. At first, a student's-t-statistic method is used to normalize and to minimize statistical error between EEG measurements. And, 2D time-frequency data set from the raw EEG signal was extracted using discrete wavelet transform (DWT) as a raw feature, standard deviations and mean of 2D time-frequency matrix were extracted as a optimal EEG feature vector along with other basis feature of sub-band signals. In the experiment, data set 1 of BCI competition IV are used and classification using SVM to prove strength of our new method.
Keywords
Brain-Computer Interface; Electroencephalogram; Feature Extraction; Discrete Wavelet Transform; Student's-t Statistic;
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1 B. Vladimir, "BCI Competition 2003-Data Sets Ib and IIb: Feature Extraction Event-Related Brain Potentials With the Continuous Wavelet Transform and the t-Value Scalogram," In IEEE Transaction on Biomedical Enginerring, vol. 51, no. 6, 2004.
2 S. Bhattacharyya, A. Khasnobish, A. Konar, D.N. Tibarrewala, A.K. Naga, "Performance Analysis of Left/Right Hand Movement Classification from EEG Signal by Intelligent Algorithms," In Computational Intelligence, Cognitive Algorithms, Mind, and Brain(CCMB, 2011 IEEE Symposium), 2011.
3 A.J. Perez-Jimenez and J.C. Perez-Cortes, "Genetic Algorithm for Linear Feature Extraction," Proceedings on Vision System-Segmentation and Pattern Recognition, pp. 423-436, 2007.
4 J.R. Wolpaw, N. Birbaumer, W.J. Heetderks, D.J. McFarland, P.H. Peckham, G. Schalk, E. Donchin, L.A. Quatrano, C.J. Robinson, T.M. Vaughan, "Brain-computer interface technology: a review of the first international meeting," In Rehabilitation Engineering, IEEE Transactions, vol. 8, no. 2, 2000.
5 H.N. Jeremy, L. Thomas, S. Michael, H. Thilo, W. Guido, E. Christain, S. Bernhard, B. Niels, "Classifying Event-Related Desynchronization in EEG, ECoG and MEG signals," In Towards Brain-Computer Interfacing, vol. 4174, pp. 404-413, 2006.
6 F. Lotte, M. Congedo, A. Lecuyer, F. Lamarhe, B. Arnaldi, "A review of classification algorithms for EEG-based brain-computer interfaces," In Journal of Neural Engineering 4, pp. R1-R13, 2007.   DOI   ScienceOn
7 J.R. Wolpaw, N. Birbaumer, D.J. McFarland, G. Pfurtscheller, T.M. Vaughan, "Brain-computer interfaces for communications and control," In Clinical Neurophysiology, vol. 113, pp. 767-791, 2002.   DOI   ScienceOn
8 J.R. Wolpaw, N. Birbaumer, D.J. McFarland, G. Pfurtscheller, T.M. Vaughan, "Brain-computer interfaces for communications and control," In Clinical Neurophysiology, vol. 113, pp. 767-791, 2002.   DOI   ScienceOn
9 J.S. Vincent, B. Ajit , R. Raghuveer, S. Kenneth, "Wavelet Analysis of Neuroelectric Waveforms: A conceptual Tutorial," In Brain and Language, vol. 66, issue 1, pp. 7-60, 1999.   DOI   ScienceOn
10 P.S. Addison, "Wavelet transform and the ECG: a review," In Physiological Measurement, vol 26, no. 5, pp. R155-R199, 2005.   DOI   ScienceOn
11 H.B. Kekre, K. Patil, "Standard Deviation of Mean and Variance of Rows and Columns of Images for CBIR," In International Journal of Computer and Information Engineering, 2009.