基于多特征和BP神经网络的脑-机接口研究
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摘要
研究了一种基于运动想象识别的脑-机接口(BCI)系统,通过提取想象过程中的脑电信号(EEG)中Alpha波特征,采用多特征分类的方法,以提高脑-机接口系统运动想象识别的正确率。针对脑电信号单特征分类精确度低、耗时长等缺点,采用自回归模型法、统计特征提取和频域分析的方法对Alpha波提取多个特征值,利用BP神经网络进行分类,对运动想象进行识别。通过实验验证了其识别率较高,取得了预期的效果,证明了多特征融合结合BP神经网络运用于脑机接口系统的可行性。
This research is carried out re-designing a brain-computer interface( BCI) system based on motor imagery recognition through extracting features of Alpha wave in electroencephalography( EEG) signal during motor imagery process, using multi-feature classification method in order to increase the accuracy of classification. Aiming at the shortcomings such as low accuracy and time-consuming when one feature is adopted in the classification process, methods including AR model, statistical characteristics extrac-tion and frequency domain analysis, etc. are taken to extract various features of Alpha wave. BP neural network is used to classify features. The system is designed to identify motor imagery and through experimental verification, it has achieved expected effect with high classification accuracy. The research proves the feasibility of brain-computer interface system combining multi-feature integra-tion with BP neural network.
This research is carried out re-designing a brain-computer interface( BCI) system based on motor imagery recognition through extracting features of Alpha wave in electroencephalography( EEG) signal during motor imagery process, using multi-feature classification method in order to increase the accuracy of classification. Aiming at the shortcomings such as low accuracy and time-consuming when one feature is adopted in the classification process, methods including AR model, statistical characteristics extrac-tion and frequency domain analysis, etc. are taken to extract various features of Alpha wave. BP neural network is used to classify features. The system is designed to identify motor imagery and through experimental verification, it has achieved expected effect with high classification accuracy. The research proves the feasibility of brain-computer interface system combining multi-feature integra-tion with BP neural network.
引文
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[9]王宁,孙广金,刘学文.一种基于脑机接口的头盔显示/瞄准系统设计[J].电子技术应用,2015,41(5):149-151,155.
[10]杨彬.基于脑-机接口的想象运动脑电信号的分析[D].兰州:兰州理工大学,2008.
[11]Ronager.Power spectrum analysis of EMG pattern in normal and diseased muscles[J].J Neurol Sci,1989,94(1-3):283-294.
[12]邢进良.BP神经网络模型及其应用[J].沙洋师范高等专科学校学报,2007(5):46-49.
[13]李颖洁,邱意弘,朱贻盛.脑电信号分析方法及其应用[M].北京:科学出版社,2009.
[14]肖婵.基于脑电波的注意力训练研究[D].武汉:华中师范大学,2016.
[2]ARROYO S,LESSER R P,GORDON B et al.Functional significance of the mu rhythm of human cortex:an eletrophysiologic study with subdural electrodes[J].Electroenceph clin Neurophysiol 1993,87:76-87.
[3]陈骞.基于脑电Alpha波的BCI电视遥控系统的设计与实验研究[D].天津:天津大学,2005.
[4]INOUE K,MATSUOKA S.Pattern recognition of EEG signals during right and left hand motor imagery based on AR-model[J].Clinical Neurophysiology,2007,118(10):209-209.
[5]张莉.表面肌电信号模式识别及其运动分析[D].长春:吉林大学,2013.
[6]杨大鹏,姜力,赵京东,等.基于脑电信号的高智能假手控制[J].吉林大学学报(工学版),2008(5):1225-1230.
[7]MATTHIAS K,PETER M.BCI competition 2003-data setⅡb:support vector machines for the P300 speller paradigm[J].IEEE Transactions on Biomedical Engineering,2004,51(6):1073-1076.
[8]李明爱,王蕊,郝冬梅.想象左右手运动的脑电特征提取及分类研究[J].中国生物医学工程学报,2009(2):166-170,176.
[9]王宁,孙广金,刘学文.一种基于脑机接口的头盔显示/瞄准系统设计[J].电子技术应用,2015,41(5):149-151,155.
[10]杨彬.基于脑-机接口的想象运动脑电信号的分析[D].兰州:兰州理工大学,2008.
[11]Ronager.Power spectrum analysis of EMG pattern in normal and diseased muscles[J].J Neurol Sci,1989,94(1-3):283-294.
[12]邢进良.BP神经网络模型及其应用[J].沙洋师范高等专科学校学报,2007(5):46-49.
[13]李颖洁,邱意弘,朱贻盛.脑电信号分析方法及其应用[M].北京:科学出版社,2009.
[14]肖婵.基于脑电波的注意力训练研究[D].武汉:华中师范大学,2016.