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http://dx.doi.org/10.5302/J.ICROS.2011.17.8.747

HSA-based HMM Optimization Method for Analyzing EEG Pattern of Motor Imagery  

Ko, Kwang-Eun (Chung-Ang University)
Sim, Kwee-Bo (Chung-Ang University)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.17, no.8, 2011 , pp. 747-752 More about this Journal
Abstract
HMMs (Hidden Markov Models) are widely used for biological signal, such as EEG (electroencephalogram) sequence, analysis because of their ability to incorporate sequential information in their structure. A recent trends of research are going after the biological interpretable HMMs, and we need to control the complexity of the HMM so that it has good generalization performance. So, an automatic means of optimizing the structure of HMMs would be highly desirable. In this paper, we described a procedure of classification of motor imagery EEG signals using HMM. The motor imagery related EEG signals recorded from subjects performing left, right hand and foots motor imagery. And the proposed a method that was focus on the validation of the HSA (Harmony Search Algorithm) based optimization for HMM. Harmony search algorithm is sufficiently adaptable to allow incorporation of other techniques. A HMM training strategy using HSA is proposed, and it is tested on finding optimized structure for the pattern recognition of EEG sequence. The proposed HSA-HMM can performs global searching without initial parameter setting, local optima, and solution divergence.
Keywords
hidden markov model; harmony search algorithm; optimization; EEG;
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Times Cited By KSCI : 4  (Citation Analysis)
Times Cited By SCOPUS : 1
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1 W.-Y. Hsu, "EEG-based motor imagery classification using enhanced active segment selection and adaptive classifier," Computers in Biology and Medicine, In Press, Corrected Proof, Available online 17, 2011.
2 C. W. Chau, S. Kwong, C. K. Diu, and W. R. Fahrner, "Optimization of HMM by a genetic algorithm," Proc. ICASSP-97, vol. 3, pp. 1727-1730, 1997.
3 D. Obermaier, C. Guger, C. Neuper, and G. Pfurtscheller, "Hidden markov models for online classification of single trial EEG data," Pattern Recognition Letters, vol. 22, no. 12, pp. 1299-1309, 2001.   DOI
4 Z. W. Geem and K. B. Sim, "Parameter-setting-free harmony search algorithm," Applied Mathematics and Computation, vol. 217, pp. 3881-3889, 2010.   DOI   ScienceOn
5 H. K. Lee and S. J. Choi, "PCA+HMM+SVM for EEG pattern classification," Proc. 7th Inter. Symp. on Signal Processing and its Applications, vol. 1, pp. 541-544, July 2003.
6 C. H. Han, C. S. Oh, and B. W. Choi, "Recognition of fighting motion using a 3D-chain code and HMM," Journal of Institute of Control, Robotics, and Systems (in Korean), vol. 16, no. 8, pp. 756-760, 2010.   과학기술학회마을   DOI
7 D. W. Lee, K. R, Cho, and S. W. Baek, "Fitness change of mission scheduling algorithm using genetic theory according to the control constants," Journal of Institute of Control, Robotics, and Systems (in Korean), vol. 16, no. 6, pp. 572-578, 2010.   과학기술학회마을   DOI
8 A. Delorme and S. Makeig, "EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics," Journal of Neuroscience Methods, vol. 134, pp. 9-21, 2004   DOI
9 B. Blankertz, G. Dornhege, M. Krauledat, K.-R. Műller, 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   DOI
10 L. R. Rabiner, "A tutorial on hidden markov models and selected applications in speech recognition," Proc. IEEE, vol. 77, no. 2, pp. 257-285, 1989.   DOI
11 H. K. Lee and S. J. Choi, "PCA+HMM+SVM for EEG pattern classification," Proc. of the 7th International Symposium in Signal Processing and Its Applications, pp. 541-544, 2003.
12 C. H, Lee, J. W. Kwon, G. D. Kim, J. E. Hong, D. S. Shin, and D. H. Lee, "A study on EEG based concentration transmission and brain computer interface application," Journal of the Electrics Engineering of Korea, vol. 46, SC, no. 2, pp. 41-45, 2009.   과학기술학회마을
13 K. B. Sim, H. G. Yeom, and I. Y. Lee, "EEG signals measurement and analysis method for brain-computer interface," Journal of Korean Institute of Intelligent Systems (in Korean), vol. 18, no. 5, pp. 605-610, 2008.   과학기술학회마을   DOI
14 K. E. Ko and K. B. Sim, "Optimization of HMM structure with HSA for EEG sequence analysis," 2011 ICROS Annual Conference (in Korean), 2011.
15 R. Durbin, S. Eddy, A. Krogh, and G. Mitchison, "Biological sequence analysis," Cambridge, UK: Cambridge University Press, 1998.
16 W.-Y. Hsu and Y.-N. Sun, "EEG-based motor imagery analysis using weighted wavelet transform features," Journal of Neuroscience Methods, vol. 176, no. 2, pp. 310-318, 2009.   DOI
17 W.-Y. Hsu, "EEG-based motor imagery classification using neuro-fuzzy prediction and wavelet fractal features," Journal of Neuroscience Methods, vol. 189, no. 2, pp. 295-302, 2010.   DOI
18 K.-J. Won, A. Prűgel-Bennett, and A. Krogh, "Training HMM structure with genetic algorithm for biological sequence analysis," Bioinformatics, vol. 20, no. 18, pp. 3613-3619, Dec. 2004.   DOI   ScienceOn