基于想象驾驶行为的脑电信号分析与脑机接口研究
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摘要
脑机接口技术在康复领域的应用是为那些有肢体残疾但大脑思维正常的人群提供一种辅助控制方式。本文对基于想象驾驶的脑电特征及控制接口系统进行了研究，目的是为有肢体障碍的人群提供一种辅助驾驶方式。为了模拟真实的驾驶环境，实验设计将外界动态的交通场景融入到研究中，同时从驾驶者自身感知包括视觉、听觉及视听多模态感知出发，设计了基于多模态感知的驾驶场景并对采集的相关脑电信号进行特征分析。实验中将受试者本身作为黑盒系统，受试者在交通信息的提示下利用左右手想象运动，实现对车辆或轮椅的启动和制动控制。
利用Neuroscan软件，本文对采集的三类感知下的脑电数据分别进行了预处理，并利用公共空间模式（common spatial patterns, CSP）算法进行了特征提取分析和比较。同时，将线性回归模型引入了特征信号的分类中，取得了较好的分类效果。
鉴于听觉感知的良好效果，利用SPEC061a单片机及外围系统，设计了基于语音识别技术的脑电驾驶模型，利用语音系统对车辆模型进行命令驱动，并将其作为基于受试者本身的反馈信息，以便及时对错误信息进行调整。
全文主要研究工作如下：
1、虚拟交通环境的设计
采用三维动画软件Maya技术，设计了虚拟的动态交通环境。结合现实交通场景，从视觉、听觉以及视听融合的交通信息出发，分别设计了基于不同感知的三种环境。其中视觉设计采用了红绿灯系统，听觉设计采用刹车鸣笛提示音频系统，而视听环境则采用二者环境的融合。虚拟交通环境的设计，即能够从实际驾驶环境出发，充分考虑人类感知器官对脑电的影响，又能够提高了脑电采集中受试者对实验的沉浸感。
2、脑电的预处理
脑电的采集和预处理采用美国Neuroscan公司的脑电采集系统，利用该软件进行了去眼电、肌电、工频干扰等预处理，并利用脑电地形图软件进行了定位分析，得出不同感知下的脑电在反应强度和持续时间上存在着不同，听觉感知下得到的响应脑电持续时间较长，超过1s，为本文中脑电驾驶的进一步研究奠定了基础。
3、脑电的特征提取
选用CSP算法，对预处理的数据分别从时域、频域进行了特征提取。结果表明：听觉感知下得到的脑电信息时域特征明显，在与左右手想象运动相关的电极C3、C4上，均表现出右手任务得到的脑电幅值高于左手任务，且最大幅值差达120μV，且脑电响应时间较长，这一点与脑电地形图分析结果类似。
4、分类处理
文中将线性回归算法引入到脑电的分类处理中，视觉和视听感知下的最高平均识别率达83.3%，而对于特征比较明显的听觉感知下的脑电数据来说，最高平均识别率达96.67%。从处理结果看，线性回归算法在对听觉感知下脑电数据分类中，取得了较好的效果。
5、基于语音识别的脑电驾驶模型的设计
根据三类脑电处理结果，针对听觉感知实验获得的良好效果，本文结合语音识别技术，利用SPCE061a单片机系统，设计了以语音作为反馈效果的脑机接口车辆控制模型，实现了利用想象脑电控制车辆前进和停止的目的，为残疾人车辆驾驶提供了实验数据。
BCI can provide an auxiliary control mode for the people with physical disabilities, butnormal brain thinking. In this paper, EEG features and control interface system based onmotor imagery under driving behavior are researched. In order to simulate a real driving scene,the experiment designed into the characteristics of the traffic scene and driver’s feelings Thisresearch involves the state of perception of visual, auditory and audio-visual multi-mode, inwhich the related EEG is collected and featured, while the subjects are looked as black boxsystems. In the tips of the traffic information, the subjects imagine the right or left handmovement to the driving behavior to start or brake the vehicle, then the EEG is collected andanalysed.
With Neuroscan software, three types of EEG are preprocessed, then the feature isextracted and classified using the CSP algorithm and the linear regression algorithm,respectively. The result is better.
Considering the good results of the auditory perception, a BCI system model for drivingis designed with SPEC061a and peripheral systems based on speech recognition technology.The speech system can produce voice command to drive the vehicle, and also can adjust thedriving behavior as a feedback-based on subjects in a timely manner.
The main work is as follows:
1. Design of virtual traffic environment
A virtual traffic environment is designed by Maya software. Combining of the realistictraffic scenarios, three environments are designed for simultaneous auditory, visual oraudio-visual. The visual design uses a traffic light system, while the auditory mode uses thebrake whistle and audio-visual environment with the integration of both. The design of virtualtraffic environment can give full consideration to the human sensory organs, and also can improve the immersion of subjects which proves a favorable effect on the experiment.
2. EEG pretreatment
Using Neuroscan, the driving EEG is collected and preprocessed, after which the EOG,EMG, and frequency interference are removed. The EEG graph shows the differences inreaction intensity and duration in the three perception mode, while the EEG of auditory modehas a longer duration more than1s, which lays the foundation for further study.
3. Feature extraction
Using CSP, the feature of pretreatment data is extraced from the time and frequencydomain. Results: EEG under auditory perception has an obvious feature from the time domain.The signals sampled from C3and C4all shows that the voltage amplitude caused by the rightimagery is higher than the others, the difference between the maximum amplitude is120μV;and the longer duration is similar to the result of EEG graph.
4. Classification of EEG
Using linear regression algorithm，the highest average recognition rate is83.3%underthe visual and audio-visual perception, but the rate under auditory perception is96.67%. Thelinear regression algorithm obtains a better result in classifying the data under the auditoryperceptive.
5. Design of speech recognition driving model
According to the good results obtained from the auditory perceptive, the controllingmodel with moive control and feedback of BCI vehicle is designed using SPCE061a systems.The system is able to achieve the control of vehicle to go forward or stop by motor imagery,which can provide the experimental data for the BCI vehicle.
利用Neuroscan软件，本文对采集的三类感知下的脑电数据分别进行了预处理，并利用公共空间模式（common spatial patterns, CSP）算法进行了特征提取分析和比较。同时，将线性回归模型引入了特征信号的分类中，取得了较好的分类效果。
鉴于听觉感知的良好效果，利用SPEC061a单片机及外围系统，设计了基于语音识别技术的脑电驾驶模型，利用语音系统对车辆模型进行命令驱动，并将其作为基于受试者本身的反馈信息，以便及时对错误信息进行调整。
全文主要研究工作如下：
1、虚拟交通环境的设计
采用三维动画软件Maya技术，设计了虚拟的动态交通环境。结合现实交通场景，从视觉、听觉以及视听融合的交通信息出发，分别设计了基于不同感知的三种环境。其中视觉设计采用了红绿灯系统，听觉设计采用刹车鸣笛提示音频系统，而视听环境则采用二者环境的融合。虚拟交通环境的设计，即能够从实际驾驶环境出发，充分考虑人类感知器官对脑电的影响，又能够提高了脑电采集中受试者对实验的沉浸感。
2、脑电的预处理
脑电的采集和预处理采用美国Neuroscan公司的脑电采集系统，利用该软件进行了去眼电、肌电、工频干扰等预处理，并利用脑电地形图软件进行了定位分析，得出不同感知下的脑电在反应强度和持续时间上存在着不同，听觉感知下得到的响应脑电持续时间较长，超过1s，为本文中脑电驾驶的进一步研究奠定了基础。
3、脑电的特征提取
选用CSP算法，对预处理的数据分别从时域、频域进行了特征提取。结果表明：听觉感知下得到的脑电信息时域特征明显，在与左右手想象运动相关的电极C3、C4上，均表现出右手任务得到的脑电幅值高于左手任务，且最大幅值差达120μV，且脑电响应时间较长，这一点与脑电地形图分析结果类似。
4、分类处理
文中将线性回归算法引入到脑电的分类处理中，视觉和视听感知下的最高平均识别率达83.3%，而对于特征比较明显的听觉感知下的脑电数据来说，最高平均识别率达96.67%。从处理结果看，线性回归算法在对听觉感知下脑电数据分类中，取得了较好的效果。
5、基于语音识别的脑电驾驶模型的设计
根据三类脑电处理结果，针对听觉感知实验获得的良好效果，本文结合语音识别技术，利用SPCE061a单片机系统，设计了以语音作为反馈效果的脑机接口车辆控制模型，实现了利用想象脑电控制车辆前进和停止的目的，为残疾人车辆驾驶提供了实验数据。
BCI can provide an auxiliary control mode for the people with physical disabilities, butnormal brain thinking. In this paper, EEG features and control interface system based onmotor imagery under driving behavior are researched. In order to simulate a real driving scene,the experiment designed into the characteristics of the traffic scene and driver’s feelings Thisresearch involves the state of perception of visual, auditory and audio-visual multi-mode, inwhich the related EEG is collected and featured, while the subjects are looked as black boxsystems. In the tips of the traffic information, the subjects imagine the right or left handmovement to the driving behavior to start or brake the vehicle, then the EEG is collected andanalysed.
With Neuroscan software, three types of EEG are preprocessed, then the feature isextracted and classified using the CSP algorithm and the linear regression algorithm,respectively. The result is better.
Considering the good results of the auditory perception, a BCI system model for drivingis designed with SPEC061a and peripheral systems based on speech recognition technology.The speech system can produce voice command to drive the vehicle, and also can adjust thedriving behavior as a feedback-based on subjects in a timely manner.
The main work is as follows:
1. Design of virtual traffic environment
A virtual traffic environment is designed by Maya software. Combining of the realistictraffic scenarios, three environments are designed for simultaneous auditory, visual oraudio-visual. The visual design uses a traffic light system, while the auditory mode uses thebrake whistle and audio-visual environment with the integration of both. The design of virtualtraffic environment can give full consideration to the human sensory organs, and also can improve the immersion of subjects which proves a favorable effect on the experiment.
2. EEG pretreatment
Using Neuroscan, the driving EEG is collected and preprocessed, after which the EOG,EMG, and frequency interference are removed. The EEG graph shows the differences inreaction intensity and duration in the three perception mode, while the EEG of auditory modehas a longer duration more than1s, which lays the foundation for further study.
3. Feature extraction
Using CSP, the feature of pretreatment data is extraced from the time and frequencydomain. Results: EEG under auditory perception has an obvious feature from the time domain.The signals sampled from C3and C4all shows that the voltage amplitude caused by the rightimagery is higher than the others, the difference between the maximum amplitude is120μV;and the longer duration is similar to the result of EEG graph.
4. Classification of EEG
Using linear regression algorithm，the highest average recognition rate is83.3%underthe visual and audio-visual perception, but the rate under auditory perception is96.67%. Thelinear regression algorithm obtains a better result in classifying the data under the auditoryperceptive.
5. Design of speech recognition driving model
According to the good results obtained from the auditory perceptive, the controllingmodel with moive control and feedback of BCI vehicle is designed using SPCE061a systems.The system is able to achieve the control of vehicle to go forward or stop by motor imagery,which can provide the experimental data for the BCI vehicle.
引文
[1]张世民，侯春林，顾玉东等．神经假体与脊髓损伤．中国临床康复，2003，7(8)：1300~1303
[2]武永生．人口老龄化的经济效应研究综述．西北人口，2011，32(5)：118~122
[3]王存波．人口老龄化多我国经济持续增长的影响研究：[硕士学位论文]．长春：吉林大学，2010
[4]程龙龙．BCICFES重建运动神经系统的信号处理与控制关键技术研究：[博士学位论文]．天津：天津大学，2010
[5] Wolpaw JR, Birbaumer N, Heetderks WJ, et al． Brain-Computer Interface Technology: A Review ofthe First International Meeting． IEEE Trans． on Neural Systems and Rehabilitation,2000,8(2):164~172
[6]Hans Berger． Hans Berger on the electroencephalogram of man: The fourteen original reports on thehuman electroencephalogram． New York: Elsevier Publishing Company,1969,28:1-150
[7]Wolpaw JR, Birbaumer N, McFarland D, et al． Brain-computer interfaces for communication andcontrol． Clinical Neurophysiology,2002,113(6):767~791
[8] Farwell LA, Donchin E． Talking off the top of your head: A mental prosthesis utilizing event-relatedbrain potentials． Electroencephalography Clinical Neurophysiology,1988,70(6):510~523
[9] Karmali F, Polak M． A Environmental Control by a Brain Computer Interface． John．D Enderle:22nd Annual International Conference of the IEEE Engineering in Medicine and BiologySociety． Chicago: IEEE,2000,4:2990~2992
[10] Johan W, Christopher R, Stambaugh, et al． Real-time prediction of hand trajectory by ensembles ofcortical neurons in primates． Nature,2000,408:361-365
[11] Theresa M, Vaughan, Jonathan R, et al． EEG-based communication: prospects and problems． IEEETransactions on Rehabilitation Engineering,1996,4(4):425~430
[12] Vaughan TM, Heetderks WJ, Trejo LJ, et al． Brain–Computer Interface Technology: A Review of theSecond International Meeting． Trans Rehabil Eng．,2003,11(2):94~109
[13] Kubler A, Mushahwar VK, Hochberg LR, et al． BCI Meeting2005—Workshop on Clinical Issuesand Applications． Trans Rehabil Eng．,2006,14(2):131~134
[14] Pfurtscheller G, Neuper C, Guger C, et al． Current trends in Graz brain–computer interfaceresearch． IEEE Trans Rehabil Eng．,2000,8(2):216-219
[15]世界首个仿生人:思想开电视玩电脑．http://news．sohu．com/20060714/n244249889．shtml
[16] http://forum．home．news．cn/thread/71349029/1．html
[17] http://roll．sohu．com/20110623/n311349513．shtml
[18]清华大学．基于听觉认知神经信号的自主意愿表达方法．中国， G06F3/01，CN101464729．2009-06-24
[19] http://news.ename.cn/article-12301-1.html
[20]上海交通大学．基于眼电信号的警觉度检测系统．中国， A61B5/0496，CN102125429A．2011-07-20
[21]李伟，何其昌，范秀敏．基于汽车操纵信号的驾驶员疲劳状态检测．上海交通大学学报，2010，44(2)：292~296
[22]研究发现：脑电图可帮助植物人恢复． http://qixie．qqyy．com/article/1111/22/3020．html
[23] Wolpaw JR, Jonathan R, Birbaumer N, et al． Brain–computer interfaces for communication andcontrol． ClinicalNeurophysiology,2002,113(6):767-791
[24] Sinkjaer T, Haugland M, Inmann A． Biopotentials as command and feedback signals in functionalelectrical stimulation systems． Medical Engineering and Physics,2003,25(1):29-40
[25] Dobelle WH． Artificial Vision for the Blind by Connecting a Television Camera to the VisualCortex． Artificial Internal Organs,2000,46(1)：3~9
[26] Constans A． Mind over machines． The Scientist,2005,19(3):27~31
[27]孙久荣，戴振东．仿生学的现状和未来．生物物理学报，2007，23(2)：109~114
[28] Ingrid W． Tapping the Mind． Science,2003,299(5606):496~499
[29] Wu LW, Liao HC, Hu S, et al． Brain-controlled robot agent: an EEG-based a Robotagent． Industrial Robot,2008,35:507~519
[30] Millan JJ, Galan F, Vanhooydonck D, et al． Asynchronous non-invasive brain-actuated control of anintelligent wheelchair． Conf Proc IEEE Eng Med Biol Soc．, Minneapolis． MN．2009,2009:3361~3364．
[31] Bell CJ, Shenoy P, Chalodhorn R, et al． Control of a humanoid robot by a noninvasivebrain-computer interface in humans． Neural Eng．,2008,5:214~220
[32]贾文川．四足机器人脑机协作导航与规划：[博士学位论文]．武汉：华中科技大学，2011
[33]伍亚舟，吴宝明，何庆华．基于脑电的脑$机接口系统研究现状．中国临床康复，2006，10(1)：147~150
[34]杨立才，李佰敏，李光林．脑-机接口技术综述．电子学报，2005，33(7)：1234~1239
[35] Vaughan TM． EEG-based communication: prospects and problems． IEEE Trans Rehabil Eng,1996,4(4):425~430
[36]高上凯．神经工程与脑－机接口．生命科学，2009，21(2):177~180
[37]赵均榜．张智君基于皮层诱发电位的脑机接口研究进展．航天医学与医学工程，2010，23(1)：74~78
[38] Vidal JJ． Real-time detection of brain events in EEG．IEEE Proc．1977,65(5):633~634
[39] Elbert T, Rockstroh B, Lutzenberger W, et al． Biofeedback of slow corticalpotentials． Electroencephalogr．Clin．Neurophysiol．,1980,48(3):293~301
[40] Sutter EE． The brain response interface: Communication through visually-induced electrical brainresponses． Journal of Microcomputer Applications,1992,15(1):31~45
[41] BIOMERIX CORP． Self-Expandable Endovascular Device for Aneurysm Occlusion． US,A61B17/08, US2009318941．2009-12-24
[42] UNIV WASHINGTON． All-optical modulation and sdwitching with patterned optically absorbingpolymers． US, G02B6/12, US2009297094．2009-12-3
[43]何庆华，彭承琳，吴宝明．脑机接口技术研究方法．重庆大学学报，2002，25(12):106~109
[44]何庆华．基于视觉诱发电位的脑机接口实验研究：[博士学位论文]．重庆：重庆大学，2003
[45]程明，高上凯，张琳．基于脑电信号的脑－计算机接口．北京生物医学工程，2002，19(2):113~118
[46] Farwell LA, Donchin E． Talking off the top of your head: towards a mental prosthesis utilizingevent-related brain potentials． Electroencephalography and Clinical Neurophysiology,1988,70(6):510~523
[47] Emanuel Donchin, Kevin M． Spencer, Ranjith Wijesinghe． The mental prosthesis:Assessing thespeed of a P300-based brain–computer interface． IEEE TRANSACTIONS ONREHABILITATION ENGINEERING,2000,8(2):174~179
[48]天津大学．一种Speller BCI系统及其控制方法．中国，G06F3/01，CN102200833A．2011-09-28
[49]天津大学．一种脑机接口系统及其控制方法．中国，G06F3/01，CN102184018A．2011-09-14
[50]天津大学．基于隐性注意的视听联合刺激脑-机接口方法．中国，G06F3/01，CN102184019A．2011-09-14
[51]杨红宇，徐鹏，陈彦．异步脑机接口技术现状及发展趋势．中国生物医学工程学报，2011，30(5):774~779
[52]邓志东，李修全，郑宽浩等．一种基于SSVEP的仿人机器人异步脑机接口控制系统机器人，2011，30(2):129~135
[53] Cilliers PJ, Van Der Kouwe, A．J．W．．A VEP-based Computer Interface for C2-quadriplegics．IEEEConference on Electronic Devices for the Disabled,1993,159:1263-1264
[54] Calhoun GL, McMillan GR, EEG-based control for human-computerinteraction．Proc．Annu．symp．Human Interaction with Complex Systems．1996,4~9
[55] Middendorf M, McMillan G, Calhoun G, et al．Brain-computer interfaces based on the steady-statevisual-evoked response．IEEE Trans．Rehab．Eng．,2000,8(2):211~214
[56]中国人民解放军第三军医大学野战外科研究所．脑机接口短消息发送控制装置．中国，A61B5/0484， CN202010156U．2011-10-19
[57]中国人民解放军第三军医大学野战外科研究所．脑机接口短消息发送控制装置及发送控制方法．中国，H04W4/14，CN102098639A．2011-06-15
[58] Cheng Ming, Gao Shangkai． An EEG-based cursor control system． Proceedings of the first jointBMES/EMBS conference,1999,1:669
[59] Yijun Wang, Ruiping Wang, Xiaorong Gao, et al． A Practical VEP-Based Brain–ComputerInterface． IEEE Transactions On Neural Systems Rehabilitation Engineering,2006,14(2):234~240
[60] Kelly SP, Lalor E, Finucane C, et al．Visual spatial attention tracking using high-density SSVEP datafor independent brain-computer communication． IEEE Transactions of Neural Systems andRehabilitation Engineering,2005,13(2)：172~178
[61]李葆明，梅镇彤．在延缓分析作业的学习过程中猕猴大脑皮层慢电位的变化．生理学报，1990，42(1):9-17
[62] Donchin, While a monkey waits: Electro-cortical events recorded during the fore-period of a reactiontime study．Electroencephalography and Clinical Neurophysiology,1971,31(2):115~127
[63] Rockstroh B, Slow cortical potentials and behavior, MD: Urban&Schwarzenberg,1989,1~267
[64] Birbaumer N, Hinterberger T, Kubler A, Neumann N, et al． The thought-translation device (TTD):neurobehavioral mechanisms and clinical outcome．Neural Systems and Rehabilitation Engineering,2003,11(2):120~123
[65]赵海滨，王宏，王旭．基于Matlab/Simulink的脑-机接口系统设计，2009,21(9)：2770~2775
[66]吴婷．自发脑电脑机接口模式识别关键技术与实验研究：[博士学位论文]．上海：上海交通大学，2008
[67] Pfurtscheller G, Lopes FH． Event-related EEG/MEG synchronization and desynchronization: basicprinciples．Clin Neurophysiol,1999,110:1842~1857
[68] Kozelka JW, Pedley TA． Beta and mu rhythms．J．Clin． Neurophysiol,1990,7(2):191~207
[69]任亚莉．基于脑电的脑-机接口系统．中国组织工程研究与临床康复，2011，15(4):749~751
[70] Keirn ZA, Aunon JI．Man-Machine Communications through Brain-Wave Processing． IEEEEng．Med．Biol．Mag．,1990,9(1):55-7
[71] Keirn ZA, Aunon JI．A new mode of communication between man and his surroundings．IEEETrans．Biomed Eng,1990,37(12):1209~1214
[72]陈香，杨基海，李强．基于节律同化效应的思维脑电信号分类研究．中国生物医学工程学报，2006，25(4):430~437
[73] Anderson CW, Stolz EA, Shamsunder S．Multivariate autoregressive models for classification ofspontaneous electroencephalographic signals during mental tasks． IEEE Transactions onBiomedical Engineering,1998,45(3)：277~286
[74]潘洁，高小榕，高上凯．稳态视觉诱发电位频率与相位特性的脑电研究．清华大学学报，2011，50(2):250~254
[75]李世俊，高小榕，王懿．事件相关电位活动中脑电信号的连通性．清华大学学报，2010，50(12):1939~1943
[76]清华大学．基于听觉认知神经信号的自主意愿表达方法．中国， G06F3/01，CN101464729．2009-06-24
[77]张翠，驾驶员自身因素引起的驾驶疲劳对交通安全的影响．道路交通与安全，2010，10(3):30~33
[78]王振华，贾银山，陈兴．基于SVM的驾驶员疲劳检测研究．科学技术与工程，2011，11(8):1828~1832
[79]欧居尚．基于脑电波分析技术的安全驾驶实验研究．西南交通大学学报，2011，46(4):695~700
[80] Jap B． Using EEG spectral components to assess algo-rithms for detecting fatigue． Expert Systemswith Applications,2009,36(2):2352-2359
[81]毛科俊，赵晓华，房瑞雪．驾驶疲劳声音对策有效性的驾驶模拟．吉林大学学报，2010，40(6):1533~1537
[82]钟铭恩，吴平东，彭军强．基于脑电信号的驾驶员情绪状态识别研究．中国安全科学学报，2011，21(9):64~69
[83] Megías Alberto, Maldonado Antonio, Cndido Antonio, et al．Emotional modulation of urgent andevaluative behaviors in risky driving scenarios．Accident Analysis and Prevention,2011,43(3):813~817
[84] Eriksson M, Papanikolopoulos NPEye-tracking for detection of driver’s fatigue．Pro-ceeding of the InIEEE Conference on Intelligent Transportation Systems，1997:314~319
[85]马贇，王毅军，高小榕等．基于脑-机接口技术的虚拟现实康复训练平台．中国生物医学工程学报，2007，26(3):373~378
[86]梁静坤，徐桂芝，孙辉．视、听感知下模拟驾驶行为的脑电实验比较研究．国际生物医学工程杂志，2011，34(4):209~211
[87]伍亚舟．基于想象左右手运动思维脑电BCI实验及分类识别研究：[博士学位论文]．重庆：第三军医大学，2007
[88]王玉，周卫东，李淑芳等．脑电信号的分形截距特征分析及在癫痫检测中的应用．中国生物医学工程学报，2011，30(4):562~566
[89]蔡冬梅，周卫东，刘凯，等．基于Hurst指数和SVM的脑电癫痫检测方法．中国生物医学工程学报，2010，29(6):836~840
[90]王磊．基于运动想象的脑电信号分类与脑机接口技术研究：[博士学位论文]．天津：河北工业大学，2008
[91]孙辉．基于视听觉刺激下的左右手运动想象的脑机接口研究．[硕士学位论文]．天津：河北工业大学，2010
[92]姚志彬．临床神经解剖学．广东：世界图书出版公司．2001．1~330
[93]周鹏．基于运动想象的脑机接口的研究：[博士学位论文]．天津：天津大学，2007
[94] Lichtman J, Lehrer J．Neuroscience: Making connections．Nature,2009,457:524~527
[95]梁夏，王金辉，贺永．人脑连接组研究:脑结构网络和脑功能网络．科学通报，2010，55(16):1565~1583
[96] Lichtman JW, Livet J, Sanes JR. A technicolour approach to the connectome. Nat Rev Neurosci,2008,9:417~422
[97] Suffczynski P, Kalitzin S, Pfurtscheller G, et al．Computational modal of thalomo-cortical network:dynamical control of alpha rhythms in relation to focal attention. Psychophysiology,2001,43:25~40
[98] Restuccia D, DellaMarca G, Marra C, et al. Attentional load of the primary task influences the frontalbut not the temporal generators of mismatch negativity. Cognitive Brain Research,2005,25:891~899
[99] Lin Z，Zhang C，Wu W, et al. Frequeney Recognition Based on Canonieal Correlation Analysis forSSVEP-Based BCIs. IEEE Transaetions on biomedical engineering,2007,54(6):1172~1176
[100] Benjamin Blankertz, Ryota Tomioka, Steven Lemm, et al．Optimizing Spatial Filters for Robust EEGSingle-Trial Analysis. IEEE SIGNAL PROCESSING MAGAZINE,2008,25(1):41~56
[101] Lee H, Choi S. PCA-based linear dynamical systems for multichannel EEG classification.Proceedings of the9th International Conference on Neural Information Processing,2002,2:745~749
[102] Graimann B, Huggins J E, Levine S P. Toward a direct brain interface based on human subduralrecordings and wavelet-packet analysis. IEEE Trans on Biomedical Engineering,2004,51(6):954-962
[103]毛大伟．分尺度复杂性及希尔伯特-黄变换在脑电分析中的应用．[博士学位论文]．杭州:浙江大学,2005
[104]李明爱，刘净瑜.基于改进CSP算法的运动想象脑电信号识别方法.中国生物医学工程学报，2009，28(2):161~165
[105]刘冲，赵海滨等.基于CSP与SVM算法的运动想象脑电信号分类.东北大学学报(自然科学版)，2010，31(8):1098~101
[106] Liao X, Yao DZ, Wu D. Combining spatial filters for the classification of single-trial EEG in a fingermovement task. IEEE Transactions on Biomedical Engineering,2007,54(5):821~831
[107]刘美春．基于运动想象的脑-机接口系统模式识别算法研究：[博士学位论文]．广州：华南理工大学，2009
[108]李明钊.基于EEG的BCI系统脑电信号分类方法研究：[硕士学位论文].天津：天津大学，2011
[109] Wang F, Zhang CS. Label Propagation through linear neighborhoods. IEEE Transactions onKnowledge and Data Engineering,2008,20:55~67
[110] Champency DC. A handbook of Fourier theorems．Cambridge university press,1987,18(1)：1~200
[111]华继钊，王建国，杨静宇.基于PCA的边缘检测方法.中国图像图形学报,2009，5:913~915
[112]梁静坤,徐桂芝,李明钊等.基于听觉感知任务下的脑电驾驶实验研究.中国生物医学工程学报,2012,31(2):131~135
[113]单斌,李芳.基于LDA话题演化研究方法综述.中文信息学报,2010，24(6):43~49
[114] Roweis ST, Saul LK. Nonlinear dimensionality reduction by locally linear embedding. Science,2000,290:323~2326
[115] Maryam Mohebbi, Hassan Ghassemian． Detection of Atrial Fibrillation Episodes UsingSVM．Proc．of the30th Annual International Conference of the IEEE Engineering in Medicine andBiology Society,Vancouver,Canada，2008,177~180
[116]彭博．基于Hilbert-huang变换和支持向量机的生物电信号的分析研究：[硕士学位论文]．杭州：浙江大学,2006
[117] Imran Naseem, Roberto Togneri, Mohammed Bennamoun. Linear Regression for Face Recognition.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2010,32(11):2106~2112
[118] M．Revan Ozkale．A jackknife ridge estimator in the linear regression model with heteroscedastic orcorrelated errors．Statistics and Probability Letters,2008,78:3159～3169
[119] Firinguetti L．A simulation study of ridge regression estimators with autocorrelated errors．CommStatist Simulation,1989,18(2):673～702
[120] Naseem I, Togneri R, Bennamoun M. Linear regression for face recognition. IEEE Transactions onPattern Analysis and Machine Intelligence,2010,32(11):2106-2112
[121]赵启斌，张丽清，CICHOCKI Andrzej.三维虚拟现实环境中基于EEG的异步BCI小车导航系统.科学通报:2888~2895
[122]李守轩，张华，刘继忠等.基于SPCE061A的智能轮椅语音控制系统.计算机工程，2006，34(14):248~250
[123]张国臣．基于ARM的脑电测量电路的研究：[硕士学位论文]．秦皇岛：燕山大学，2011
[124]徐宝国，宋爱国，杨仁桓．基于运动想象脑电的在线脑机接口实验．华中科技大学学报（自然科学版），2011，39（4）：60~64
[125]宋阳．基于单片机的语言识别系统软件设计与开发：[硕士学位论文]．成都：电子科技大学，2011
[2]武永生．人口老龄化的经济效应研究综述．西北人口，2011，32(5)：118~122
[3]王存波．人口老龄化多我国经济持续增长的影响研究：[硕士学位论文]．长春：吉林大学，2010
[4]程龙龙．BCICFES重建运动神经系统的信号处理与控制关键技术研究：[博士学位论文]．天津：天津大学，2010
[5] Wolpaw JR, Birbaumer N, Heetderks WJ, et al． Brain-Computer Interface Technology: A Review ofthe First International Meeting． IEEE Trans． on Neural Systems and Rehabilitation,2000,8(2):164~172
[6]Hans Berger． Hans Berger on the electroencephalogram of man: The fourteen original reports on thehuman electroencephalogram． New York: Elsevier Publishing Company,1969,28:1-150
[7]Wolpaw JR, Birbaumer N, McFarland D, et al． Brain-computer interfaces for communication andcontrol． Clinical Neurophysiology,2002,113(6):767~791
[8] Farwell LA, Donchin E． Talking off the top of your head: A mental prosthesis utilizing event-relatedbrain potentials． Electroencephalography Clinical Neurophysiology,1988,70(6):510~523
[9] Karmali F, Polak M． A Environmental Control by a Brain Computer Interface． John．D Enderle:22nd Annual International Conference of the IEEE Engineering in Medicine and BiologySociety． Chicago: IEEE,2000,4:2990~2992
[10] Johan W, Christopher R, Stambaugh, et al． Real-time prediction of hand trajectory by ensembles ofcortical neurons in primates． Nature,2000,408:361-365
[11] Theresa M, Vaughan, Jonathan R, et al． EEG-based communication: prospects and problems． IEEETransactions on Rehabilitation Engineering,1996,4(4):425~430
[12] Vaughan TM, Heetderks WJ, Trejo LJ, et al． Brain–Computer Interface Technology: A Review of theSecond International Meeting． Trans Rehabil Eng．,2003,11(2):94~109
[13] Kubler A, Mushahwar VK, Hochberg LR, et al． BCI Meeting2005—Workshop on Clinical Issuesand Applications． Trans Rehabil Eng．,2006,14(2):131~134
[14] Pfurtscheller G, Neuper C, Guger C, et al． Current trends in Graz brain–computer interfaceresearch． IEEE Trans Rehabil Eng．,2000,8(2):216-219
[15]世界首个仿生人:思想开电视玩电脑．http://news．sohu．com/20060714/n244249889．shtml
[16] http://forum．home．news．cn/thread/71349029/1．html
[17] http://roll．sohu．com/20110623/n311349513．shtml
[18]清华大学．基于听觉认知神经信号的自主意愿表达方法．中国， G06F3/01，CN101464729．2009-06-24
[19] http://news.ename.cn/article-12301-1.html
[20]上海交通大学．基于眼电信号的警觉度检测系统．中国， A61B5/0496，CN102125429A．2011-07-20
[21]李伟，何其昌，范秀敏．基于汽车操纵信号的驾驶员疲劳状态检测．上海交通大学学报，2010，44(2)：292~296
[22]研究发现：脑电图可帮助植物人恢复． http://qixie．qqyy．com/article/1111/22/3020．html
[23] Wolpaw JR, Jonathan R, Birbaumer N, et al． Brain–computer interfaces for communication andcontrol． ClinicalNeurophysiology,2002,113(6):767-791
[24] Sinkjaer T, Haugland M, Inmann A． Biopotentials as command and feedback signals in functionalelectrical stimulation systems． Medical Engineering and Physics,2003,25(1):29-40
[25] Dobelle WH． Artificial Vision for the Blind by Connecting a Television Camera to the VisualCortex． Artificial Internal Organs,2000,46(1)：3~9
[26] Constans A． Mind over machines． The Scientist,2005,19(3):27~31
[27]孙久荣，戴振东．仿生学的现状和未来．生物物理学报，2007，23(2)：109~114
[28] Ingrid W． Tapping the Mind． Science,2003,299(5606):496~499
[29] Wu LW, Liao HC, Hu S, et al． Brain-controlled robot agent: an EEG-based a Robotagent． Industrial Robot,2008,35:507~519
[30] Millan JJ, Galan F, Vanhooydonck D, et al． Asynchronous non-invasive brain-actuated control of anintelligent wheelchair． Conf Proc IEEE Eng Med Biol Soc．, Minneapolis． MN．2009,2009:3361~3364．
[31] Bell CJ, Shenoy P, Chalodhorn R, et al． Control of a humanoid robot by a noninvasivebrain-computer interface in humans． Neural Eng．,2008,5:214~220
[32]贾文川．四足机器人脑机协作导航与规划：[博士学位论文]．武汉：华中科技大学，2011
[33]伍亚舟，吴宝明，何庆华．基于脑电的脑$机接口系统研究现状．中国临床康复，2006，10(1)：147~150
[34]杨立才，李佰敏，李光林．脑-机接口技术综述．电子学报，2005，33(7)：1234~1239
[35] Vaughan TM． EEG-based communication: prospects and problems． IEEE Trans Rehabil Eng,1996,4(4):425~430
[36]高上凯．神经工程与脑－机接口．生命科学，2009，21(2):177~180
[37]赵均榜．张智君基于皮层诱发电位的脑机接口研究进展．航天医学与医学工程，2010，23(1)：74~78
[38] Vidal JJ． Real-time detection of brain events in EEG．IEEE Proc．1977,65(5):633~634
[39] Elbert T, Rockstroh B, Lutzenberger W, et al． Biofeedback of slow corticalpotentials． Electroencephalogr．Clin．Neurophysiol．,1980,48(3):293~301
[40] Sutter EE． The brain response interface: Communication through visually-induced electrical brainresponses． Journal of Microcomputer Applications,1992,15(1):31~45
[41] BIOMERIX CORP． Self-Expandable Endovascular Device for Aneurysm Occlusion． US,A61B17/08, US2009318941．2009-12-24
[42] UNIV WASHINGTON． All-optical modulation and sdwitching with patterned optically absorbingpolymers． US, G02B6/12, US2009297094．2009-12-3
[43]何庆华，彭承琳，吴宝明．脑机接口技术研究方法．重庆大学学报，2002，25(12):106~109
[44]何庆华．基于视觉诱发电位的脑机接口实验研究：[博士学位论文]．重庆：重庆大学，2003
[45]程明，高上凯，张琳．基于脑电信号的脑－计算机接口．北京生物医学工程，2002，19(2):113~118
[46] Farwell LA, Donchin E． Talking off the top of your head: towards a mental prosthesis utilizingevent-related brain potentials． Electroencephalography and Clinical Neurophysiology,1988,70(6):510~523
[47] Emanuel Donchin, Kevin M． Spencer, Ranjith Wijesinghe． The mental prosthesis:Assessing thespeed of a P300-based brain–computer interface． IEEE TRANSACTIONS ONREHABILITATION ENGINEERING,2000,8(2):174~179
[48]天津大学．一种Speller BCI系统及其控制方法．中国，G06F3/01，CN102200833A．2011-09-28
[49]天津大学．一种脑机接口系统及其控制方法．中国，G06F3/01，CN102184018A．2011-09-14
[50]天津大学．基于隐性注意的视听联合刺激脑-机接口方法．中国，G06F3/01，CN102184019A．2011-09-14
[51]杨红宇，徐鹏，陈彦．异步脑机接口技术现状及发展趋势．中国生物医学工程学报，2011，30(5):774~779
[52]邓志东，李修全，郑宽浩等．一种基于SSVEP的仿人机器人异步脑机接口控制系统机器人，2011，30(2):129~135
[53] Cilliers PJ, Van Der Kouwe, A．J．W．．A VEP-based Computer Interface for C2-quadriplegics．IEEEConference on Electronic Devices for the Disabled,1993,159:1263-1264
[54] Calhoun GL, McMillan GR, EEG-based control for human-computerinteraction．Proc．Annu．symp．Human Interaction with Complex Systems．1996,4~9
[55] Middendorf M, McMillan G, Calhoun G, et al．Brain-computer interfaces based on the steady-statevisual-evoked response．IEEE Trans．Rehab．Eng．,2000,8(2):211~214
[56]中国人民解放军第三军医大学野战外科研究所．脑机接口短消息发送控制装置．中国，A61B5/0484， CN202010156U．2011-10-19
[57]中国人民解放军第三军医大学野战外科研究所．脑机接口短消息发送控制装置及发送控制方法．中国，H04W4/14，CN102098639A．2011-06-15
[58] Cheng Ming, Gao Shangkai． An EEG-based cursor control system． Proceedings of the first jointBMES/EMBS conference,1999,1:669
[59] Yijun Wang, Ruiping Wang, Xiaorong Gao, et al． A Practical VEP-Based Brain–ComputerInterface． IEEE Transactions On Neural Systems Rehabilitation Engineering,2006,14(2):234~240
[60] Kelly SP, Lalor E, Finucane C, et al．Visual spatial attention tracking using high-density SSVEP datafor independent brain-computer communication． IEEE Transactions of Neural Systems andRehabilitation Engineering,2005,13(2)：172~178
[61]李葆明，梅镇彤．在延缓分析作业的学习过程中猕猴大脑皮层慢电位的变化．生理学报，1990，42(1):9-17
[62] Donchin, While a monkey waits: Electro-cortical events recorded during the fore-period of a reactiontime study．Electroencephalography and Clinical Neurophysiology,1971,31(2):115~127
[63] Rockstroh B, Slow cortical potentials and behavior, MD: Urban&Schwarzenberg,1989,1~267
[64] Birbaumer N, Hinterberger T, Kubler A, Neumann N, et al． The thought-translation device (TTD):neurobehavioral mechanisms and clinical outcome．Neural Systems and Rehabilitation Engineering,2003,11(2):120~123
[65]赵海滨，王宏，王旭．基于Matlab/Simulink的脑-机接口系统设计，2009,21(9)：2770~2775
[66]吴婷．自发脑电脑机接口模式识别关键技术与实验研究：[博士学位论文]．上海：上海交通大学，2008
[67] Pfurtscheller G, Lopes FH． Event-related EEG/MEG synchronization and desynchronization: basicprinciples．Clin Neurophysiol,1999,110:1842~1857
[68] Kozelka JW, Pedley TA． Beta and mu rhythms．J．Clin． Neurophysiol,1990,7(2):191~207
[69]任亚莉．基于脑电的脑-机接口系统．中国组织工程研究与临床康复，2011，15(4):749~751
[70] Keirn ZA, Aunon JI．Man-Machine Communications through Brain-Wave Processing． IEEEEng．Med．Biol．Mag．,1990,9(1):55-7
[71] Keirn ZA, Aunon JI．A new mode of communication between man and his surroundings．IEEETrans．Biomed Eng,1990,37(12):1209~1214
[72]陈香，杨基海，李强．基于节律同化效应的思维脑电信号分类研究．中国生物医学工程学报，2006，25(4):430~437
[73] Anderson CW, Stolz EA, Shamsunder S．Multivariate autoregressive models for classification ofspontaneous electroencephalographic signals during mental tasks． IEEE Transactions onBiomedical Engineering,1998,45(3)：277~286
[74]潘洁，高小榕，高上凯．稳态视觉诱发电位频率与相位特性的脑电研究．清华大学学报，2011，50(2):250~254
[75]李世俊，高小榕，王懿．事件相关电位活动中脑电信号的连通性．清华大学学报，2010，50(12):1939~1943
[76]清华大学．基于听觉认知神经信号的自主意愿表达方法．中国， G06F3/01，CN101464729．2009-06-24
[77]张翠，驾驶员自身因素引起的驾驶疲劳对交通安全的影响．道路交通与安全，2010，10(3):30~33
[78]王振华，贾银山，陈兴．基于SVM的驾驶员疲劳检测研究．科学技术与工程，2011，11(8):1828~1832
[79]欧居尚．基于脑电波分析技术的安全驾驶实验研究．西南交通大学学报，2011，46(4):695~700
[80] Jap B． Using EEG spectral components to assess algo-rithms for detecting fatigue． Expert Systemswith Applications,2009,36(2):2352-2359
[81]毛科俊，赵晓华，房瑞雪．驾驶疲劳声音对策有效性的驾驶模拟．吉林大学学报，2010，40(6):1533~1537
[82]钟铭恩，吴平东，彭军强．基于脑电信号的驾驶员情绪状态识别研究．中国安全科学学报，2011，21(9):64~69
[83] Megías Alberto, Maldonado Antonio, Cndido Antonio, et al．Emotional modulation of urgent andevaluative behaviors in risky driving scenarios．Accident Analysis and Prevention,2011,43(3):813~817
[84] Eriksson M, Papanikolopoulos NPEye-tracking for detection of driver’s fatigue．Pro-ceeding of the InIEEE Conference on Intelligent Transportation Systems，1997:314~319
[85]马贇，王毅军，高小榕等．基于脑-机接口技术的虚拟现实康复训练平台．中国生物医学工程学报，2007，26(3):373~378
[86]梁静坤，徐桂芝，孙辉．视、听感知下模拟驾驶行为的脑电实验比较研究．国际生物医学工程杂志，2011，34(4):209~211
[87]伍亚舟．基于想象左右手运动思维脑电BCI实验及分类识别研究：[博士学位论文]．重庆：第三军医大学，2007
[88]王玉，周卫东，李淑芳等．脑电信号的分形截距特征分析及在癫痫检测中的应用．中国生物医学工程学报，2011，30(4):562~566
[89]蔡冬梅，周卫东，刘凯，等．基于Hurst指数和SVM的脑电癫痫检测方法．中国生物医学工程学报，2010，29(6):836~840
[90]王磊．基于运动想象的脑电信号分类与脑机接口技术研究：[博士学位论文]．天津：河北工业大学，2008
[91]孙辉．基于视听觉刺激下的左右手运动想象的脑机接口研究．[硕士学位论文]．天津：河北工业大学，2010
[92]姚志彬．临床神经解剖学．广东：世界图书出版公司．2001．1~330
[93]周鹏．基于运动想象的脑机接口的研究：[博士学位论文]．天津：天津大学，2007
[94] Lichtman J, Lehrer J．Neuroscience: Making connections．Nature,2009,457:524~527
[95]梁夏，王金辉，贺永．人脑连接组研究:脑结构网络和脑功能网络．科学通报，2010，55(16):1565~1583
[96] Lichtman JW, Livet J, Sanes JR. A technicolour approach to the connectome. Nat Rev Neurosci,2008,9:417~422
[97] Suffczynski P, Kalitzin S, Pfurtscheller G, et al．Computational modal of thalomo-cortical network:dynamical control of alpha rhythms in relation to focal attention. Psychophysiology,2001,43:25~40
[98] Restuccia D, DellaMarca G, Marra C, et al. Attentional load of the primary task influences the frontalbut not the temporal generators of mismatch negativity. Cognitive Brain Research,2005,25:891~899
[99] Lin Z，Zhang C，Wu W, et al. Frequeney Recognition Based on Canonieal Correlation Analysis forSSVEP-Based BCIs. IEEE Transaetions on biomedical engineering,2007,54(6):1172~1176
[100] Benjamin Blankertz, Ryota Tomioka, Steven Lemm, et al．Optimizing Spatial Filters for Robust EEGSingle-Trial Analysis. IEEE SIGNAL PROCESSING MAGAZINE,2008,25(1):41~56
[101] Lee H, Choi S. PCA-based linear dynamical systems for multichannel EEG classification.Proceedings of the9th International Conference on Neural Information Processing,2002,2:745~749
[102] Graimann B, Huggins J E, Levine S P. Toward a direct brain interface based on human subduralrecordings and wavelet-packet analysis. IEEE Trans on Biomedical Engineering,2004,51(6):954-962
[103]毛大伟．分尺度复杂性及希尔伯特-黄变换在脑电分析中的应用．[博士学位论文]．杭州:浙江大学,2005
[104]李明爱，刘净瑜.基于改进CSP算法的运动想象脑电信号识别方法.中国生物医学工程学报，2009，28(2):161~165
[105]刘冲，赵海滨等.基于CSP与SVM算法的运动想象脑电信号分类.东北大学学报(自然科学版)，2010，31(8):1098~101
[106] Liao X, Yao DZ, Wu D. Combining spatial filters for the classification of single-trial EEG in a fingermovement task. IEEE Transactions on Biomedical Engineering,2007,54(5):821~831
[107]刘美春．基于运动想象的脑-机接口系统模式识别算法研究：[博士学位论文]．广州：华南理工大学，2009
[108]李明钊.基于EEG的BCI系统脑电信号分类方法研究：[硕士学位论文].天津：天津大学，2011
[109] Wang F, Zhang CS. Label Propagation through linear neighborhoods. IEEE Transactions onKnowledge and Data Engineering,2008,20:55~67
[110] Champency DC. A handbook of Fourier theorems．Cambridge university press,1987,18(1)：1~200
[111]华继钊，王建国，杨静宇.基于PCA的边缘检测方法.中国图像图形学报,2009，5:913~915
[112]梁静坤,徐桂芝,李明钊等.基于听觉感知任务下的脑电驾驶实验研究.中国生物医学工程学报,2012,31(2):131~135
[113]单斌,李芳.基于LDA话题演化研究方法综述.中文信息学报,2010，24(6):43~49
[114] Roweis ST, Saul LK. Nonlinear dimensionality reduction by locally linear embedding. Science,2000,290:323~2326
[115] Maryam Mohebbi, Hassan Ghassemian． Detection of Atrial Fibrillation Episodes UsingSVM．Proc．of the30th Annual International Conference of the IEEE Engineering in Medicine andBiology Society,Vancouver,Canada，2008,177~180
[116]彭博．基于Hilbert-huang变换和支持向量机的生物电信号的分析研究：[硕士学位论文]．杭州：浙江大学,2006
[117] Imran Naseem, Roberto Togneri, Mohammed Bennamoun. Linear Regression for Face Recognition.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2010,32(11):2106~2112
[118] M．Revan Ozkale．A jackknife ridge estimator in the linear regression model with heteroscedastic orcorrelated errors．Statistics and Probability Letters,2008,78:3159～3169
[119] Firinguetti L．A simulation study of ridge regression estimators with autocorrelated errors．CommStatist Simulation,1989,18(2):673～702
[120] Naseem I, Togneri R, Bennamoun M. Linear regression for face recognition. IEEE Transactions onPattern Analysis and Machine Intelligence,2010,32(11):2106-2112
[121]赵启斌，张丽清，CICHOCKI Andrzej.三维虚拟现实环境中基于EEG的异步BCI小车导航系统.科学通报:2888~2895
[122]李守轩，张华，刘继忠等.基于SPCE061A的智能轮椅语音控制系统.计算机工程，2006，34(14):248~250
[123]张国臣．基于ARM的脑电测量电路的研究：[硕士学位论文]．秦皇岛：燕山大学，2011
[124]徐宝国，宋爱国，杨仁桓．基于运动想象脑电的在线脑机接口实验．华中科技大学学报（自然科学版），2011，39（4）：60~64
[125]宋阳．基于单片机的语言识别系统软件设计与开发：[硕士学位论文]．成都：电子科技大学，2011