独立分量分析和遗传算法相结合的运动想象频带优化
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摘要
对结合独立分量分析(independent component analysis,简称ICA)和遗传算法(genetic algorithm,简称GA)的运动想象脑电(motor imagery electroencephalogram,简称MI-EEG)特征检测及其优化方法开展研究.设计了基于ICA的MI-EEG分类算法.在此基础上,针对不同受试个体,用GA算法对运动想象诱发的事件相关去同步(event-related desynchronization,简称ERD)频段进行优化选择,用以改善运动想象脑-机接口(brain-computer interface,简称BCI)系统的识别率.实验结果表明,基于ICA的GA算法特征优化方法具有较好的可靠性和实用性,可用于在线BCI的设计与实现.
The paper focused on the research of feature detection and the optimization algorithm of motor imagery electroencephalogram(MI-EEG) by using independent component analysis(ICA)and genetic algorithm(GA),and a MI-EEG classification method based on ICA was proposed in this paper.For different individual,the method used GA to optimize selection of the optimal event-related desynchronization(ERD)band which was induced by motor imagery,and it improved the recognition rate of motor imagery brain-computer interface(BCI)system.Experimental results revealed that the feature optimization of GA method based on ICA,which not only had better reliability and practicability,but could be used for the design and implementation of online BCI.
The paper focused on the research of feature detection and the optimization algorithm of motor imagery electroencephalogram(MI-EEG) by using independent component analysis(ICA)and genetic algorithm(GA),and a MI-EEG classification method based on ICA was proposed in this paper.For different individual,the method used GA to optimize selection of the optimal event-related desynchronization(ERD)band which was induced by motor imagery,and it improved the recognition rate of motor imagery brain-computer interface(BCI)system.Experimental results revealed that the feature optimization of GA method based on ICA,which not only had better reliability and practicability,but could be used for the design and implementation of online BCI.
引文
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[21]雷英杰,张善文,李续武,等.Matlab遗传算法工具箱及应用[M].西安:西安电子科技大学出版社,2005.
[22]LU J,ZHANG J L.The adaptive spatial filtering algorithm for brain-computer interface based on ERS/ERD[J].Electronics&Information Technology,2009,31(2):216-220.
[23]周颖,王涛,冯焕清,等.运动想象脑电的ERD/ERS分析[J].北京生物医学工程,2004,23(4):263-268.
[2]程明,高上凯.基于脑电信号的脑-计算机接口[J].北京生物医学工程,2000,19(2):113-118.
[3]VAN GERVE M,FARQUHA J,SCHAEFER R,et al.The brain-computer interface cycle[J].Journal of Neural Engineering,2009,6(4):1-10.
[4]KELLY S P,LALOR E C,REILLY R B,et al.Visual spatial attention tracking using high-density SSVEPdata for independent brain-computer communication[J].Neural System and Rehabilitation Engineering,2005,13(2):172-178.
[5]PFURTSCHELLER G D,SILVA F H.Event-related EEG/MEG synchronization and desynchronization:basic principles[J].Clinical Neurophysiology,1999,110(11):1842-1857.
[6]HINTERBERGER T,WILHELM B,MELLINGER J,et al.A device for the detection of cognitive brain functions in completely paralyzed or unresponsive patients[J].Biomedical Engineering,2005,52(2):211-220.
[7]BLANKERTZ B,MULLER K R,KRUSIENSKI D J,et al.The BCI competition III:Validating alternative approaches to actual BCI problems[J].Neural Systems and Rehabilitation Engineering,2006,14(2):153-159.
[8]SUMAN K,MITRA S N,SARBADHIKAR I.Iterative function system and genetic algorithm-based EEGcompression[J].Medical Engineering and Physics,1997,19(7):605-617.
[9]HASAN O.Optimal classification of epileptic seizures in EEG using wavelet analysis and gentic algorithm[J].Signal Processing,2008,88(7):1858-1867.
[10]JONES E,PUNKLE P,DASGUPTA N,et al.Genetic algorithm wavelet design for signal classification[J].Pattern Analysis and Machine Intelligence,2001,23(8):890-895.
[11]乔均俭,付君丽,徐雅玲.应用遗传算法原理确定函数的最优解[J].微计算机信息,2007,23(18):240-241.
[12]杨福生,洪波.独立分量分析的原理与应用[M].北京:清华大学出版社,2006.
[13]YAMAWAKI N,WILKE C,LIU Z,et al.An enhanced time-frequency-spatial approach for motor imagery classification[J].Neural Systems and Rehabilitation Engineering,2006,14(2):250-254.
[14]BRUNNER C,NAEEM M,LEEB R,et al.Spatial filtering and selection of optimized components in four class motor imagery EEG data using independent components analysis[J].Pattern Recognition Letters,2007,28(8):957-964.
[15]ARVANEH M,GUAN C,ANG K K.Optimizing spatial filters by minimizing within-class dissimilarities in electroencephalogram-based brain-computer interface[J].Neural Networks and Learning System,2013,24(4):610-619.
[16]LINDING-LEON C,BOUGRAIN L.A comparison between different multiclass common spatial pattern approaches for identification of motor imagery tasks[C]//International Brain-Computer Interface Conference,2014:1872-1877.
[17]GRANDCHAMP R,BRABOSZCZ C,MAKEIG S.Stability of ICA decomposition across within-subject EEGdatasets[C]//Engineering in Medicine and Biology Society,International Conference of the IEEE,2012:6735-6739.
[18]WANG Y J,WANG Y T,JUNG T P.Translation of EEG spatial filters from resting to motor imagery using independent component analysis[J].PLoS ONE,2012,7(5):e37665.
[19]葛继科,邱玉辉,吴春明,等.遗传算法研究综述[J].计算机应用研究,2008,25(10):2911-2916.
[20]吴小培,周蚌艳,张磊,等.运动想象脑-机接口中的ICA滤波器设计[J].生物物理学报,2014,30(7):540-554.
[21]雷英杰,张善文,李续武,等.Matlab遗传算法工具箱及应用[M].西安:西安电子科技大学出版社,2005.
[22]LU J,ZHANG J L.The adaptive spatial filtering algorithm for brain-computer interface based on ERS/ERD[J].Electronics&Information Technology,2009,31(2):216-220.
[23]周颖,王涛,冯焕清,等.运动想象脑电的ERD/ERS分析[J].北京生物医学工程,2004,23(4):263-268.