基于改进共空间模式与深度信念网络的脑电信号识别算法研究
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摘要
使用脑电信号控制智能轮椅是智能轮椅的一种新型控制方式,其控制中最大的问题在于脑电信号的识别与分类,尤其是对多种脑电信号的分类.为了提高多种脑电信号识别与分类的准确度,本文提出了将改进共空间模式与深度信念网络运用于脑电信号识别与分类中.采用Emotiv EPOC+脑电采集仪采集多种脑电信号,改进共空间模式针对多种脑电信号进行特征信号提取,深度信念网络对提取的特征信号进行识别与分类.实验表明,提出的改进共空间模式与深度信念网络的分类准确率高于传统脑电信号的分类方法,在未来研究多种脑电信号的识别与分类上提供了一种研究思路.
Using electroencephalogram(EEG) signals to control intelligent wheelchairs is one of the new control methods for controlling intelligent wheelchairs. The most serious problem in the control process is recognition and classification of EEG signals, especially a variety of EEG signals. To improve the accuracy of EEG classification,improved-common spatial patterns and a deep-belief network algorithm are proposed and used for recognition and classification of EEG signals. A variety of different EEG signals were collected by an Emotiv EPOC+ EEG collector, and the characteristic signals were extracted by improved-common spatial patterns and identified and classified by a deep-belief network algorithm. Simulation results show that the classification accuracy of improvedcommon spatial patterns and the deep-belief network algorithm is higher than that of the traditional classification method, and a research perspective is provided for the classification of various EEG signals in the future.
Using electroencephalogram(EEG) signals to control intelligent wheelchairs is one of the new control methods for controlling intelligent wheelchairs. The most serious problem in the control process is recognition and classification of EEG signals, especially a variety of EEG signals. To improve the accuracy of EEG classification,improved-common spatial patterns and a deep-belief network algorithm are proposed and used for recognition and classification of EEG signals. A variety of different EEG signals were collected by an Emotiv EPOC+ EEG collector, and the characteristic signals were extracted by improved-common spatial patterns and identified and classified by a deep-belief network algorithm. Simulation results show that the classification accuracy of improvedcommon spatial patterns and the deep-belief network algorithm is higher than that of the traditional classification method, and a research perspective is provided for the classification of various EEG signals in the future.
引文
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2 Hoffmann U,Vesin J M,Ebrahimi T,et al.An efficient P300-based brain-computer interface for disabled subjects.J Neurosci Method,2008,167:115–125
3 Li X.Study and implementation of online motor imagery based brain-computer interface system.Dissertation for Master Degree.Beijing:Tsinghua University,2012[李翔.基于运动想象的在线脑–机接口系统的研究与实现.硕士学位论文.北京:清华大学,2012]
4 Wang S F,Lee Y H,Shiah Y J,et al.Time-frequency analysis of EEGs recorded during meditation.In:Proceedings of the 1st International Conference on Robot,Vision and Signal Processing(RVSP),Kaohsiung,2011.73–76
5 Wang K,Ye C,Shen Y Q,et al.Automatic removal algorithm of ocular artifact in electroencephalogram signal.Comput Eng,2011,37:257–260[王魁,叶闯,沈益青,等.脑电信号中眼电伪迹的自动去除算法.计算机工程,2011,37 :257–260]
6 Rajesh P N Rao.Brain-computer Interfacing an Introduction.Beijing:China Machine Press,2016[Rajesh P N Rao.脑机接口导论.北京:机械工业出版社,2016]
7 Gao S K.Comments on recent progress and challenges in the study of brain-computer interface.Chinese J Biomedical Eng,2007,26:801–803[高上凯.浅谈脑–机接口的发展现状与挑战.中国生物医学工程学报,2007,26:801–803]
8 Dornhege G.Toward Brain-Computer Interfacing.Cambridge:MIT Press,2007
9 Tang X L,Zhou J L,Zhang N,et al.Recognition of motor imagery EEG based on deep belief network.Inf Control,2015,44:717–721[唐贤伦,周家林,张娜,等.基于深度信念网络的运动想象脑电信号识别.信息与控制,2015,44:717 –721]
10 Ma M Z,Guo L B,Su K F.Classification method of motor imagery EEG signals based on wavelet and common spatial pattern.China Sci Technol Inf,2017,08:83–85[马满振,郭理彬,苏奎峰.基于小波包和共空间模式的运动想象脑电信号分类方法.中国科技信息,2017,08:83–85]
11 Li L T.Classification method of four-class motor imagery EEG data based on common spatial pattern.Instrumentation,2016,05:12–14[李立亭.基于共空间模式的四类运动想象脑电信号分类方法.仪器仪表用户,2016,05:12–14]
12 Jia X W,Li K,Li X Y,et al.A novel semi-supervised deep learning framework for affective state recognition on EEG signals.In:Proceedings of the 14th International Conference on Bioinformatics and Bioengineering,Boca Raton,2014
13 Li X Y,Jia X W,Xun G X,et al.Improving EEG feature learning via synchronized facial video.In:Proceedings of International Conference on Big Data,Santa Clara,2015.843–848
14 Wang J W.Preprocessing methods and applications based on EEG.Dissertation for Master Degree.Beijing:Beijing University of Posts and Telecommunications,2015[王佳威.脑电波信号的处理方法与应用.硕士学位论文.北京:北京邮电大学,2015]
15 Liang J K.EEG Analysis and BCI Research based on Motor Imagery under Driving Behavior.Beijing:National Defense Industry Press,2015[梁静坤.基于想象驾驶行为的脑机接口控制.北京:国防工业出版社,2015]
16 Zhang S H.Analysis of motor imagery EEG.Dissertation for Master Degree.Shanghai:Shanghai Jiao Tong University,2015[张昇欢.运动想象脑电信号的分析研究.硕士学位论文.上海:上海交通大学,2015]
17 Wang T.SMS classification methods based on deep learning.Dissertation for Master Degree.Xi’an:Chang’an University,2016[王婷.基于深度学习的短信分类技术研究.硕士学位论文.西安:长安大学,2016]
18 Yang D P.Product image classification based on deep learning.Dissertation for Master Degree.Dalian:Dalian Jiaotong University,2015[杨东坡.基于深度学习的商品图像分类.硕士学位论文.大连:大连交通大学,2015]
19 Dai R M.The motor imagery EEG classification based on deep learning.Dissertation for Master Degree.Beijing:Beijing Institute of Technology,2015[戴若梦.基于深度学习的运动想象脑电分类.硕士学位论文.北京:北京理工大学,2015]
20 Gao Y B,Lee H J,Mehmood R M,et al.Deep learning of EEG signals for emotion recognition.In:Proceedings of IEEE International Conference on Multimedia&Expo Workshops(ICMEW),Turin,2016
21 Liu J W,Cheng Y,Zhang W D,et al.Deep learning EEG response representation for brain computer interface.In:Proceedings of the 34th Chinese Control Conference(CCC),Hangzhou,2015.3518–3523