EEG Feature Extraction Based on Quantum Particle Swarm Optimizer And Independent Component Analysis_Journal of Biomedical Engineering

Authors：

HUANGLu ^1,3 ,  WANGHong ²

1. Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang 110819, China;
2. School of Mechanical Engineering & Automation, Northeastern University, Shenyang 110819, China;
3. College of Information Engineering, Dalian Ocean University, Dalian 116023, China;

Corresponding author：

WANGHong, Email: hongwang@mail.neu.edu.cn

Keywords：

electroencephalogram; P300; feature extraction; quantum particle swarm optimizer; independent component analysis

DOI：

10.7507/1001-5515.20140093

Video：

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Abstract Full text Figures/Tables Video References Cited by

Feature extraction is a very crucial step in P300-based brain-computer interface (BCI) and independent component analysis (ICA) is a suitable P300 feature extraction method. But at present the convergence performance of the general ICA iteration methods are not very satisfactory. In this paper, a method based on quantum particle swarm optimizer (QPSO) algorithm and ICA technique is put forward for P300 extraction. In this method, quantum computing is used to impel ICA iteration to globally converge faster. It achieved the purpose of extracting P300 rapidly and efficiently. The method was tested on two public datasets of BCI Competition Ⅱ and Ⅲ, and a simple linear classifier was employed to classify the extracted P300 features. The recognition accuracy reached 94.4% with 15 times averaged. The results showed that the proposed method could extract P300 rapidly and the extraction effect did not reduce. It provides an experimental basis for further study of real-time BCI system.

Citation： HUANGLu, WANGHong. EEG Feature Extraction Based on Quantum Particle Swarm Optimizer And Independent Component Analysis. Journal of Biomedical Engineering, 2014, 31(3): 502-505. doi: 10.7507/1001-5515.20140093 Copy

1.	SCHERER R, FALLER J, BALDERAS D, et al. Brain-computer interfacing:more than the sum of its parts[J]. Soft Comput, 2013, 17(2):317-331.
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4.	GANIN I P, SHISHKIN S L, KOCHETOVA A G, et al. The P300 based brain-computer interface:effect of stimulus position in a stimulus train[J]. Human Physiology, 2012, 38(2):5-13.
5.	CECOTTI H, GR? SER A. Convolutional neural networks for P300 detection with application to brain-computer interfaces[J]. IEEE Trans Pattern Anal Mach Intell, 2011, 33(3):433-445.
6.	MUGLER E M, RUF C A, HALDER S, et al. Design and implementation of a P300-based brain-computer interface for controlling an internet browser[J]. IEEE Trans Neural Syst Rehabil Eng, 2010, 18(6):599-609.
7.	ISCAN Z. Detection of P300 wave from EEG data for brain-computer interface applications[J]. Pattern Recognit Image Anal, 2011, 21(3):481-485.
8.	杨立才,李金亮,姚玉翠,等.基于F-score特征选择和支持向量机的P300识别算法[J].生物医学工程学杂志,2008,25(1):23-26,52.
9.	HYVÄRINEN A, OJA E. Independent component analysis:algorithms and applications[J]. Neur Netw, 2000, 13(4-5):411-430.
10.	HYVÄRINEN A. The fixed-point algorithm and maximum likelihood estimation for independent component analysis[J]. Neur Process Lett, 1999, 10(1):1-5.
11.	RATNAWEERA A, HALGAMUGE S K, WATSON H C. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients[J]. Trans. Evol. Comp, 2004, 8(3):240-255.
12.	SUN J, FENG B, XU W B. Particle swarm optimization with particles having quantum behavior^[C]//Proceedings of the 2004 Congress on Evolutionary Computation. Portland, USA:2004:325-331.
13.	BLANKERTZ B, MÜLLER K R, CURIO G, et al. The BCI competition 2003:progress and perspectives in detection and discrimination of EEG single trials[J]. IEEE Trans Biomed Eng, 2004, 51(6):1044-1051.
14.	BLANKERTZ B, MVLLER K R, KRUSIENSKI D J, et al. The BCI competition. Ⅲ:validating alternative approaches to actual BCI problems[J]. IEEE Trans Neural Syst Rehabil Eng, 2006, 14(2):153-159.
15.	FARWELL L A, DONCHIN E. Talking off the top of your head:toward a mental prosthesis utilizing event-related brain potentials[J]. Electroencephalogr Clin Neurophysiol, 1988, 70(6):510-523.
16.	WANG B, LU W K. A fixed-point ICA algorithm with initialization constraint[C]//Proceedings of 2005 International Conference on Communications, Circuit s and Systems. Hong Kong, China:2005, 2:891-894.

1. SCHERER R, FALLER J, BALDERAS D, et al. Brain-computer interfacing:more than the sum of its parts[J]. Soft Comput, 2013, 17(2):317-331.
2. 刘泓,何庆华,闫庆广,等.基于EEG的脑-机接口实用化研究进展[J].生物医学工程学杂志,2010,27(3):702-706.
3. GANIN I P, SHISHKIN S L, KOCHETOVA A G, et al. P300-based brain-computer interface:The effect of the stimulus position in a stimulus train[J]. Hum Physiol,2012,38(2):121-128.
4. GANIN I P, SHISHKIN S L, KOCHETOVA A G, et al. The P300 based brain-computer interface:effect of stimulus position in a stimulus train[J]. Human Physiology, 2012, 38(2):5-13.
5. CECOTTI H, GR? SER A. Convolutional neural networks for P300 detection with application to brain-computer interfaces[J]. IEEE Trans Pattern Anal Mach Intell, 2011, 33(3):433-445.
6. MUGLER E M, RUF C A, HALDER S, et al. Design and implementation of a P300-based brain-computer interface for controlling an internet browser[J]. IEEE Trans Neural Syst Rehabil Eng, 2010, 18(6):599-609.
7. ISCAN Z. Detection of P300 wave from EEG data for brain-computer interface applications[J]. Pattern Recognit Image Anal, 2011, 21(3):481-485.
8. 杨立才,李金亮,姚玉翠,等.基于F-score特征选择和支持向量机的P300识别算法[J].生物医学工程学杂志,2008,25(1):23-26,52.
9. HYVÄRINEN A, OJA E. Independent component analysis:algorithms and applications[J]. Neur Netw, 2000, 13(4-5):411-430.
10. HYVÄRINEN A. The fixed-point algorithm and maximum likelihood estimation for independent component analysis[J]. Neur Process Lett, 1999, 10(1):1-5.
11. RATNAWEERA A, HALGAMUGE S K, WATSON H C. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients[J]. Trans. Evol. Comp, 2004, 8(3):240-255.
12. SUN J, FENG B, XU W B. Particle swarm optimization with particles having quantum behavior^[C]//Proceedings of the 2004 Congress on Evolutionary Computation. Portland, USA:2004:325-331.
13. BLANKERTZ B, MÜLLER K R, CURIO G, et al. The BCI competition 2003:progress and perspectives in detection and discrimination of EEG single trials[J]. IEEE Trans Biomed Eng, 2004, 51(6):1044-1051.
14. BLANKERTZ B, MVLLER K R, KRUSIENSKI D J, et al. The BCI competition. Ⅲ:validating alternative approaches to actual BCI problems[J]. IEEE Trans Neural Syst Rehabil Eng, 2006, 14(2):153-159.
15. FARWELL L A, DONCHIN E. Talking off the top of your head:toward a mental prosthesis utilizing event-related brain potentials[J]. Electroencephalogr Clin Neurophysiol, 1988, 70(6):510-523.
16. WANG B, LU W K. A fixed-point ICA algorithm with initialization constraint[C]//Proceedings of 2005 International Conference on Communications, Circuit s and Systems. Hong Kong, China:2005, 2:891-894.

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