운동심상 EEG 패턴분석을 위한 HSA 기반의 HMM 최적화 방법

다국어 초록 (Multilingual 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 in 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.

목차 (Table of Contents)

• Abstract
• Ⅰ. 서론
• Ⅱ. 관련 연구
• Ⅲ. 제안 모델 및 방법
• Ⅳ. 시뮬레이션 및 결과분석
• Ⅴ. 결론
• 참고문헌

참고문헌 (Reference)

1 이대우, "유전 이론을 이용한 위성 임무 스케줄링 알고리즘의 제어상수에 따른 적합도 변화 연구" 제어·로봇·시스템학회 16 (16): 572-578, 2010

2 이충헌, "뇌파기반 집중도 전송 및 BCI 적용에 관한 연구" 대한전자공학회 46 (46): 41-46, 2009

3 심귀보, "뇌와 컴퓨터의 인터페이스를 위한 뇌파 측정 및 분석 방법" 한국지능시스템학회 18 (18): 605-610, 2008

4 K.-J. Won, "Training HMM structure with genetic algorithm for biological sequence analysis" 20 (20): 3613-3619, 2004

5 B. Blankertz, "The non-invasive berlin brain-computer interface: fast acquisition of effective performance in untrained subjects" 37 (37): 539-550, 2007

6 "Parameter-setting-free harmony search algorithm" 217 (217): 3881-3889, 201012

7 H. K. Lee, "PCA+HMM+SVM for EEG pattern classification" 541-544, 2003

8 H. K. Lee, "PCA+HMM+SVM for EEG pattern classification" 541-544, 2003

9 K. E. Ko, "Optimization of HMM structure with HSA for EEG sequence analysis" 2011

10 C. W. Chau, "Optimization of HMM by a genetic algorithm" 1727-1730, 1997

11 D. Obermaier, "Hidden markov models for online classification of single trial EEG data" 22 (22): 1299-1309, 2001

12 A. Delorme, "EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics" 134 : 9-21, 2004

13 W.-Y. Hsu, "EEG-based motor imagery classification using neuro-fuzzy prediction and wavelet fractal features" 189 (189): 295-302, 2010

14 W.-Y. Hsu, "EEG-based motor imagery classification using enhanced active segment selection and adaptive classifier" In Press, Corrected Proof, Available online 17 : 2011

15 W.-Y. Hsu, "EEG-based motor imagery analysis using weighted wavelet transform features" 176 (176): 310-318, 2009

16 R. Durbin, "Biological sequence analysis" Cambridge University Press 1998

17 L. R. Rabiner, "A tutorial on hidden markov models and selected applications in speech recognition" 77 (77): 257-285, 1989

18 한창호, "3차원 체인코드와 은닉마르코프 모델을 이용한 권투모션 인식" 제어·로봇·시스템학회 16 (16): 756-760, 2010