基于运动想象的脑机接口的研究
|
摘要
脑机接口(Brain-Computer Interface ,BCI)是基于脑电信号,而不依赖于脑的正常输出通路(即外周神经和肌肉)的,实现人脑与计算机或其它电子设备通讯和控制的技术。BCI可以为那些大脑正常,而中枢神经系统受到严重损伤的闭锁病人提供一种与外界交流和控制的途径,在康复领域具有重大应用价值。
本文在充分了解国内外相关研究的基础上,以运动想象作为切入点,系统地研究了基于左右手运动想象的脑机接口从离线分析到在线应用的各个层面的问题,取得了卓有成效的成果。
针对受试者在运动想象时的个体差异所造成的特征提取的盲目性,分类正确率难以进一步提高的缺点,本文提出了“基于时频分析的左右手运动想象频带能量特征差脑地形图法”,“基于样本熵的左右手运动想象复杂度特征差脑地形图法”,和“基于高阶累积量的左右手运动想象特征差脑地形图法”三种方法,成功解决了左右手运动想象BCI的个性化设计难题,实现了用最少的导联,最少的特征向量,最简单的特征提取和分类算法,取得较好的分类效果。
全面论证了频带能量,样本熵,4阶累积量作为左右手运动想象分类特征的可行性,并针对这三种特征研究了各自的特征提取快速算法和最佳参数选择问题。探讨了五种分类器在左右手运动想象BCI中的应用,研究了其分类原理与设计方法,全面比较了各种分类器的优缺点和适用范围,为实时BCI系统分类器的选择以及个性化BCI最佳特征的选择提供理论和实验依据。
为了进一步改善BCI系统性能,本文对运动想象思维策略的选择,EEG采集方式,训练数据选择等方面进行了深入探讨。实验结果表明:复杂度高的左右手运动想象策略对于BCI系统整体分类正确率的提高有所帮助;三同心圆环拉普拉斯电极具有良好的空间分辨率和空间定位性能,比传统EEG测量更能有效检测运动想象时的大脑区域化特征,而且操作简单,适合在线BCI系统的应用;“基于决策融合的训练数据选择算法”,可以排除原样本中由于受试者主观因素导致的“不利样本”,有效改善分类器的性能,提高BCI分类正确率。
自行研制了基于左右手运动想象的实时在线BCI系统,采用“决策融合”算法实现了对空闲状态的识别,成功解决了该类BCI连续工作问题。并在此基础上,将BCI与具体应用相结合,开发了“基于BCI的瘫痪病人辅助康复系统”和“基于BCI的环境控制系统”,初步实验证明了这两种应用方案的可行性,展示了其潜在应用价值。
Brain-Computer Interface (BCI) is a technology that used to realize the communication between human thoughts and computer or other electric instruments,which typically operates by EEG signals without depending on the brain’s normal output pathway of peripheral nerves and muscles. For those with severe disabilities (e.g., spinal cord injury, amyotrophic lateral sclerosis or brainstem stroke etc.), a BCI may be the only feasible method for communicating with others and for environmental control. BCI technology is highly valued both in theory and application area of rehabilitation.
Based on the knowledge of national and international BCI development, the work for this paper systematically studied a BCI that was based on the left-right hand motor imagery. It discussed both offline problems and real-time online problems, and made several significant improvements.
Due to subject specificity, a general feature extraction method is not possible to acquire high classification ratio from all subjects. To solve this problem, the paper proposed three feature-analysis methods:“EEG frequency-energy-difference brain topography methods during left-right hand motor imagery based on time-frequency analysis”;“EEG complexity-difference brain topography methods during left-right hand motor imagery based on sample entropy analysis”;“EEG feature-difference brain topography methods during left-right hand motor imagery based on high-order cumulant analysis”. Those methods successfully realized the subject-specific design, which enabled acquiring higher classification accuracy with least number of leads and least number of features, meanwhile simplest feature extraction and classification algorithm.
The paper systematically discussed the feasibility of taking the frequency energy, sample entropy, and 4th-order cumulant as the extraction feature for the left-right hand motor imagery classification, then developed fast feature extraction algorithm and optimum parameters selection algorithm for each of the three features. Five classifiers were proposed and discussed, including their classification principle, design method, advantages and limitations, and applicable conditions. The work of this paper provided a foundation for the BCI selection of best classifier and optimum feature parameters both theoretically and experimentally.
To further improve the efficiency of the BCI system, three factors that can influence the classification accuracy were studied, which were the imaginary content design, EEG signal acquiring electrodes selection, and training data selection. The experiment results showed that higher-complexity left-right hand motor imagery could enhance classification accuracy. Study also showed that compared with traditional electrodes, tripolar-ring Laplacian electrodes had higher signal to noise ratio, better spatial resolution and sensitivity, thus was more suitable for the regional-functioned motor imagery EEG acquiring and real-time online BCI system. What’s more, syncretic decision-making algorithm based training data selection could remove those bad signals caused by subjective factors, benefite the performance of classifiers, and consequently improve the efficiency of the BCI system.
The real-time online BCI system based on left-right hand motor imagery was established, and syncretic decision-making algorithm was used in recognition of vacant condition, which made the BCI system could work continuously. Based on this online BCI system, two application systems were also developed. One was“BCI based rehabilitation-assisting system for paralytic”, the other was“BCI based environmental control system”. Though those two systems were on their early stages, experiments had shown that they are feasible with great potential value.
本文在充分了解国内外相关研究的基础上,以运动想象作为切入点,系统地研究了基于左右手运动想象的脑机接口从离线分析到在线应用的各个层面的问题,取得了卓有成效的成果。
针对受试者在运动想象时的个体差异所造成的特征提取的盲目性,分类正确率难以进一步提高的缺点,本文提出了“基于时频分析的左右手运动想象频带能量特征差脑地形图法”,“基于样本熵的左右手运动想象复杂度特征差脑地形图法”,和“基于高阶累积量的左右手运动想象特征差脑地形图法”三种方法,成功解决了左右手运动想象BCI的个性化设计难题,实现了用最少的导联,最少的特征向量,最简单的特征提取和分类算法,取得较好的分类效果。
全面论证了频带能量,样本熵,4阶累积量作为左右手运动想象分类特征的可行性,并针对这三种特征研究了各自的特征提取快速算法和最佳参数选择问题。探讨了五种分类器在左右手运动想象BCI中的应用,研究了其分类原理与设计方法,全面比较了各种分类器的优缺点和适用范围,为实时BCI系统分类器的选择以及个性化BCI最佳特征的选择提供理论和实验依据。
为了进一步改善BCI系统性能,本文对运动想象思维策略的选择,EEG采集方式,训练数据选择等方面进行了深入探讨。实验结果表明:复杂度高的左右手运动想象策略对于BCI系统整体分类正确率的提高有所帮助;三同心圆环拉普拉斯电极具有良好的空间分辨率和空间定位性能,比传统EEG测量更能有效检测运动想象时的大脑区域化特征,而且操作简单,适合在线BCI系统的应用;“基于决策融合的训练数据选择算法”,可以排除原样本中由于受试者主观因素导致的“不利样本”,有效改善分类器的性能,提高BCI分类正确率。
自行研制了基于左右手运动想象的实时在线BCI系统,采用“决策融合”算法实现了对空闲状态的识别,成功解决了该类BCI连续工作问题。并在此基础上,将BCI与具体应用相结合,开发了“基于BCI的瘫痪病人辅助康复系统”和“基于BCI的环境控制系统”,初步实验证明了这两种应用方案的可行性,展示了其潜在应用价值。
Brain-Computer Interface (BCI) is a technology that used to realize the communication between human thoughts and computer or other electric instruments,which typically operates by EEG signals without depending on the brain’s normal output pathway of peripheral nerves and muscles. For those with severe disabilities (e.g., spinal cord injury, amyotrophic lateral sclerosis or brainstem stroke etc.), a BCI may be the only feasible method for communicating with others and for environmental control. BCI technology is highly valued both in theory and application area of rehabilitation.
Based on the knowledge of national and international BCI development, the work for this paper systematically studied a BCI that was based on the left-right hand motor imagery. It discussed both offline problems and real-time online problems, and made several significant improvements.
Due to subject specificity, a general feature extraction method is not possible to acquire high classification ratio from all subjects. To solve this problem, the paper proposed three feature-analysis methods:“EEG frequency-energy-difference brain topography methods during left-right hand motor imagery based on time-frequency analysis”;“EEG complexity-difference brain topography methods during left-right hand motor imagery based on sample entropy analysis”;“EEG feature-difference brain topography methods during left-right hand motor imagery based on high-order cumulant analysis”. Those methods successfully realized the subject-specific design, which enabled acquiring higher classification accuracy with least number of leads and least number of features, meanwhile simplest feature extraction and classification algorithm.
The paper systematically discussed the feasibility of taking the frequency energy, sample entropy, and 4th-order cumulant as the extraction feature for the left-right hand motor imagery classification, then developed fast feature extraction algorithm and optimum parameters selection algorithm for each of the three features. Five classifiers were proposed and discussed, including their classification principle, design method, advantages and limitations, and applicable conditions. The work of this paper provided a foundation for the BCI selection of best classifier and optimum feature parameters both theoretically and experimentally.
To further improve the efficiency of the BCI system, three factors that can influence the classification accuracy were studied, which were the imaginary content design, EEG signal acquiring electrodes selection, and training data selection. The experiment results showed that higher-complexity left-right hand motor imagery could enhance classification accuracy. Study also showed that compared with traditional electrodes, tripolar-ring Laplacian electrodes had higher signal to noise ratio, better spatial resolution and sensitivity, thus was more suitable for the regional-functioned motor imagery EEG acquiring and real-time online BCI system. What’s more, syncretic decision-making algorithm based training data selection could remove those bad signals caused by subjective factors, benefite the performance of classifiers, and consequently improve the efficiency of the BCI system.
The real-time online BCI system based on left-right hand motor imagery was established, and syncretic decision-making algorithm was used in recognition of vacant condition, which made the BCI system could work continuously. Based on this online BCI system, two application systems were also developed. One was“BCI based rehabilitation-assisting system for paralytic”, the other was“BCI based environmental control system”. Though those two systems were on their early stages, experiments had shown that they are feasible with great potential value.
引文
[1] Jonathan R. Wolpaw, Gerald E. Loeb, Brendan Z. Allison, et al. BCI Meeting 2005-Workshop on Signals and Recording Methods, IEEE Transactions On Neural Systems Rehabilitation Engineering, 2006,14(2):138~141
[2] Theresa M. Vaughan, Jonathan R. Wolpaw, Emanuel Donchin, EEG-based communication: prospects and problems, IEEE Transactions On Rehabilitation Engineering, 1996, 4(4):425~430
[3] Jonathan R.Wolpaw, Niels Birbaumer,William J. Heetderks,et al. Brain–Computer Interface Technology: A Review of the First International Meeting, IEEE Transactions On Rehabilitation Engineering, 2000, 8(2):164~172
[4] Vaughan, et al. Brain–Computer Interface Technology:A Review of the Second International Meeting, IEEE Transactions On Neural Systems Rehabilitation Engineering, 2003,11(2):94~109
[5] Kirkup L, Searle A, Craig A, et al. EEG-based system for rapid on-off switching without prior learning, Medical and Biological Engineering and Computing, 1997, 25(5): 504~509
[6] JR Wolpaw, N Birbaumer, DJ McFarland, et al. Brain-computer interfaces for communication and control, Clin. Neurophysiol., 2002,113:767~791
[7] Kübler A, Kotchoubey B, Kaiser J, et al. Brain-computer communication: unlock the locked-in, Psychological Bulletin. 2001,127: 358~375
[8] Melody M. Moore, Real-World Applications for Brain–Computer Interface Technology, IEEE Transactions On Neural Systems Rehabilitation Engineering, 2003,11(2):162~165
[9] Gerwin Schalk , Dennis J. McFarland, Thilo Hinterberger, BCI2000: A General-Purpose Brain-Computer Interface (BCI) System,IEEE Transactions On Biomedical Engineering, 2004,51(6):1034~1043
[10] Robert Leeb, Gert Pfurtscheller, Walking through a Virtual City by Thought, Proceedings of the 26th Annual International Conference of the IEEE EMBS,San Francisco, CA, USA , 2004.9,1~5
[11] Steven G. Mason, Gary E. Birch, A General Framework for Brain–Computer Interface Design, IEEE Transactions On Neural Systems Rehabilitation Engineering,2003,11(1):70~85
[12] 杨帮华,颜国正,丁国清,脑机接口关键技术研究,北京生物医学工程,2005,24(4):308~310
[13] Dewan, Occipital alpha rhythm eye position and lens accommodation, Nature,1967,214:975~977
[14] JJ Vidal, Toward Direct Brain-Computer Communication, Annual Review of Biophysics and Bioengineering, 1973, 2:157~180
[15] Vidal JJ, Real-time detection of brain events in EEG, Proceedings of IEEE, 1977 ,65(5):633~664
[16] Dennis J. McFarland, Charles W. Anderson, Klaus-Robert Müller, et al. BCI Meeting 2005—Workshop on BCI Signal Processing: Feature Extraction and Translation, IEEE Transactions On Neural Systems Rehabilitation Engineering,2006,14(2):135~138
[17] Febo Cincotti, Luigi Bianchi, Gary Birch,et.at. BCI Meeting 2005—Workshop on Technology: Hardware and Software, IEEE Transactions On Neural Systems Rehabilitation Engineering, 2006,14(2):128~131
[18] A. Kübler, V. K. Mushahwar, L. R. Hochberg, et.at. BCI Meeting 2005—Workshop on Clinical Issues and Applications, IEEE Transactions On Neural Systems Rehabilitation Engineering, 2006,14(2):131~134
[19] 沈强,蒋大宗,基于EEG的信息交换技术的应用前景与存在的问题,国外医学生物医学工程分册,1999,22 (1): 51~56
[20] 华小梅,林家瑞,官金安,“脑-机接口”进展的研究,国外医学生物医学工程分册,2004,27 (2): 94~111
[21] 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
[22] Sutter EE, The brain response interface: communication through visually-induced electrical brain responses, Journal of Microcomputer Applications, 1992, 15: 31~45
[23] 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,重庆大学,2003
[24] Middendorf M, McMillan G, Calhoun G, et al. Brain–computer interfaces basedon the steady-state visual-evoked response, IEEE Transactions On Rehabilitation Engineering, 2000,8(2):211~214
[25] Cheng M, Gao XR, Gao SK, et al. Design and implementation of a brain-computer interface with high transfer rates, IEEE Transactions on Biomedical Engineering, 2002,49 (10):1181~1186
[26] Yijun Wang, Ruiping Wang, Xiaorong Gao, Bo Hong,et al. A Practical VEP-Based Brain–Computer Interface, IEEE Transactions On Neural Systems Rehabilitation Engineering, 2006,14(2):234~240
[27] Xiaorong Gao Dingfeng Xu Ming Cheng ,et al. A BCI-based environmental controller for the motion-disabled, IEEE Transactions On Neural Systems Rehabilitation Engineering, 2003 ,11( 2):137~140
[28] G. R. Müller-Putz, R. Scherer, C. Brauneis, G. Pfurtscheller, Steady-state visual evoked potential (SSVEP)—Based communication: Impact of harmonic frequency components, J. Neural Eng., 2005, 2:123~130
[29] P. J. Cilliers, Van der Kouwe, and J. W.Andre, VEP-based computer interface for C2-quadriplegics, Proc. Annu. Conf. Engineering in Medicine and Biology, 1993, 1236~1238
[30] Muller MM, Picton TW, Valdes-Sosa P, et al. Effects of spatial selective attention on the steady-state visual evoked potential in the 20–28 Hz range, Cognition Brain Research, 1998, 6:249~261
[31] S. T. Morgan, J. C. Hansen, S. A. Hillyard, Selective attention to stimulus location modulates the steady-state visual evoked potential, Proc. Nat. Acad. Sci. USA, 1996, 93: 4770~4774
[32] Farwell LA, Donchin E,Talking off the top of your head: A mental prosthesis utilizing event-related brain potentials,Electroenceph. Clin. Neurophysiol.,1988, 70(6):510~523
[33] Donchin E, Spencer KM, Wijesinghe R. The mental prosthesis: Assessing the speed of a P300-based brain–computer interface. IEEE Transactions On Rehabilitation Engineering, 2000, 8(2): 174~179
[34] Serby H, Yom-Tov E, Inbar GF, An improved P300-based brain-computer interface, IEEE Transactions On Neural Systems Rehabilitation Engineering, 2005 ,13(1):89~98
[35] Sellers EW, Krusienski DJ, McFarland DJ, A P300 event-related potential brain-computer interface (BCI): the effects of matrix size and inter stimulusinterval on performance, Biol Psychol. , 2006,73(3):242~52
[36] 庄平,脑电事件相关去同步化和同步化活动与运动相关性作业,中国临床康复,2004,8(1):152~153
[37] G. Pfurtscheller, C. Neuper, C. Guger, Current Trends in Graz Brain–Computer Interface (BCI) Research, IEEE Transactions On Rehabilitation Engineering, 2000, 8(2):216~219
[38] G. Pfurtscheller, C. Neuper, G. R. Müller,Graz-BCI: State of the Art and Clinical Applications, IEEE Transactions On Neural Systems Rehabilitation Engineering, 2003,11(2):177~180
[39] G. Pfurtscheller, G. R. Müller-Putz, A. Schl?gl, 15 Years of BCI Research at Graz University of Technology:Current Projects, IEEE Transactions On Neural Systems Rehabilitation Engineering, 2006,14(2):205~210
[40] Gert Pfurtscheller, Christa Neuper, Alois Schl?gl, Separability of EEG Signals Recorded During Right and Left Motor Imagery Using Adaptive Autoregressive Parameters, IEEE Transactions On Rehabilitation Engineering,1998,6(3):316~325
[41] Gernot R. Müller, Christa Neuper, and Gert Pfurtscheller, Implementation of a Telemonitoring System for the Control of an EEG-Based Brain–Computer Interface, IEEE Transactions On Neural Systems Rehabilitation Engineering, 2003,11(1): 54~59
[42] B. Graimann, J. E. Huggins, A. Schl?gl, et al. Toward a direct brain interface based on human subdural recordings and wavelet-packet analysis, IEEE Transactions On Biomedical Engineering, 2004,51(6): 954~962
[43] R. Scherer,G. R.Müller, C. Neuper,et al. An asynchronously controlled EEG-based virtual keyboard: Improvement of the spelling rate, IEEE Transactions On Biomedical Engineering, 2004, 51(6):979~984
[44] G. Pfurtscheller, G. R. Müller, J. Pfurtscheller, et al. Thought-control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia,” Neuroscience Letters, 2003, 351:33~36
[45] G. R. Müller-Putz, R. Scherer, G. Pfurtscheller, et al. EEG based neuroprosthesis control: A step towards clinical practice, Neuroscience Letters, 2005, 382:169~174
[46] B. Graimann, J. E. Huggins, A. Schl?gl, et al. Detection of movement-related desynchronization patterns in ongoing single-channel electrocorticogram, IEEETransactions On Neural Systems Rehabilitation Engineering, 2003,11(3):276~281
[47] M. Pregenzer, G. Pfurtscheller, Frequency component selection of an EEG-based brain to computer interface,” IEEE Transactions On Rehabilitation Engineering, 1999,7(4): 413~419
[48] C. Neuper, R. Scherer, M. Reiner,et al. Imagery of motor actions: Differential effect of kinsthetic and visual-motor mode of imagery in single-trial EEG, Cognition Brain Research, 2005,25:668~677
[49] G. Townsend, B. Graimann, G. Pfurtscheller, Continous EEG classification during motor imagery, IEEE Transactions On Neural Systems Rehabilitation Engineering, 2004,12(2): 258~265
[50] C. Brunner, B. Graimann, J. E. Huggins, et al. Phase relationships between different subdural electrode recordings in man, Neuroscience Letters, 2005,375:69~74
[51] G. Townsend, B. Graimann, G. Pfurtscheller, A comparison of common spatial patterns with complex band power features in a fourclass BCI experiment, IEEE Transactions On Biomedical Engineering, 2006, 53(4):642~651
[52] C. Neuper, G. R.Müller, A.Kübler,et al. Clinical application of an EEG-based brain-computer interface: A case study in a patient with severe motor impairment,” Clinical Neurophysiology, 2003,114:399~409
[53] G. Pfurtscheller, R. Leeb, C. Keinrath, et al. Walking from thought, Brain Res., 2006 ,1071:145~152
[54] A Schl?gl, C Keinrath, D Zimmermann, A fully automated correction method of EOG artifacts in EEG recordings, Clinical Neurophysiology, 2007, 118(1):98~104
[55] C.Guger, H.Ramoser, G.Pfurtscheller, Real-Time EEG Analysis with Subject-Specific Spatial Patterns for a Brain–Computer Interface (BCI), IEEE Transactions On Rehabilitation Engineering, 2000,8(4): 447~455
[56] George Townsend, Bernhard Graimann, Gert Pfurtscheller, Continuous EEG Classification During Motor Imagery—Simulation of an Asynchronous BCI, IEEE Transactions On Neural Systems Rehabilitation Engineering, 2004,12(2):258~265
[57] Bernhard Graimann, Jane E. Huggins, Alois Schl?gl,Detection of Movement-Related Desynchronization Patterns in Ongoing Single-ChannelElectrocorticogram, IEEE Transactions On Neural Systems Rehabilitation Engineering, 2003,11(3): 276~281
[58] Gert Pfurtscheller,Christa Neuper,Motor Imagery and Direct Brain–Computer Communication, Proceedings of the IEEE,2001,89(7):1123~1134
[59] Georg E. Fabiani, Dennis J. McFarland, Jonathan R. Wolpaw, et al. Conversion of EEG Activity Into Cursor Movement by a Brain–Computer Interface (BCI), IEEE Transactions On Neural Systems Rehabilitation Engineering, 2004,12(3):331~338
[60] B. Obermaier, G. R. Müller, G. Pfurtscheller, “Virtual Keyboard” Controlled bySpontaneous EEG Activity, IEEE Transactions On Neural Systems Rehabilitation Engineering, 2003,11(4):422~426
[61] G. Pfurtscheller, C. Guger, G. Müller, Brain oscillations control hand orthosis in a tetraplegic, Neuroscience Letters,2000, 292:211~214
[62] G.R. Müller, C. Neuper, R. Rupp, Event-related beta EEG changes during wrist movements induced by functional electrical stimulation of forearm muscles in man, Neuroscience Letters, 2003,340:143~147
[63] Gernot R. Müller -Putz, Reinhold Scherer, Gert Pfurtscheller, EEG-based neuroprosthesis control: A step towards clinical practice, Neuroscience Letters, 2005,382:169~174
[64] 陈强,基于自发脑电的脑—机接口研究,博士学位论文,中国科学技术大学,2005
[65] 赵丽,基于脑电信号的脑-机接口技术研究,博士学位论文,天津大学,2003
[66] Miner LA, McFarland DJ, Wolpaw JR , Answering questions with anelectroencephalogram-based brain-computer interface,Arch. Phys. Med. Rehabil. ,1998,79(9):1029~1033
[67] Wolpaw JR, McFarland DJ, Neat GW, et al. An EEG-based brain-computer interface for cursor control,Electroenceph. Clin. Neurophysiol.,1991,78(3): 252~259
[68] Wolpaw JR, McFarland DJ, Vaughan TM. Brain–computer interface research at the Wadsworth center,IEEE Transactions On Rehabilitation Engineering, 2000,8(2): 222~226
[69] Wolpaw JR, McFarland DJ , Multichannel EEG-based brain-computer communication,Electroenceph. Clin. Neurophysiol. ,1994, 90(6): 444~449
[70] Birbaumer N., Kübler A., Ghanayim N., et al. The Thought Translation Device(TTD) for completely paralyzed patients, IEEE Transactions On Rehabilitation Engineering, 2000,8(2):190~193
[71] Birbaumer N, Ghanayim N, Hinterberger T, et.al. A spelling device for the paralysed, Nature,1999,398:297~298
[72] Birbaumer N, Elbert T, Canavan A, et al. Slow potentials of the cerebral cortex and behavior, Physiological Reviews, 1990,70: 1~41
[73] Keirn ZA, Aunon JI, A new mode of communication between man and his surroundings, IEEE Transactions On Biomedical Engineering,1990,37(12): 1209~1214
[74] Anderson CW, Stolz EA, Shamsunder S,Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks, IEEE Transactions On Biomedical Engineering,1998,45(3): 277~286
[75] 薛建中,基于自发脑电的脑-计算机接口研究,博士学位论文,西安交通大学,2003
[76] 陈香,基于节律同化效应的思维脑电信号分类研究,博士学位论文,中国科学技术大学,2004
[77] BCI Competition III. http://ida.first.fraunhofer.de/projects/bci/competition_iii/
[78] 张明岛,陈兴时,脑诱发电位学,上海:上海科技教育出版社,1995,1~120
[79] 姚志彬,临床神经解剖学,广东:世界图书出版公司,2001,1~330
[80] 谭郁玲,临床脑电图与脑地形图学,北京:人民卫生出版社,1999,1~100
[81] 杨雄里,脑科学的现代进展,上海:上海科学教育出版社,1999,1~100
[82] 吴小培,独立分量分析及其在脑电信号处理中的应用,博士学位论文,中国科学技术大学,2002
[83] 黄力宇,基于大脑多阶信息理论的脑电图(EEG)分析与应用研究,博士学位论文,西安交通大学,2001
[84] 李颖洁,脑电信号动力学特性分析及其在精神分裂症中的应用研究,博士学位论文,上海交通大学,2001
[85] 张辉,大脑电生理信号特征信息的提取和识别新技术研究,博士学位论文,西安交通大学,2003
[86] G. Pfurtscheller, F.H. Lopes da Silva,Event-related EEG/MEG synchronization and desynchronization: basic principles,Clinical Neurophysiology, 1999,110:1842~1857
[87] Zhuang P, Toro C, Grafman J, et al. Event-related desynchronization (ERD) in the alpha frequency during development of implicit and explicit learning,Electroenceph. Clin. Neurophysiol.,1997 ,102(4):374~81
[88] Leocani L, Toro C, Manganotti P, et al. Event-related coherence and event-related desynchronization/synchronization in the 10 Hz and 20 Hz EEG during self-paced movements, Electroenceph. Clin. Neurophysiol. ,1997,104(3):199~206
[89] G Pfurtscheller, A Berghold, Patterns of cortical activation during planning of voluntary movement, Electroenceph. Clin. Neurophysiol.,1989 , 72(3): 250~258
[90] Manganotti P, Gerloff C, Toro C, et al. Task-related coherence and task-related spectral power changes during sequential finger movements, Electroenceph. Clin. Neurophysiol.,1998 ,109(1):50~62
[91] G. Pfurtscheller, Event-related synchronization(ERS): an electrophysiological correlate of cortical areas at rest, Electroenceph. Clin. Neurophysiol.,1992, 83(1):62~69
[92] Pfurtscheller G, Neuper C, Kalcher J, 40-Hz oscillations during motor behavior in man, Neuroscience Letters, 1993 ,164(1):179~182
[93] 余英林,谢胜利,蔡汉添等,信号处理新方法导论,北京:清华大学出版社,2004,255~262
[94] 张旭东,陆明泉,离散随机信号处理,北京:清华大学出版社,2005,1~400
[95] 葛哲学,陈仲生,Matlab时频分析技术及其应用,北京:人民邮电出版社,2006,1~19
[96] 刘庆云,李志舜,刘朝晖,时频分析技术及研究现状,计算机工程,2004,30(1):171~173
[97] Joshua S. Richman, J. Randall Moorman, Physiological time-series analysis using approximate entropy and sample entropy, Am J Physiol Heart Circ Physiol, 2000, 278: H2039~ H2049
[98] 刘慧,和卫星,陈晓平.生物时间序列的近似熵和样本熵方法比较.仪器仪表学报,2004;25(4):806
[99] SM Pincus, Approximate Entropy as a Measure of System Complexity, Proceedings of National Academy of Sciences, USA, 1991;2297
[100] 洪波,唐庆玉,杨福生等.近似熵、互近似熵的性质、快速算法及其在脑电与认知研究中的初步应用.信号处理, 1999;15(2):100~107
[101] 陈亚勇,Matlab信号处理详解,北京:人民邮电出版社,2001,131~153
[102] 吴小培,叶中付,基于脑电四阶累积量的运动意识分类研究. 生物物理学报,21(5):364～370
[103] 杨福生,吕扬生,生物医学信号的处理和识别(第一版),天津:天津科技翻译出版公司,1997,57~315
[104] R. Duda,P. Hart,D. Stork. Pattern Classification(second edition), New York: Wiley, 2001, 16~218
[105] Sergios Theodoridis,Konstantinos Koutroumbas,模式识别(第二版),北京:机械工业出版社,2004,13~177
[106] 边肇祺,张学工,模式识别(第二版),北京:清华大学出版社,2000,1~336
[107] K.S. Narendra, K. Parthasarathy, Identification and control of dynamical systems using neuralnetworks,IEEE Transactions on Neural Networks,1990,1(1):4~27
[108] 段勤业,杨宗凯,谈正等,模式识别与神经网络,北京:机械工业出版社,1992,113~133
[109] 张立明,人工神经网络的模型及其应用,上海:复旦大学出版社,1993,13~51
[110] 董长虹,神经网络与应用,北京:国防工业出版社,2005,14~251
[111] M.T. Hagan, M.B. Menhaj, Training feedforward networks with the Marquardt algorithm, IEEE Transactions on Neural Networks,1994,5(6):989~993
[112] K.Z. Mao, K.C. Tan, W. Ser, Probabilistic neural-network structure determination for patternclassification, IEEE Transactions on Neural Networks,2000,11(4):1009~1016
[113] R.L. Streit, T.E. Luginbuhl, Maximum likelihood training of probabilistic neural networks, IEEE Transactions on Neural Networks,1994,5(5): 764~783
[114] B A. G.ors, I. Pitas, Median radial basis function neural network, IEEE Transactions on Neural Networks,1996,7(6): 1351~1364
[115] Nello Cristianini, John Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods(李国正,王猛,曾华军译),北京:电子工业出版社,82~139
[116] 张学工,关于统计学习理论与支持向量机,自动化学报,2000,26(1):32~42
[117] D.J. Sebald, J.A. Bucklew, Support vector machine techniques for nonlinear equalization, IEEE Transactions on Signal Processing, 2000, 48(11): 3217~3226
[118] B. Scholkopf, C.J.C. Kah-Kay Sung Burges, F. Girosi, et al. Comparingsupport vector machines with Gaussian kernels to radialbasis function classifiers, IEEE Transactions on Signal Processing, 1997, 45(11): 2758~2765
[119] 邓乃扬,田英杰,数据挖掘中的新方法——支持向量机,北京:科学出版社,2004,1~378
[120] Bin He, Brain electric source imaging: scalp Laplacian mapping and cortical imaging .Critical Reviews in Biomedical Engineering, 1999, 27:149~88
[121] ZHAI Yi-ran, YAO De-zhong, A Study on the Finite Difference Approach of the Surface Laplacian. Journal of Electronic Science and Technology of China, 2006, 4(1):35~38
[122] Hjorth B.An on-line transformation of EEG scalp potentials into orthogonal source derivations, Electroencephalogr Clin Neurophysiol. 1975, 39(5) :526~530
[123] Gin-Shin Chen, Chih-Wei Chen, Ming-Shaung Ju,ect. Portable Active Surface Laplacian EEG Sensor for Real-Time Mu Rhythms Detection, Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, China: 2005,5424~5426
[124] Gang Li, Lili Wang, ect. The Feasibility Study of the Laplacian Electrode for EEG, Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, China: 2005,4670~4673
[125] Geertjan Huiskamp. Difference formulas for the surface laplacian on a triangulated surface, Journal of Computational Physics, 1991,95(2):477~496
[126] Walter G Besio, Kanthaiah Koka, Rajesh Aakula, etc. Tri-Polar Concentric Ring Electrode Development for Laplacian Electroencephalography, IEEE Transactions On Biomedical Engineering, 2006, 53(5):926~933
[127] 曹玉珍,靳世久,李刚等,体表势表面拉普拉斯的三电极传感方法实现及其实验研究,中国生物医学工程学报,2006,25(1):125~128
[128] 万百坤,李佳,明东,用于肢体运动系统的神经假体研究进展,中国医疗器械杂志,2006,30(4):235~240
[129] 袁启明,功能性电刺激装置及其临床应用前景,上海生物医学工程杂志,1997,18(1):52~54
[130] 高怀民,蓝宁,功能性电刺激在截瘫治疗中的应用,中国脊柱脊髓杂志,1995,5(6):268~270
[131] 蓝宁,肖志雄,聂开宝等,功能性电刺激的原理、设计与应用(二),中国康复理论与实践,1998,4(1):7~9
[132] 聂开宝,蓝宁,马亚权等,功能性电刺激的原理、设计与应用(三),中国康复理论与实践,1998,4(4):153~155
[2] Theresa M. Vaughan, Jonathan R. Wolpaw, Emanuel Donchin, EEG-based communication: prospects and problems, IEEE Transactions On Rehabilitation Engineering, 1996, 4(4):425~430
[3] Jonathan R.Wolpaw, Niels Birbaumer,William J. Heetderks,et al. Brain–Computer Interface Technology: A Review of the First International Meeting, IEEE Transactions On Rehabilitation Engineering, 2000, 8(2):164~172
[4] Vaughan, et al. Brain–Computer Interface Technology:A Review of the Second International Meeting, IEEE Transactions On Neural Systems Rehabilitation Engineering, 2003,11(2):94~109
[5] Kirkup L, Searle A, Craig A, et al. EEG-based system for rapid on-off switching without prior learning, Medical and Biological Engineering and Computing, 1997, 25(5): 504~509
[6] JR Wolpaw, N Birbaumer, DJ McFarland, et al. Brain-computer interfaces for communication and control, Clin. Neurophysiol., 2002,113:767~791
[7] Kübler A, Kotchoubey B, Kaiser J, et al. Brain-computer communication: unlock the locked-in, Psychological Bulletin. 2001,127: 358~375
[8] Melody M. Moore, Real-World Applications for Brain–Computer Interface Technology, IEEE Transactions On Neural Systems Rehabilitation Engineering, 2003,11(2):162~165
[9] Gerwin Schalk , Dennis J. McFarland, Thilo Hinterberger, BCI2000: A General-Purpose Brain-Computer Interface (BCI) System,IEEE Transactions On Biomedical Engineering, 2004,51(6):1034~1043
[10] Robert Leeb, Gert Pfurtscheller, Walking through a Virtual City by Thought, Proceedings of the 26th Annual International Conference of the IEEE EMBS,San Francisco, CA, USA , 2004.9,1~5
[11] Steven G. Mason, Gary E. Birch, A General Framework for Brain–Computer Interface Design, IEEE Transactions On Neural Systems Rehabilitation Engineering,2003,11(1):70~85
[12] 杨帮华,颜国正,丁国清,脑机接口关键技术研究,北京生物医学工程,2005,24(4):308~310
[13] Dewan, Occipital alpha rhythm eye position and lens accommodation, Nature,1967,214:975~977
[14] JJ Vidal, Toward Direct Brain-Computer Communication, Annual Review of Biophysics and Bioengineering, 1973, 2:157~180
[15] Vidal JJ, Real-time detection of brain events in EEG, Proceedings of IEEE, 1977 ,65(5):633~664
[16] Dennis J. McFarland, Charles W. Anderson, Klaus-Robert Müller, et al. BCI Meeting 2005—Workshop on BCI Signal Processing: Feature Extraction and Translation, IEEE Transactions On Neural Systems Rehabilitation Engineering,2006,14(2):135~138
[17] Febo Cincotti, Luigi Bianchi, Gary Birch,et.at. BCI Meeting 2005—Workshop on Technology: Hardware and Software, IEEE Transactions On Neural Systems Rehabilitation Engineering, 2006,14(2):128~131
[18] A. Kübler, V. K. Mushahwar, L. R. Hochberg, et.at. BCI Meeting 2005—Workshop on Clinical Issues and Applications, IEEE Transactions On Neural Systems Rehabilitation Engineering, 2006,14(2):131~134
[19] 沈强,蒋大宗,基于EEG的信息交换技术的应用前景与存在的问题,国外医学生物医学工程分册,1999,22 (1): 51~56
[20] 华小梅,林家瑞,官金安,“脑-机接口”进展的研究,国外医学生物医学工程分册,2004,27 (2): 94~111
[21] 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
[22] Sutter EE, The brain response interface: communication through visually-induced electrical brain responses, Journal of Microcomputer Applications, 1992, 15: 31~45
[23] 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,重庆大学,2003
[24] Middendorf M, McMillan G, Calhoun G, et al. Brain–computer interfaces basedon the steady-state visual-evoked response, IEEE Transactions On Rehabilitation Engineering, 2000,8(2):211~214
[25] Cheng M, Gao XR, Gao SK, et al. Design and implementation of a brain-computer interface with high transfer rates, IEEE Transactions on Biomedical Engineering, 2002,49 (10):1181~1186
[26] Yijun Wang, Ruiping Wang, Xiaorong Gao, Bo Hong,et al. A Practical VEP-Based Brain–Computer Interface, IEEE Transactions On Neural Systems Rehabilitation Engineering, 2006,14(2):234~240
[27] Xiaorong Gao Dingfeng Xu Ming Cheng ,et al. A BCI-based environmental controller for the motion-disabled, IEEE Transactions On Neural Systems Rehabilitation Engineering, 2003 ,11( 2):137~140
[28] G. R. Müller-Putz, R. Scherer, C. Brauneis, G. Pfurtscheller, Steady-state visual evoked potential (SSVEP)—Based communication: Impact of harmonic frequency components, J. Neural Eng., 2005, 2:123~130
[29] P. J. Cilliers, Van der Kouwe, and J. W.Andre, VEP-based computer interface for C2-quadriplegics, Proc. Annu. Conf. Engineering in Medicine and Biology, 1993, 1236~1238
[30] Muller MM, Picton TW, Valdes-Sosa P, et al. Effects of spatial selective attention on the steady-state visual evoked potential in the 20–28 Hz range, Cognition Brain Research, 1998, 6:249~261
[31] S. T. Morgan, J. C. Hansen, S. A. Hillyard, Selective attention to stimulus location modulates the steady-state visual evoked potential, Proc. Nat. Acad. Sci. USA, 1996, 93: 4770~4774
[32] Farwell LA, Donchin E,Talking off the top of your head: A mental prosthesis utilizing event-related brain potentials,Electroenceph. Clin. Neurophysiol.,1988, 70(6):510~523
[33] Donchin E, Spencer KM, Wijesinghe R. The mental prosthesis: Assessing the speed of a P300-based brain–computer interface. IEEE Transactions On Rehabilitation Engineering, 2000, 8(2): 174~179
[34] Serby H, Yom-Tov E, Inbar GF, An improved P300-based brain-computer interface, IEEE Transactions On Neural Systems Rehabilitation Engineering, 2005 ,13(1):89~98
[35] Sellers EW, Krusienski DJ, McFarland DJ, A P300 event-related potential brain-computer interface (BCI): the effects of matrix size and inter stimulusinterval on performance, Biol Psychol. , 2006,73(3):242~52
[36] 庄平,脑电事件相关去同步化和同步化活动与运动相关性作业,中国临床康复,2004,8(1):152~153
[37] G. Pfurtscheller, C. Neuper, C. Guger, Current Trends in Graz Brain–Computer Interface (BCI) Research, IEEE Transactions On Rehabilitation Engineering, 2000, 8(2):216~219
[38] G. Pfurtscheller, C. Neuper, G. R. Müller,Graz-BCI: State of the Art and Clinical Applications, IEEE Transactions On Neural Systems Rehabilitation Engineering, 2003,11(2):177~180
[39] G. Pfurtscheller, G. R. Müller-Putz, A. Schl?gl, 15 Years of BCI Research at Graz University of Technology:Current Projects, IEEE Transactions On Neural Systems Rehabilitation Engineering, 2006,14(2):205~210
[40] Gert Pfurtscheller, Christa Neuper, Alois Schl?gl, Separability of EEG Signals Recorded During Right and Left Motor Imagery Using Adaptive Autoregressive Parameters, IEEE Transactions On Rehabilitation Engineering,1998,6(3):316~325
[41] Gernot R. Müller, Christa Neuper, and Gert Pfurtscheller, Implementation of a Telemonitoring System for the Control of an EEG-Based Brain–Computer Interface, IEEE Transactions On Neural Systems Rehabilitation Engineering, 2003,11(1): 54~59
[42] B. Graimann, J. E. Huggins, A. Schl?gl, et al. Toward a direct brain interface based on human subdural recordings and wavelet-packet analysis, IEEE Transactions On Biomedical Engineering, 2004,51(6): 954~962
[43] R. Scherer,G. R.Müller, C. Neuper,et al. An asynchronously controlled EEG-based virtual keyboard: Improvement of the spelling rate, IEEE Transactions On Biomedical Engineering, 2004, 51(6):979~984
[44] G. Pfurtscheller, G. R. Müller, J. Pfurtscheller, et al. Thought-control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia,” Neuroscience Letters, 2003, 351:33~36
[45] G. R. Müller-Putz, R. Scherer, G. Pfurtscheller, et al. EEG based neuroprosthesis control: A step towards clinical practice, Neuroscience Letters, 2005, 382:169~174
[46] B. Graimann, J. E. Huggins, A. Schl?gl, et al. Detection of movement-related desynchronization patterns in ongoing single-channel electrocorticogram, IEEETransactions On Neural Systems Rehabilitation Engineering, 2003,11(3):276~281
[47] M. Pregenzer, G. Pfurtscheller, Frequency component selection of an EEG-based brain to computer interface,” IEEE Transactions On Rehabilitation Engineering, 1999,7(4): 413~419
[48] C. Neuper, R. Scherer, M. Reiner,et al. Imagery of motor actions: Differential effect of kinsthetic and visual-motor mode of imagery in single-trial EEG, Cognition Brain Research, 2005,25:668~677
[49] G. Townsend, B. Graimann, G. Pfurtscheller, Continous EEG classification during motor imagery, IEEE Transactions On Neural Systems Rehabilitation Engineering, 2004,12(2): 258~265
[50] C. Brunner, B. Graimann, J. E. Huggins, et al. Phase relationships between different subdural electrode recordings in man, Neuroscience Letters, 2005,375:69~74
[51] G. Townsend, B. Graimann, G. Pfurtscheller, A comparison of common spatial patterns with complex band power features in a fourclass BCI experiment, IEEE Transactions On Biomedical Engineering, 2006, 53(4):642~651
[52] C. Neuper, G. R.Müller, A.Kübler,et al. Clinical application of an EEG-based brain-computer interface: A case study in a patient with severe motor impairment,” Clinical Neurophysiology, 2003,114:399~409
[53] G. Pfurtscheller, R. Leeb, C. Keinrath, et al. Walking from thought, Brain Res., 2006 ,1071:145~152
[54] A Schl?gl, C Keinrath, D Zimmermann, A fully automated correction method of EOG artifacts in EEG recordings, Clinical Neurophysiology, 2007, 118(1):98~104
[55] C.Guger, H.Ramoser, G.Pfurtscheller, Real-Time EEG Analysis with Subject-Specific Spatial Patterns for a Brain–Computer Interface (BCI), IEEE Transactions On Rehabilitation Engineering, 2000,8(4): 447~455
[56] George Townsend, Bernhard Graimann, Gert Pfurtscheller, Continuous EEG Classification During Motor Imagery—Simulation of an Asynchronous BCI, IEEE Transactions On Neural Systems Rehabilitation Engineering, 2004,12(2):258~265
[57] Bernhard Graimann, Jane E. Huggins, Alois Schl?gl,Detection of Movement-Related Desynchronization Patterns in Ongoing Single-ChannelElectrocorticogram, IEEE Transactions On Neural Systems Rehabilitation Engineering, 2003,11(3): 276~281
[58] Gert Pfurtscheller,Christa Neuper,Motor Imagery and Direct Brain–Computer Communication, Proceedings of the IEEE,2001,89(7):1123~1134
[59] Georg E. Fabiani, Dennis J. McFarland, Jonathan R. Wolpaw, et al. Conversion of EEG Activity Into Cursor Movement by a Brain–Computer Interface (BCI), IEEE Transactions On Neural Systems Rehabilitation Engineering, 2004,12(3):331~338
[60] B. Obermaier, G. R. Müller, G. Pfurtscheller, “Virtual Keyboard” Controlled bySpontaneous EEG Activity, IEEE Transactions On Neural Systems Rehabilitation Engineering, 2003,11(4):422~426
[61] G. Pfurtscheller, C. Guger, G. Müller, Brain oscillations control hand orthosis in a tetraplegic, Neuroscience Letters,2000, 292:211~214
[62] G.R. Müller, C. Neuper, R. Rupp, Event-related beta EEG changes during wrist movements induced by functional electrical stimulation of forearm muscles in man, Neuroscience Letters, 2003,340:143~147
[63] Gernot R. Müller -Putz, Reinhold Scherer, Gert Pfurtscheller, EEG-based neuroprosthesis control: A step towards clinical practice, Neuroscience Letters, 2005,382:169~174
[64] 陈强,基于自发脑电的脑—机接口研究,博士学位论文,中国科学技术大学,2005
[65] 赵丽,基于脑电信号的脑-机接口技术研究,博士学位论文,天津大学,2003
[66] Miner LA, McFarland DJ, Wolpaw JR , Answering questions with anelectroencephalogram-based brain-computer interface,Arch. Phys. Med. Rehabil. ,1998,79(9):1029~1033
[67] Wolpaw JR, McFarland DJ, Neat GW, et al. An EEG-based brain-computer interface for cursor control,Electroenceph. Clin. Neurophysiol.,1991,78(3): 252~259
[68] Wolpaw JR, McFarland DJ, Vaughan TM. Brain–computer interface research at the Wadsworth center,IEEE Transactions On Rehabilitation Engineering, 2000,8(2): 222~226
[69] Wolpaw JR, McFarland DJ , Multichannel EEG-based brain-computer communication,Electroenceph. Clin. Neurophysiol. ,1994, 90(6): 444~449
[70] Birbaumer N., Kübler A., Ghanayim N., et al. The Thought Translation Device(TTD) for completely paralyzed patients, IEEE Transactions On Rehabilitation Engineering, 2000,8(2):190~193
[71] Birbaumer N, Ghanayim N, Hinterberger T, et.al. A spelling device for the paralysed, Nature,1999,398:297~298
[72] Birbaumer N, Elbert T, Canavan A, et al. Slow potentials of the cerebral cortex and behavior, Physiological Reviews, 1990,70: 1~41
[73] Keirn ZA, Aunon JI, A new mode of communication between man and his surroundings, IEEE Transactions On Biomedical Engineering,1990,37(12): 1209~1214
[74] Anderson CW, Stolz EA, Shamsunder S,Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks, IEEE Transactions On Biomedical Engineering,1998,45(3): 277~286
[75] 薛建中,基于自发脑电的脑-计算机接口研究,博士学位论文,西安交通大学,2003
[76] 陈香,基于节律同化效应的思维脑电信号分类研究,博士学位论文,中国科学技术大学,2004
[77] BCI Competition III. http://ida.first.fraunhofer.de/projects/bci/competition_iii/
[78] 张明岛,陈兴时,脑诱发电位学,上海:上海科技教育出版社,1995,1~120
[79] 姚志彬,临床神经解剖学,广东:世界图书出版公司,2001,1~330
[80] 谭郁玲,临床脑电图与脑地形图学,北京:人民卫生出版社,1999,1~100
[81] 杨雄里,脑科学的现代进展,上海:上海科学教育出版社,1999,1~100
[82] 吴小培,独立分量分析及其在脑电信号处理中的应用,博士学位论文,中国科学技术大学,2002
[83] 黄力宇,基于大脑多阶信息理论的脑电图(EEG)分析与应用研究,博士学位论文,西安交通大学,2001
[84] 李颖洁,脑电信号动力学特性分析及其在精神分裂症中的应用研究,博士学位论文,上海交通大学,2001
[85] 张辉,大脑电生理信号特征信息的提取和识别新技术研究,博士学位论文,西安交通大学,2003
[86] G. Pfurtscheller, F.H. Lopes da Silva,Event-related EEG/MEG synchronization and desynchronization: basic principles,Clinical Neurophysiology, 1999,110:1842~1857
[87] Zhuang P, Toro C, Grafman J, et al. Event-related desynchronization (ERD) in the alpha frequency during development of implicit and explicit learning,Electroenceph. Clin. Neurophysiol.,1997 ,102(4):374~81
[88] Leocani L, Toro C, Manganotti P, et al. Event-related coherence and event-related desynchronization/synchronization in the 10 Hz and 20 Hz EEG during self-paced movements, Electroenceph. Clin. Neurophysiol. ,1997,104(3):199~206
[89] G Pfurtscheller, A Berghold, Patterns of cortical activation during planning of voluntary movement, Electroenceph. Clin. Neurophysiol.,1989 , 72(3): 250~258
[90] Manganotti P, Gerloff C, Toro C, et al. Task-related coherence and task-related spectral power changes during sequential finger movements, Electroenceph. Clin. Neurophysiol.,1998 ,109(1):50~62
[91] G. Pfurtscheller, Event-related synchronization(ERS): an electrophysiological correlate of cortical areas at rest, Electroenceph. Clin. Neurophysiol.,1992, 83(1):62~69
[92] Pfurtscheller G, Neuper C, Kalcher J, 40-Hz oscillations during motor behavior in man, Neuroscience Letters, 1993 ,164(1):179~182
[93] 余英林,谢胜利,蔡汉添等,信号处理新方法导论,北京:清华大学出版社,2004,255~262
[94] 张旭东,陆明泉,离散随机信号处理,北京:清华大学出版社,2005,1~400
[95] 葛哲学,陈仲生,Matlab时频分析技术及其应用,北京:人民邮电出版社,2006,1~19
[96] 刘庆云,李志舜,刘朝晖,时频分析技术及研究现状,计算机工程,2004,30(1):171~173
[97] Joshua S. Richman, J. Randall Moorman, Physiological time-series analysis using approximate entropy and sample entropy, Am J Physiol Heart Circ Physiol, 2000, 278: H2039~ H2049
[98] 刘慧,和卫星,陈晓平.生物时间序列的近似熵和样本熵方法比较.仪器仪表学报,2004;25(4):806
[99] SM Pincus, Approximate Entropy as a Measure of System Complexity, Proceedings of National Academy of Sciences, USA, 1991;2297
[100] 洪波,唐庆玉,杨福生等.近似熵、互近似熵的性质、快速算法及其在脑电与认知研究中的初步应用.信号处理, 1999;15(2):100~107
[101] 陈亚勇,Matlab信号处理详解,北京:人民邮电出版社,2001,131~153
[102] 吴小培,叶中付,基于脑电四阶累积量的运动意识分类研究. 生物物理学报,21(5):364～370
[103] 杨福生,吕扬生,生物医学信号的处理和识别(第一版),天津:天津科技翻译出版公司,1997,57~315
[104] R. Duda,P. Hart,D. Stork. Pattern Classification(second edition), New York: Wiley, 2001, 16~218
[105] Sergios Theodoridis,Konstantinos Koutroumbas,模式识别(第二版),北京:机械工业出版社,2004,13~177
[106] 边肇祺,张学工,模式识别(第二版),北京:清华大学出版社,2000,1~336
[107] K.S. Narendra, K. Parthasarathy, Identification and control of dynamical systems using neuralnetworks,IEEE Transactions on Neural Networks,1990,1(1):4~27
[108] 段勤业,杨宗凯,谈正等,模式识别与神经网络,北京:机械工业出版社,1992,113~133
[109] 张立明,人工神经网络的模型及其应用,上海:复旦大学出版社,1993,13~51
[110] 董长虹,神经网络与应用,北京:国防工业出版社,2005,14~251
[111] M.T. Hagan, M.B. Menhaj, Training feedforward networks with the Marquardt algorithm, IEEE Transactions on Neural Networks,1994,5(6):989~993
[112] K.Z. Mao, K.C. Tan, W. Ser, Probabilistic neural-network structure determination for patternclassification, IEEE Transactions on Neural Networks,2000,11(4):1009~1016
[113] R.L. Streit, T.E. Luginbuhl, Maximum likelihood training of probabilistic neural networks, IEEE Transactions on Neural Networks,1994,5(5): 764~783
[114] B A. G.ors, I. Pitas, Median radial basis function neural network, IEEE Transactions on Neural Networks,1996,7(6): 1351~1364
[115] Nello Cristianini, John Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods(李国正,王猛,曾华军译),北京:电子工业出版社,82~139
[116] 张学工,关于统计学习理论与支持向量机,自动化学报,2000,26(1):32~42
[117] D.J. Sebald, J.A. Bucklew, Support vector machine techniques for nonlinear equalization, IEEE Transactions on Signal Processing, 2000, 48(11): 3217~3226
[118] B. Scholkopf, C.J.C. Kah-Kay Sung Burges, F. Girosi, et al. Comparingsupport vector machines with Gaussian kernels to radialbasis function classifiers, IEEE Transactions on Signal Processing, 1997, 45(11): 2758~2765
[119] 邓乃扬,田英杰,数据挖掘中的新方法——支持向量机,北京:科学出版社,2004,1~378
[120] Bin He, Brain electric source imaging: scalp Laplacian mapping and cortical imaging .Critical Reviews in Biomedical Engineering, 1999, 27:149~88
[121] ZHAI Yi-ran, YAO De-zhong, A Study on the Finite Difference Approach of the Surface Laplacian. Journal of Electronic Science and Technology of China, 2006, 4(1):35~38
[122] Hjorth B.An on-line transformation of EEG scalp potentials into orthogonal source derivations, Electroencephalogr Clin Neurophysiol. 1975, 39(5) :526~530
[123] Gin-Shin Chen, Chih-Wei Chen, Ming-Shaung Ju,ect. Portable Active Surface Laplacian EEG Sensor for Real-Time Mu Rhythms Detection, Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, China: 2005,5424~5426
[124] Gang Li, Lili Wang, ect. The Feasibility Study of the Laplacian Electrode for EEG, Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, China: 2005,4670~4673
[125] Geertjan Huiskamp. Difference formulas for the surface laplacian on a triangulated surface, Journal of Computational Physics, 1991,95(2):477~496
[126] Walter G Besio, Kanthaiah Koka, Rajesh Aakula, etc. Tri-Polar Concentric Ring Electrode Development for Laplacian Electroencephalography, IEEE Transactions On Biomedical Engineering, 2006, 53(5):926~933
[127] 曹玉珍,靳世久,李刚等,体表势表面拉普拉斯的三电极传感方法实现及其实验研究,中国生物医学工程学报,2006,25(1):125~128
[128] 万百坤,李佳,明东,用于肢体运动系统的神经假体研究进展,中国医疗器械杂志,2006,30(4):235~240
[129] 袁启明,功能性电刺激装置及其临床应用前景,上海生物医学工程杂志,1997,18(1):52~54
[130] 高怀民,蓝宁,功能性电刺激在截瘫治疗中的应用,中国脊柱脊髓杂志,1995,5(6):268~270
[131] 蓝宁,肖志雄,聂开宝等,功能性电刺激的原理、设计与应用(二),中国康复理论与实践,1998,4(1):7~9
[132] 聂开宝,蓝宁,马亚权等,功能性电刺激的原理、设计与应用(三),中国康复理论与实践,1998,4(4):153~155