基于猴子M1区的腕部解码系统研究
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摘要
脑机接口是指不依赖于常规的脊髓或者外周神经肌肉系统,在脑与外部设备之间建立一种新型的信息交流与控制通道,从而实现脑与外界的直接交互。目前,非人灵长类猕猴是脑机接口研究的主要动物模型。在以猕猴为模型的脑机接口研究中,关键难点是如何长期获得高质量的神经信号和运动信号,并通过构建解码算法实现神经信号对运动信号的解码预测。本文以非人灵长类猕猴为实验对象,对初级运动皮层(primary motor cortex, M1区)神经集群信号记录和腕部精细运动信号记录等方面的若干关键技术进行了探索性研究,构建了一个基于猕猴腕部运动的植入式脑机接口系统。通过对M1区神经信号的处理和分析,实现了对腕部精细运动的解码预测。本文主要研究内容包括：
首先,本文设计了一个腕部运动信号采集系统,应用于猴子腕部内翻、外翻、前展、后屈运动时运动参数的采集。该系统包括摇杆系统、固定系统、奖赏系统、PC (personal computer)机系统、下位机微控制器系统、摄像监控系统等。针对猴子腕部精细运动的特点,本文设计了简单的四方向center-out实验范式。经过任务分解训练,猴子可以较快地学会用腕部完成摇杆行为任务。该系统成功实现了猴子腕部运动信号的采集与记录。
第二,本文对猕猴大脑皮层神经集群记录的若干关键技术进行了探索性研究。设计了两种用于猴子头部固定的机械装置。比较分析了这两种装置的性能,发现八脚headpost和球头型head holder可以较好地用于猴子头部的固定。探索了两种接口犹他电极的植入技术,比较分析了两种接口犹他电极的优缺点。设计并改进了用于ICS-96接口犹他电极的固定基座,实现了犹他电极在M1区的精确埋植。基于本文建立的微电极阵列植入技术,能够从猴子运动皮层长期获得高质量的神经集群锋电位信号(spike)。
第三,同步采集获得猴子M1区的spike信号和腕部的运动信号,并定性分析了两者之间的相关性。首先,探索了神经集群发放模式与腕部运动方向之间的关系,发现神经集群的spike发放率在摇杆运动起始前后的变化最大。神经集群发放模式在不同摇杆方向上的差异性较大,这种差异性可用于不同摇杆“方向对”间的区分。其次,通过神经可视化算法分析神经信号与运动轨迹之间的关系,发现神经信号具有很强的内在规律性。降维后所得到的神经轨迹具有明显的可区分性,能够很好地反映实际运动规律。
最后,以神经元spike发放率为输入特征,通过解码算法实现了对猴子腕部运动方向、位置、速度的精确预测。此外,本文系统分析了影响解码效果的各种因素。本文分别选用K最近邻域算法(k-nearest neighbor algorithm,KNN)和支持向量机算法(support vector machine, SVM),实现了对猴子腕部运动方向的解码预测,预测正确率可以达到96%。此外,本文选用的卡尔曼滤波算法(Kalman Filter, KF)和广义回归神经网络算法(general regression neural network, GRNN)对位置和速度等运动参数进行解码分析。两种算法均取得了较好的解码效果。其中,GRNN算法对X、Y方向位置和速度的最高解码相关系数(correlation coefficient,CC)值可以达到0.9170±0.0458,0.8872±0.0778,0.8254±0.0798和0.8376±0.0915。
综上所述,利用猴子M1区记录的神经信号可以较好地预测出腕部的运动参数。本文建立的基于猴子M1区的腕部解码系统是一个成功的脑机接口系统。该植入式脑机接口系统为进一步研究大脑运动皮层的编解码规律,以及理解大脑控制运动的神经生物学机制奠定了基础。
Brain-machine interfaces (BMIs) can provides a direct communication and control channel between the brain and external devices. Its aim is to translate neural activities of the brain into specific instructions that can be carried out by external devices. Nowadays, non-human primate model has been the main subject in BMI research, In these studies, the key point is how to get neural signals and movement signals with high quality in a long time. This paper presented exploratory research on neural ensemble recording from motor cortex and wrist movement signal recording techniques based on monkey model. And an invasive BMI system had been developed, in which neural signals from primary motor cortex could be accurately decoded into wrist movement parameters. These included:
Firstly, a wrist movement signal recording system was designed. This system could record movement parameters while monkey used his wrist to do rotary actions of supination and pronationturn or to do abduction and flexion movement. This system was consisted of several subsystems, including lever system, headpost system, water reward system, personal computer system, MCU control system and camera monitor system. A simple four direction center-out paradigm was designed for monkey wrist training. After training step by step, monkey could learn to finish this task with his wrist. And the wrist motor parameters were recorded.
Secondly, some important neural ensemble recording techniques were explored in this paper. We developed two kinds of headpost and head holder systems. The performance of the two headpost systems was compared and analyzed. It showed that the eight-leg headpost and spheric head holder were better for monkey head fixation. Two kinds of Utah arrays with different connector were used to record neural signals. Also, the characteristics of the connector were analyzed. A fixed pedestal for ICS-96connector was designed. The Utah array could be implanted in the primary motor cortex. We could record neuronal spike activities from motor cortex for a long time.
Thirdly, the neural signals from M1cortex and the wrist movement signals were recorded synchronously. The correlation between neuronal firing rates and wrist movement direction was analyzed. The results showed that the neuronal firing rates changed significantly during moving onset. And the neuronal firing mode was different among four directions. These differences were capable of discriminating direction pairs. The LE algorithm was used to visualize the variety pattern of the neural signals in a3-D space. The visualization results showed that the neural signals had regular variety patterns. And the neural trajectory could match the movement trajectory very well.
At last, the neuronal firing rates were used to decoded monkey wrist moving parameters, such as direction, position and velocity. The factors with respect to decoding were analyzed systematically. The four directions could be predicted with high accuracy using support vector machine (SVM) and k-Nearest Neighbor algorithm respectively. The four directions could be classified with96%accuracy using SVM algorithm. The wrist position and velocity were predicted by Kalman Filter and general regression neural network (GRNN) respectively. The correlation coefficients (CCs) of trajectory and velocity prediction were above0.8for both horizontal and vertical directions with GRNN algorithm. The best decoded CCs of position and velocity for horizontal and vertical directions were0.9170±0.045,0.8872±0.0778,0.8254±0.0798,0.8376±0.0915, respectively.
In a word, the neural signals recorded from M1cortex could be used to predict monkey wrist moving parameters. It was indicated that this monkey wrist decoding system was a successful BMI system. The system could also be used to research the encoding and decoding principle of the motor cortex and to understand the biological mechanism of brain control movement.
首先,本文设计了一个腕部运动信号采集系统,应用于猴子腕部内翻、外翻、前展、后屈运动时运动参数的采集。该系统包括摇杆系统、固定系统、奖赏系统、PC (personal computer)机系统、下位机微控制器系统、摄像监控系统等。针对猴子腕部精细运动的特点,本文设计了简单的四方向center-out实验范式。经过任务分解训练,猴子可以较快地学会用腕部完成摇杆行为任务。该系统成功实现了猴子腕部运动信号的采集与记录。
第二,本文对猕猴大脑皮层神经集群记录的若干关键技术进行了探索性研究。设计了两种用于猴子头部固定的机械装置。比较分析了这两种装置的性能,发现八脚headpost和球头型head holder可以较好地用于猴子头部的固定。探索了两种接口犹他电极的植入技术,比较分析了两种接口犹他电极的优缺点。设计并改进了用于ICS-96接口犹他电极的固定基座,实现了犹他电极在M1区的精确埋植。基于本文建立的微电极阵列植入技术,能够从猴子运动皮层长期获得高质量的神经集群锋电位信号(spike)。
第三,同步采集获得猴子M1区的spike信号和腕部的运动信号,并定性分析了两者之间的相关性。首先,探索了神经集群发放模式与腕部运动方向之间的关系,发现神经集群的spike发放率在摇杆运动起始前后的变化最大。神经集群发放模式在不同摇杆方向上的差异性较大,这种差异性可用于不同摇杆“方向对”间的区分。其次,通过神经可视化算法分析神经信号与运动轨迹之间的关系,发现神经信号具有很强的内在规律性。降维后所得到的神经轨迹具有明显的可区分性,能够很好地反映实际运动规律。
最后,以神经元spike发放率为输入特征,通过解码算法实现了对猴子腕部运动方向、位置、速度的精确预测。此外,本文系统分析了影响解码效果的各种因素。本文分别选用K最近邻域算法(k-nearest neighbor algorithm,KNN)和支持向量机算法(support vector machine, SVM),实现了对猴子腕部运动方向的解码预测,预测正确率可以达到96%。此外,本文选用的卡尔曼滤波算法(Kalman Filter, KF)和广义回归神经网络算法(general regression neural network, GRNN)对位置和速度等运动参数进行解码分析。两种算法均取得了较好的解码效果。其中,GRNN算法对X、Y方向位置和速度的最高解码相关系数(correlation coefficient,CC)值可以达到0.9170±0.0458,0.8872±0.0778,0.8254±0.0798和0.8376±0.0915。
综上所述,利用猴子M1区记录的神经信号可以较好地预测出腕部的运动参数。本文建立的基于猴子M1区的腕部解码系统是一个成功的脑机接口系统。该植入式脑机接口系统为进一步研究大脑运动皮层的编解码规律,以及理解大脑控制运动的神经生物学机制奠定了基础。
Brain-machine interfaces (BMIs) can provides a direct communication and control channel between the brain and external devices. Its aim is to translate neural activities of the brain into specific instructions that can be carried out by external devices. Nowadays, non-human primate model has been the main subject in BMI research, In these studies, the key point is how to get neural signals and movement signals with high quality in a long time. This paper presented exploratory research on neural ensemble recording from motor cortex and wrist movement signal recording techniques based on monkey model. And an invasive BMI system had been developed, in which neural signals from primary motor cortex could be accurately decoded into wrist movement parameters. These included:
Firstly, a wrist movement signal recording system was designed. This system could record movement parameters while monkey used his wrist to do rotary actions of supination and pronationturn or to do abduction and flexion movement. This system was consisted of several subsystems, including lever system, headpost system, water reward system, personal computer system, MCU control system and camera monitor system. A simple four direction center-out paradigm was designed for monkey wrist training. After training step by step, monkey could learn to finish this task with his wrist. And the wrist motor parameters were recorded.
Secondly, some important neural ensemble recording techniques were explored in this paper. We developed two kinds of headpost and head holder systems. The performance of the two headpost systems was compared and analyzed. It showed that the eight-leg headpost and spheric head holder were better for monkey head fixation. Two kinds of Utah arrays with different connector were used to record neural signals. Also, the characteristics of the connector were analyzed. A fixed pedestal for ICS-96connector was designed. The Utah array could be implanted in the primary motor cortex. We could record neuronal spike activities from motor cortex for a long time.
Thirdly, the neural signals from M1cortex and the wrist movement signals were recorded synchronously. The correlation between neuronal firing rates and wrist movement direction was analyzed. The results showed that the neuronal firing rates changed significantly during moving onset. And the neuronal firing mode was different among four directions. These differences were capable of discriminating direction pairs. The LE algorithm was used to visualize the variety pattern of the neural signals in a3-D space. The visualization results showed that the neural signals had regular variety patterns. And the neural trajectory could match the movement trajectory very well.
At last, the neuronal firing rates were used to decoded monkey wrist moving parameters, such as direction, position and velocity. The factors with respect to decoding were analyzed systematically. The four directions could be predicted with high accuracy using support vector machine (SVM) and k-Nearest Neighbor algorithm respectively. The four directions could be classified with96%accuracy using SVM algorithm. The wrist position and velocity were predicted by Kalman Filter and general regression neural network (GRNN) respectively. The correlation coefficients (CCs) of trajectory and velocity prediction were above0.8for both horizontal and vertical directions with GRNN algorithm. The best decoded CCs of position and velocity for horizontal and vertical directions were0.9170±0.045,0.8872±0.0778,0.8254±0.0798,0.8376±0.0915, respectively.
In a word, the neural signals recorded from M1cortex could be used to predict monkey wrist moving parameters. It was indicated that this monkey wrist decoding system was a successful BMI system. The system could also be used to research the encoding and decoding principle of the motor cortex and to understand the biological mechanism of brain control movement.
引文
[1]Shoham S, Halgren E, Maynard E M, et al. Motor-cortical activity in tetraplegics[J]. Nature. 2001,413(6858):793.
[2]Schmidt E M. Single neuron recording from motor cortex as a possible source of signals for control of external devices[J]. Ann Biomed Eng.1980,8(4-6):339-349.
[3]Hochberg L R, Bacher D, Jarosiewicz B, et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm[J]. Nature.2012,485(7398):372-375.
[4]Hatsopoulos N G, Donoghue J P. The Science of Neural Interface Systems[J]. Annual Review of Neuroscience.2009,32:249-266.
[5]Gilja V, Chestek C A, Diester I, et al. Challenges and Opportunities for Next-Generation Intracortically Based Neural Prostheses[J]. IEEE Transactions on Biomedical Engineering.2011, 58(7):1891-1899.
[6]Green A M, Kalaska J F. Learning to move machines with the mind[J]. Trends Neurosci. 2011,34(2):61-75.
[7]Lebedev M A, Nicolelis M A. Brain-machine interfaces:past, present and future[J]. Trends Neurosci.2006,29(9):536-546.
[8]Schwartz A B, Cui X T, Weber D J, et al. Brain-controlled interfaces:movement restoration with neural prosthetics[J]. Neuron.2006,52(1):205-220.
[9]Rouse A G, Stanslaski S R, Cong P, et al. A chronic generalized bi-directional brain-machine interface[J]. Journal of Neural Engineering.2011,8(0360183).
[10]Heliot R, Ganguly K, Jimenez J, et al. Learning in Closed-Loop Brain-Machine Interfaces: Modeling and Experimental Validation[J]. IEEE Transactions On Systems Man And Cybernetics Part B-Cybernetics.2010,40(5):1387-1397.
[11]Guenther F H, Brumberg J S, Wright E J, et al. A Wireless Brain-Machine Interface for Real-Time Speech Synthesis[J]. PLOS ONE.2009,4.
[12]Ethier C, Oby E R, Bauman M J, et al. Restoration of grasp following paralysis through brain-controlled stimulation of muscles[J]. Nature.2012,485(7398):368-371.
[13]Welberg L. Brain-machine interfaces:Restoring movement in a paralysed hand[J]. Nat Rev Neurosci.2012,13(6):360-361.
[14]Hema C R, Paulraj M P, Yaacob S, et al. Asynchronous brain machine interface-based control of a wheelchair[J]. Adv Exp Med Biol.2011,696:565-572.
[15]Zander T O, Kothe C. Towards passive brain-computer interfaces:applying brain-computer interface technology to human-machine systems in general[J]. Journal of Neural Engineering. 2011,8(0250052SI).
[16]Cerf M, Thiruvengadam N, Mormann F, et al. On-line, voluntary control of human temporal lobe neurons[J]. Nature.2010,467(7319):1104-1108.
[17]Ryohei, Hasegawa P. Neural mind reading for cognitive brain-machine interface[J]. Neuroscience Research.2008,61:S30.
[18]Vansteensel M J, Hermes D, Aarnoutse E J, et al. Brain-computer interfacing based on cognitive control[J]. Ann Neural.2010,67(6):809-816.
[19]Del R Milan J, Carmena J. Invasive or Noninvasive:Understanding Brain-Machine Interface Technology [J]. IEEE Engineering in Medicine and Biology Magazine.2010,29(1): 16-22.
[20]Ortiz S. Brain-computer interfaces:Where human and machine meet[J]. Computer.2007, 40(1):17-21.
[21]Contreras-Vidal J, Presacco A, Agashe H, et al. Restoration of whole body movement: toward a noninvasive brain-machine interface system[J]. IEEE Pulse.2012,3(1):34-37.
[22]Onose G, Grozea C, Anghelescu A, et al. On the feasibility of using motor imagery EEG-based brain-computer interface in chronic tetraplegics for assistive robotic arm control:a clinical test and long-term post-trial follow-up[J]. Spinal Cord.2012:1-10
[23]Perez-Marcos D, Buitrago J A, Velasquez F D. Writing through a robot:a proof of concept for a brain-machine interface[J]. Med Eng Phys.2011,33(10):1314-1317.
[24]Coffey E, Brouwer A M, Wilschut E S, et al. Brain-machine interfaces in space:Using spontaneous rather than intentionally generated brain signals[J]. Acta Astronautica.2010,67(1-2): 1-11.
[25]Slutzky M W, Jordan L R, Krieg T, et al. Optimal spacing of surface electrode arrays for brain-machine interface applications[J]. Journal of Neural Engineering.2010,7:1-9.
[26]Grosse-Wentrup M, Scholkopf B. High gamma-power predicts performance in sensorimotor-rhythm brain-computer interfaces[J]. Journal of Neural Engineering.2012,9(4):2-8.
[27]Lebedev M A, Tate A J, Hanson T L, et al. Future developments in brain-machine interface research[J]. Clinics (Sao Paulo).2011,66 Suppl 1:25-32.
[28]Chapin J K, Moxon K A, Markowitz R S, et al. Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex[J], Nat Neurosci.1999,2(7):664-670.
[29]Isaacs R E, Weber D J, Schwartz A B. Work toward real-time control of a cortical neural prothesis[J]. IEEE Trans Rehabil Eng.2000,8(2):196-198.
[30]Wessberg J, Stambaugh C R, Kralik J D, et al. Real-time prediction of hand trajectory by ensembles of cortical neurons in primates[J]. Nature.2000,408(6810):361-365.
[31]Serruya M D, Hatsopoulos N G, Paninski L, et al. Instant neural control of a movement signal[J]. Nature.2002,416(6877):141-142.
[32]Taylor D M, Tillery S 1, Schwartz A B. Direct cortical control of 3D neuroprosthetic devices[J]. Science.2002,296(5574):1829-1832.
[33]Carmena J M, Lebedev M A, Crist R E, et al. Learning to control a brain-machine interface for reaching and grasping by primates[J]. PLoS Biol.2003,1 (2):193-208.
[34]Olson B P, Si J, Hu J, et al. Closed-loop cortical control of direction using support vector machines[J]. IEEE Trans Neural Syst Rehabil Eng.2005,13(1):72-80.
[35]Velliste M, Perel S, Spalding M C, et al. Cortical control of a prosthetic arm for self-feeding[J]. Nature.2008,453(7198):1098-1101.
[36]Moritz C T, Perlmutter S I, Fetz E E. Direct control of paralysed muscles by cortical neurons[J]. Nature.2008,456(7222):639-642.
[37]O'Doherty J E, Lebedev M A, Hanson T L, et al. A brain-machine interface instructed by direct intracortical microstimulation[J]. Front Integr Neurosci.2009,3:20.
[38]O'Doherty J E, Lebedev M A, Ifft P J, et al. Active tactile exploration using a brain-machine-brain interface[J]. Nature.2011,479(7372):228-231.
[39]Lauer R T, Peckham P H, Kilgore K L, et al. Applications of cortical signals to neuroprosthetic control:a critical review[J]. IEEE Trans Rehabil Eng.2000,8(2):205-208.
[40]Kubler A, Kotchoubey B, Hinterberger T, et al. The thought translation device:a neurophysiological approach to communication in total motor paralysis [J]. Exp Brain Res.1999, 124(2):223-232.
[41]Kennedy P R, Bakay R A. Activity of single action potentials in monkey motor cortex during long-term task learning[J]. Brain Res.1997,760(1-2):251-254.
[42]Kennedy P R, Kirby M T, Moore M M, et al. Computer control using human intracortical local field potentials[J].2004,12(3):339-344.
[43]Kennedy P R, Bakay R A, Moore M M, et al. Direct control of a computer from the human central nervous system[J]. IEEE Transactions on Rehabilitation Engineering.2000,8(2):198-202.
[44]Simeral J D, Kim S P, Black M J, et al. Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array [J]. Journal of Neural Engineering.2011,8(2):1-11.
[45]Donoghue J P, Nurmikko A, Black M, et al. Assistive technology and robotic control using motor cortex ensemble-based neural interface systems in humans with tetraplegia[J]. J Physiol. 2007,579(3):603-611.
[46]Hochberg L R, Serruya M D, Friehs G M, et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia[J]. Nature.2006,442(7099):164-171.
[47]Carpaneto J, Umilta M A, Fogassi L, et al. Decoding the activity of grasping neurons recorded from the ventral premotor area F5 of the macaque monkey[J]. Neuroscience.2011,188: 80-94.
[48]Cunningham J P, Nuyujukian P, Gilja V, et al. A closed-loop human simulator for investigating the role of feedback control in brain-machine interfaces[J]. Jorunal of Neurophysiology.2011,105(4):1932-1949.
[49]Fagg A H, Ojakangas G W, Miller L E, et al. Kinetic trajectory decoding using motor cortical ensembles[J]. IEEE Trans Neural Syst Rehabil Eng.2009,17(5):487-496.
[50]Fitzsimmons N A, Lebedev M A, Peikon I D, et al. Extracting kinematic parameters for monkey bipedal walking from cortical neuronal ensemble activity[J]. Front Integr Neurosci.2009, 3:3.
[51]Pickard R S, Wall P, Ubeid M, et al. Recording neural activity in the honeybee brain with micromachined silicon sensors[J]. Sensors and Actuators B:Chemical.1990,1(1-6):460-463.
[52]Ejaz N, Peterson K D, Krapp H G. An experimental platform to study the closed-loop performance of brain-machine interfaces [J]. J Vis Exp.2011(49):1-11.
[53]Musallam S, Corneil B D, Greger B, et al. Cognitive control signals for neural prosthetics[J]. Science.2004,305(5681):258-262.
[54]Wessberg J, Nicolelis M A. Optimizing a linear algorithm for real-time robotic control using chronic cortical ensemble recordings in monkeys[J]. J Cogn Neurosci.2004,16(6):1022-1035.
[55]Shenoy K V, Meeker D, Cao S, et al. Neural prosthetic control signals from plan activity[J]. Neuroreport.2003,14(4):591-596.
[56]Taylor D M, Tillery S I, Schwartz A B. Information conveyed through brain-control:cursor versus robot[J]. IEEE Trans Neural Syst Rehabil Eng.2003,11(2):195-199.
[57]Pesaran B, Pezaris J S, Sahani M, et al. Temporal structure in neuronal activity during working memory in macaque parietal cortex[J]. Nat Neurosci.2002,5(8):805-811.
[58]Chestek C A, Batista A P, Santhanam G, et al. Single-neuron stability during repeated reaching in macaque premotor cortex[J]. J Neurosci.2007,27(40):10742-10750.
[59]Stark E, Abeles M. Predicting movement from multiunit activity[J]. J Neurosci.2007, 27(31):8387-8394.
[60]Wahnoun R, He J, Helms T S. Selection and parameterization of cortical neurons for neuroprosthetic control[J]. Journal of Neural Engineering.2006,3(2):162-171.
[61]Lebedev M A, Carmena J M, O'Doherty J E, et al. Cortical ensemble adaptation to represent velocity of an artificial actuator controlled by a brain-machine interface[J]. J Neurosci.2005, 25(19):4681-4693.
[62]Hatsopoulos N, Joshi J, O'Leary J G. Decoding continuous and discrete motor behaviors using motor and premotor cortical ensembles[J]. J Neurophysiol.2004,92(2):1165-1174.
[63]David S, Paul N, Joline M F, et al. A recurrent neural network for closed-loop intracortical brain-machine interface decoders[J]. Journal of Neural Engineering.2012,9(2):1-10.
[64]Vargas-Irwin C E, Shakhnarovich G, Yadollahpour P, et al. Decoding complete reach and grasp actions from local primary motor cortex populations[J]. J Neurosci.2010,30(29): 9659-9669.
[65]Hendrix C M, Mason C R, Ebner T J. Signaling of grasp dimension and grasp force in dorsal premotor cortex and primary motor cortex neurons during reach to grasp in the monkey [J]. J Neurophysiol.2009,102(1):132-145.
[66]Pohlmeyer E A, Oby E R, Perreault E J, et al. Toward the restoration of hand use to a paralyzed monkey:brain-controlled functional electrical stimulation of forearm muscles[J]. PLoS One.2009,4(6):e5924.
[67]Aggarwal V, Acharya S, Tenore F, et al. Asynchronous decoding of dexterous finger movements using M1 neurons[J]. IEEE Trans Neural Syst Rehabil Eng.2008,16(1):3-14.
[68]Lebedev M A, O'Doherty J E. Nicolelis M A. Decoding of temporal intervals from cortical ensemble activity[J]. J Neurophysiol.2008,99(1):166-186.
[69]Mulliken G H, Musallam S. Andersen R A. Decoding trajectories from posterior parietal cortex ensembles[J]. J Neurosci.2008,28(48):12913-12926.
[70]Nuyujukian P, Fan J M, Gilja V. et al. Monkey models for brain-machine interfaces:the need for maintaining diversity[J]. Conf Proc IEEE Eng Med Biol Soc.2011,2011:1301-1305.
[71]Van Essen D C, Lewis J W, Drury H A. et al. Mapping visual cortex in monkeys and humans using surface-based atlases[J]. Vision Res.2001,41(10-11):1359-1378.
[72]Walsh G P, Tan E V, Dela C E, et al. The Philippine cynomolgus monkey (Macaca fasicularis) provides a new nonhuman primate model of tuberculosis that resembles human disease[J]. Nat Med.1996,2(4):430-436.
[73]Orban G A, Van Essen D, Vanduffel W. Comparative mapping of higher visual areas in monkeys and humans[J]. Trends in Cognitive Science.2004,8(7):315-324.
[74]Crist R E, Li W, Gilbert C D. Learning to see:experience and attention in primary visual cortex[J]. Nat Neurosci.2001,4(5):519-525.
[75]Sperling H G, Harwerth R S. Red-green cone interactions in the increment-threshold spectral sensitivity of primates[J]. Science.1971,172(3979):180-184.
[76]Loop M S, Crossman D K. High color-vision sensitivity in macaque and humans[J]. Vis Neurosci.2000,17(1):119-125.
[77]Ogihara N, Usui H, Hirasaki E, et al. Kinematic analysis of bipedal locomotion of a Japanese macaque that lost its forearms due to congenital malformation[J]. Primates.2005,46(1): 11-19.
[78]Nakatsukasa M. Hirasaki E, Ogihara N. Energy expenditure of bipedal walking is higher than that of quadrupedal walking in Japanese macaques[J]. American Journal of Physical Anthropology.2006,131(1):33-37.
[79]Keefer E W, Botterman B R, Romero M I, et al. Carbon nanotube coating improves neuronal recordings[J]. Nature Nanotechnology.2008,3(7):434-439.
[80]Jackson A, Fetz E E. Compact movable microwire array for long-term chronic unit recording in cerebral cortex of primates[J]. J Neurophysiol.2007,98(5):3109-3118.
[81]Nicolelis M A, Dimitrov D, Carmena J M, et al. Chronic, multisite, multielectrode recordings in macaque monkeys[J]. Proc Natl Acad Sci U S A.2003,100(19):11041-11046.
[82]Williams J C, Rennaker R L, Kipke D R. Long-term neural recording characteristics of wire microelectrode arrays implanted in cerebral cortex[J]. Brain Research Protocols.1999,4(3): 303-313.
[83]Maynard E M, Nordhausen C T, Normann R A. The Utah intracortical Electrode Array:a recording structure for potential brain-computer interfaces[J]. Electroencephalogr Clin Neurophysiol.1997,102(3):228-239.
[84]Wang Y, Paiva A R C, Principe J C, et al. Sequential Monte Carlo Point-Process Estimation of Kinematics from Neural Spiking Activity for Brain-Machine Interfaces[J]. Neural Computation. 2009,21(10):2894-2930.
[85]张韶岷,陈卫东,孙超,等.动物脑电-行为同步记录及分析系统[J].浙江大学学报(工学版).2009,43(11)：2028-2033.
[86]Johnson J L, Welsh J P. Independently movable multielectrode array to record multiple fast-spiking neurons in the cerebral cortex during cognition[J]. Methods.2003,30(1):64-78.
[87]Chestek C A, Gilja V, Nuyujukian P, et al. Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex[J]. Jouranl of Neural Engineering.2011,8:1-11.
[88]Schaffelhofer S, Scherberger H. A new method of accurate hand-and arm-tracking for small primates[J]. Jouranl of Neural Engineering.2012,9(2):1-13.
[89]Peikon I D, Fitzsimmons N A, Lebedev M A, et al. Three-dimensional, automated, real-time video system for tracking limb motion in brain-machine interface studies[J]. J Neurosci Methods. 2009,180(2):224-233.
[90]Georgopoulos A P, Kalaska J F, Caminiti R, et al. On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex[J]. J Neurosci. 1982,2(11):1527-1537.
[91]Hatsopoulos N, Joshi J, O'Leary J G. Decoding continuous and discrete motor behaviors using motor and premotor cortical ensembles[J]. J Neurophysiol.2004,92(2):1165-1174.
[92]Mulick J A, Leitenberg H, Rawson R A. Alternative response training, differential reinforcement of other behavior, and extinction in squirrel monkeys (Saimiri sciureus)[J]. J Exp Anal Behav.1976,25(3):311-320.
[93]Nathan M A, Smith O J. Differential conditional emotional and cardiovascular responses-a training technique for monkeys[J]. J Exp Anal Behav.1968,11(1):77-82.
[94]Plautz E J, Milliken G W, Nudo R J. Effects of repetitive motor training on movement representations in adult squirrel monkeys:role of use versus learning[J]. Neurobiol Learn Mem. 2000,74(1):27-55.
[95]Koralek A C, Jin X, Long Ii J D, et al. Corticostriatal plasticity is necessary for learning intentional neuroprosthetic skills[J]. Nature.2012,483(7389):331-335.
[96]Bichot N P, Heard M T, Desimone R. Stimulation of the nucleus accumbens as behavioral reward in awake behaving monkeys[J]. J Neurosci Methods.2011,199(2):265-272.
[97]Barrow S, Luschei E, Nathan M, et al. A training technique for the daily chairing of monkeys[J]. J Exp Anal Behav.1966,9(6):680.
[98]Li Z, O'Doherty J E, Hanson T L, et al. Unscented Kalman filter for brain-machine interfaces[J]. PLoS One.2009,4(7):1-18.
[99]Suner S, Fellows M R, Vargas-Irwin C, et al. Reliability of signals from a chronically implanted, silicon-based electrode array in non-human primate primary motor cortex[J]. IEEE Trans Neural Syst Rehabil Eng.2005,13(4):524-541.
[100]Adams D L, Economides J R, Jocson C M, et al. A biocompatible titanium headpost for stabilizing behaving monkeys[J]. J Neurophysiol.2007,98(2):993-1001.
[101]Sanes J N, Donoghue J P. Plasticity and Primary Motor Cortex [J]. Annual Review of Neuroscience.2000,23(1):393-415.
[102]Nudo R J, Milliken G W, Jenkins W M, et al. Use-dependent alterations of movement representations in primary motor cortex of adult squirrel monkeys[J]. J Neurosci.1996,16(2): 785-807.
[103]Paxinos G, Huang X, Toga A W. The Rhesus monkey brain in stereotaxic coordinates[M]. New York:Academic Press,1999.
[104]Rousche P J, Normann R A. Chronic recording capability of the Utah Intracortical Electrode Array in cat sensory cortex [J]. J Neurosci Methods.1998,82(1):1-15.
[105]Nordhausen C T, Maynard E M, Normann R A. Single unit recording capabilities of a 100 microelectrode array[J].1996,726(1-2):129-140.
[106]Chhatbar P Y, von Kraus L M, Semework M, et al. A bio-friendly and economical technique for chronic implantation of multiple microelectrode arrays[J], J Neurosci Methods.2010,188(2): 187-194.
[107]Eriksson A R, Albrektsson T. Temperature threshold levels for heat-induced bone tissue injury:a vital-microscopic study in the rabbit[J]. J Prosthet Dent.1983,50(1):101-107.
[108]Musallam S, Bak M J. Troyk P R. et al. A floating metal microelectrode array for chronic implantation[J]. J Neurosci Methods.2007,160(1):122-127.
[109]Polikov V S, Tresco P A, Reichert W M. Response of brain tissue to chronically implanted neural electrodes[J].2005,148(1):1-18.
[110]Brown E N, Frank L M, Tang D, et al. A Statistical Paradigm for Neural Spike Train Decoding Applied to Position Prediction from Ensemble Firing Patterns of Rat Hippocampal Place Cells [J]. The Journal of Neuroscience.1998,18(18):7411-7425.
[111]Yu Y, Zhang S, Zhang H, et al. Neural decoding based on probabilistic neural network[J]. Journal of Zhejiang University-Science B.2010,11(4):298-306.
[112]Amirikian B, Georgopoulos A P. Directional tuning profiles of motor cortical cells[J]. Neurosci Res.2000,36(1):73-79.
[113]Georgopoulos A P, Schwartz A B, Kettner R E. Neuronal population coding of movement direction[J]. Science.1986,233(4771):1416-1419.
[114]Roweis S T, Saul L K. Nonlinear dimensionality reduction by locally linear embedding[J]. Science.2000,290(5500):2323-2326.
[115]Belkin M, Niyogi P. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation [J]. Neural Computation.2003,15(6):1373-1396.
[116]David S A P N. A recurrent neural network for closed-loop intracortical brain-machine interface decoders[J]. Journal of Neural Engineering.2012,9(2):1-10.
[117]Bansal A K, Truccolo W, Vargas-Irwin C E, et al. Decoding 3D reach and grasp from hybrid signals in motor and premotor cortices:spikes, multiunit activity, and local field potentials [J]. Journal of Neurophysiology.2012,107(5):1337-1355.
[118]Taylor D M, Tillery S I H, Schwartz A B. Direct Cortical Control of 3D Neuroprosthetic Devices [J]. Science.2002,296(5574):1829-1832.
[119]Kuncheva L I. Editing for the k-nearest neighbors rule by a genetic algorithm [J]. Pattern Recognition Letters.1995,16(8):809-814.
[120]Keller J M, Gray M R, Givens J A. A Fuzzy K-nearest Neighbor Algorithm[J]. IEEE Transactions on Systems Man and Cybernetics.1985,15(4):580-585.
[121]Cover T, Hart P. Nearest neighbor pattern classification[J]. IEEE Transactions on Information Theory.1967,13(1):21-27.
[122]Chapelle O, Haffner P, Vapnik V N. Support vector machines for histogram-based image classification[J]. IEEE Trans Neural Netw.1999,10(5):1055-1064.
[123]Drucker H, Wu D, Vapnik V N. Support vector machines for spam categorization[J]. IEEE Trans Neural Netw.1999,10(5):1048-1054.
[124]Burges C J C. A Tutorial on Support Vector Machines for Pattern Recognition[J]. Data Mining and Knowledge Discovery.1998,2(2):121-167.
[125]Scholkopf B, Kah-Kay S, Burges C J C, et al. Comparing support vector machines with Gaussian kernels to radial basis function classifiers[J]. IEEE Transactions on Signal Processing. 1997,45(11):2758-2765.
[126]Sain S R. The Nature of Statistical Learning Theory[J]. Technometrics.1996,38(4):409.
[127]Jrad N, Congedo M, Phlypo R, et al. sw-SVM:sensor weighting support vector machines for EEG-based brain-computer interfaces [J]. Journal of Neural Engineering.2011,8(5):1-11.
[128]Gaston F M F, Irwin G W. Systolic Kalman filtering:an overview[J]. Control Theory and Applications, IEE Proceedings D.1990,137(4):235-244.
[129]Malik W Q, Truccolo W, Brown E N, et al. Efficient Decoding With Steady-State Kalman Filter in Neural Interface Systems [J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering.2011,19(1):25-34.
[130]Srinivasan L, Eden U T, Mitter S K, et al. General-purpose filter design for neural prosthetic devices[J]. Jouranl of Neurophysiology.2007,98(4):2456-2475.
[131]Wu W, Hatsopoulos N G. Real-time decoding of nonstationary neural activity in motor cortex[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering.2008,16(3): 213-222.
[132]Specht D F. A general regression neural network[J]. IEEE Transactions on Neural Networks. 1991,2(6):568-576.
[133]Vidaurre C, Sannelli C, Muller K R, et al. Co-adaptive calibration to improve BCI efficiency[J]. Journal of Neural Engineering.2011,8:1-8.
[134]Dangi S, Gowda S, Heliot R, et al. Adaptive Kalman Filtering for Closed-Loop Brain-Machine Interface Systems[C].2011 5th International IEEE/EMBS Conference on Neural Engineering, NEW YORK:IEEE,2011,609-612.
[135]Li Z, O'Doherty J E, Lebedev M A, et al. Adaptive Decoding for Brain-Machine Interfaces Through Bayesian Parameter Updates[J]. Neural Computation.2011,23(12):3162-3204.
[136]Vidaurre C, Sannelli C, Muller K R, et al. Machine-Learning-Based Coadaptive Calibration for Brain-Computer Interfaces[J]. Neural Computation.2010,23:791-816.
[137]Digiovanna J, Mahmoudi B, Fortes J, et al. Coadaptive Brain-Machine Interface via Reinforcement Learning[J]. IEEE Transactions on Biomedical Engineering.2009,56(1):54-64.
[138]Gage G J, Ludwig K A, Otto K J, et al. Naive coadaptive cortical control[J]. J Neural Eng. 2005,2(2):52-63.
[139]Miller L E, Weber D J. Brain Training:Cortical Plasticity and Afferent Feedback in Brain-Machine Interface Systems[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering.2011,19(5):465-467.
[140]Madisen L, Mao T, Koch H, et al. A toolbox of Cre-dependent optogenetic transgenic mice for light-induced activation and silencing[J]. Nature Neuroscience.2012,15(5):793-802.
[141]Liu X, Ramirez S, Pang P T, et al. Optogenetic stimulation of a hippocampal engram activates fear memory recall[J]. Nature.2012,484(7394):381-385.
[142]Tye K M, Deisseroth K. Optogenetic investigation of neural circuits underlying brain disease in animal models[J]. Nat Rev Neurosci.2012,13(4):251-266.
[143]Suminski A J, Tkach D C, Fagg A H, et al. Incorporating Feedback from Multiple Sensory Modalities Enhances Brain-Machine Interface Control[J]. Journal of Neuroscience.2010.30(50): 16777-16787.
[144]Zhang S M, Jiang B, Zhu J M, et al. A Study on Combining Local Field Potential and Single Unit Activity for Better Neural Decoding[J]. International Journal of Imaging Systems and Technology.2011,21(2SI):165-172.
[2]Schmidt E M. Single neuron recording from motor cortex as a possible source of signals for control of external devices[J]. Ann Biomed Eng.1980,8(4-6):339-349.
[3]Hochberg L R, Bacher D, Jarosiewicz B, et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm[J]. Nature.2012,485(7398):372-375.
[4]Hatsopoulos N G, Donoghue J P. The Science of Neural Interface Systems[J]. Annual Review of Neuroscience.2009,32:249-266.
[5]Gilja V, Chestek C A, Diester I, et al. Challenges and Opportunities for Next-Generation Intracortically Based Neural Prostheses[J]. IEEE Transactions on Biomedical Engineering.2011, 58(7):1891-1899.
[6]Green A M, Kalaska J F. Learning to move machines with the mind[J]. Trends Neurosci. 2011,34(2):61-75.
[7]Lebedev M A, Nicolelis M A. Brain-machine interfaces:past, present and future[J]. Trends Neurosci.2006,29(9):536-546.
[8]Schwartz A B, Cui X T, Weber D J, et al. Brain-controlled interfaces:movement restoration with neural prosthetics[J]. Neuron.2006,52(1):205-220.
[9]Rouse A G, Stanslaski S R, Cong P, et al. A chronic generalized bi-directional brain-machine interface[J]. Journal of Neural Engineering.2011,8(0360183).
[10]Heliot R, Ganguly K, Jimenez J, et al. Learning in Closed-Loop Brain-Machine Interfaces: Modeling and Experimental Validation[J]. IEEE Transactions On Systems Man And Cybernetics Part B-Cybernetics.2010,40(5):1387-1397.
[11]Guenther F H, Brumberg J S, Wright E J, et al. A Wireless Brain-Machine Interface for Real-Time Speech Synthesis[J]. PLOS ONE.2009,4.
[12]Ethier C, Oby E R, Bauman M J, et al. Restoration of grasp following paralysis through brain-controlled stimulation of muscles[J]. Nature.2012,485(7398):368-371.
[13]Welberg L. Brain-machine interfaces:Restoring movement in a paralysed hand[J]. Nat Rev Neurosci.2012,13(6):360-361.
[14]Hema C R, Paulraj M P, Yaacob S, et al. Asynchronous brain machine interface-based control of a wheelchair[J]. Adv Exp Med Biol.2011,696:565-572.
[15]Zander T O, Kothe C. Towards passive brain-computer interfaces:applying brain-computer interface technology to human-machine systems in general[J]. Journal of Neural Engineering. 2011,8(0250052SI).
[16]Cerf M, Thiruvengadam N, Mormann F, et al. On-line, voluntary control of human temporal lobe neurons[J]. Nature.2010,467(7319):1104-1108.
[17]Ryohei, Hasegawa P. Neural mind reading for cognitive brain-machine interface[J]. Neuroscience Research.2008,61:S30.
[18]Vansteensel M J, Hermes D, Aarnoutse E J, et al. Brain-computer interfacing based on cognitive control[J]. Ann Neural.2010,67(6):809-816.
[19]Del R Milan J, Carmena J. Invasive or Noninvasive:Understanding Brain-Machine Interface Technology [J]. IEEE Engineering in Medicine and Biology Magazine.2010,29(1): 16-22.
[20]Ortiz S. Brain-computer interfaces:Where human and machine meet[J]. Computer.2007, 40(1):17-21.
[21]Contreras-Vidal J, Presacco A, Agashe H, et al. Restoration of whole body movement: toward a noninvasive brain-machine interface system[J]. IEEE Pulse.2012,3(1):34-37.
[22]Onose G, Grozea C, Anghelescu A, et al. On the feasibility of using motor imagery EEG-based brain-computer interface in chronic tetraplegics for assistive robotic arm control:a clinical test and long-term post-trial follow-up[J]. Spinal Cord.2012:1-10
[23]Perez-Marcos D, Buitrago J A, Velasquez F D. Writing through a robot:a proof of concept for a brain-machine interface[J]. Med Eng Phys.2011,33(10):1314-1317.
[24]Coffey E, Brouwer A M, Wilschut E S, et al. Brain-machine interfaces in space:Using spontaneous rather than intentionally generated brain signals[J]. Acta Astronautica.2010,67(1-2): 1-11.
[25]Slutzky M W, Jordan L R, Krieg T, et al. Optimal spacing of surface electrode arrays for brain-machine interface applications[J]. Journal of Neural Engineering.2010,7:1-9.
[26]Grosse-Wentrup M, Scholkopf B. High gamma-power predicts performance in sensorimotor-rhythm brain-computer interfaces[J]. Journal of Neural Engineering.2012,9(4):2-8.
[27]Lebedev M A, Tate A J, Hanson T L, et al. Future developments in brain-machine interface research[J]. Clinics (Sao Paulo).2011,66 Suppl 1:25-32.
[28]Chapin J K, Moxon K A, Markowitz R S, et al. Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex[J], Nat Neurosci.1999,2(7):664-670.
[29]Isaacs R E, Weber D J, Schwartz A B. Work toward real-time control of a cortical neural prothesis[J]. IEEE Trans Rehabil Eng.2000,8(2):196-198.
[30]Wessberg J, Stambaugh C R, Kralik J D, et al. Real-time prediction of hand trajectory by ensembles of cortical neurons in primates[J]. Nature.2000,408(6810):361-365.
[31]Serruya M D, Hatsopoulos N G, Paninski L, et al. Instant neural control of a movement signal[J]. Nature.2002,416(6877):141-142.
[32]Taylor D M, Tillery S 1, Schwartz A B. Direct cortical control of 3D neuroprosthetic devices[J]. Science.2002,296(5574):1829-1832.
[33]Carmena J M, Lebedev M A, Crist R E, et al. Learning to control a brain-machine interface for reaching and grasping by primates[J]. PLoS Biol.2003,1 (2):193-208.
[34]Olson B P, Si J, Hu J, et al. Closed-loop cortical control of direction using support vector machines[J]. IEEE Trans Neural Syst Rehabil Eng.2005,13(1):72-80.
[35]Velliste M, Perel S, Spalding M C, et al. Cortical control of a prosthetic arm for self-feeding[J]. Nature.2008,453(7198):1098-1101.
[36]Moritz C T, Perlmutter S I, Fetz E E. Direct control of paralysed muscles by cortical neurons[J]. Nature.2008,456(7222):639-642.
[37]O'Doherty J E, Lebedev M A, Hanson T L, et al. A brain-machine interface instructed by direct intracortical microstimulation[J]. Front Integr Neurosci.2009,3:20.
[38]O'Doherty J E, Lebedev M A, Ifft P J, et al. Active tactile exploration using a brain-machine-brain interface[J]. Nature.2011,479(7372):228-231.
[39]Lauer R T, Peckham P H, Kilgore K L, et al. Applications of cortical signals to neuroprosthetic control:a critical review[J]. IEEE Trans Rehabil Eng.2000,8(2):205-208.
[40]Kubler A, Kotchoubey B, Hinterberger T, et al. The thought translation device:a neurophysiological approach to communication in total motor paralysis [J]. Exp Brain Res.1999, 124(2):223-232.
[41]Kennedy P R, Bakay R A. Activity of single action potentials in monkey motor cortex during long-term task learning[J]. Brain Res.1997,760(1-2):251-254.
[42]Kennedy P R, Kirby M T, Moore M M, et al. Computer control using human intracortical local field potentials[J].2004,12(3):339-344.
[43]Kennedy P R, Bakay R A, Moore M M, et al. Direct control of a computer from the human central nervous system[J]. IEEE Transactions on Rehabilitation Engineering.2000,8(2):198-202.
[44]Simeral J D, Kim S P, Black M J, et al. Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array [J]. Journal of Neural Engineering.2011,8(2):1-11.
[45]Donoghue J P, Nurmikko A, Black M, et al. Assistive technology and robotic control using motor cortex ensemble-based neural interface systems in humans with tetraplegia[J]. J Physiol. 2007,579(3):603-611.
[46]Hochberg L R, Serruya M D, Friehs G M, et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia[J]. Nature.2006,442(7099):164-171.
[47]Carpaneto J, Umilta M A, Fogassi L, et al. Decoding the activity of grasping neurons recorded from the ventral premotor area F5 of the macaque monkey[J]. Neuroscience.2011,188: 80-94.
[48]Cunningham J P, Nuyujukian P, Gilja V, et al. A closed-loop human simulator for investigating the role of feedback control in brain-machine interfaces[J]. Jorunal of Neurophysiology.2011,105(4):1932-1949.
[49]Fagg A H, Ojakangas G W, Miller L E, et al. Kinetic trajectory decoding using motor cortical ensembles[J]. IEEE Trans Neural Syst Rehabil Eng.2009,17(5):487-496.
[50]Fitzsimmons N A, Lebedev M A, Peikon I D, et al. Extracting kinematic parameters for monkey bipedal walking from cortical neuronal ensemble activity[J]. Front Integr Neurosci.2009, 3:3.
[51]Pickard R S, Wall P, Ubeid M, et al. Recording neural activity in the honeybee brain with micromachined silicon sensors[J]. Sensors and Actuators B:Chemical.1990,1(1-6):460-463.
[52]Ejaz N, Peterson K D, Krapp H G. An experimental platform to study the closed-loop performance of brain-machine interfaces [J]. J Vis Exp.2011(49):1-11.
[53]Musallam S, Corneil B D, Greger B, et al. Cognitive control signals for neural prosthetics[J]. Science.2004,305(5681):258-262.
[54]Wessberg J, Nicolelis M A. Optimizing a linear algorithm for real-time robotic control using chronic cortical ensemble recordings in monkeys[J]. J Cogn Neurosci.2004,16(6):1022-1035.
[55]Shenoy K V, Meeker D, Cao S, et al. Neural prosthetic control signals from plan activity[J]. Neuroreport.2003,14(4):591-596.
[56]Taylor D M, Tillery S I, Schwartz A B. Information conveyed through brain-control:cursor versus robot[J]. IEEE Trans Neural Syst Rehabil Eng.2003,11(2):195-199.
[57]Pesaran B, Pezaris J S, Sahani M, et al. Temporal structure in neuronal activity during working memory in macaque parietal cortex[J]. Nat Neurosci.2002,5(8):805-811.
[58]Chestek C A, Batista A P, Santhanam G, et al. Single-neuron stability during repeated reaching in macaque premotor cortex[J]. J Neurosci.2007,27(40):10742-10750.
[59]Stark E, Abeles M. Predicting movement from multiunit activity[J]. J Neurosci.2007, 27(31):8387-8394.
[60]Wahnoun R, He J, Helms T S. Selection and parameterization of cortical neurons for neuroprosthetic control[J]. Journal of Neural Engineering.2006,3(2):162-171.
[61]Lebedev M A, Carmena J M, O'Doherty J E, et al. Cortical ensemble adaptation to represent velocity of an artificial actuator controlled by a brain-machine interface[J]. J Neurosci.2005, 25(19):4681-4693.
[62]Hatsopoulos N, Joshi J, O'Leary J G. Decoding continuous and discrete motor behaviors using motor and premotor cortical ensembles[J]. J Neurophysiol.2004,92(2):1165-1174.
[63]David S, Paul N, Joline M F, et al. A recurrent neural network for closed-loop intracortical brain-machine interface decoders[J]. Journal of Neural Engineering.2012,9(2):1-10.
[64]Vargas-Irwin C E, Shakhnarovich G, Yadollahpour P, et al. Decoding complete reach and grasp actions from local primary motor cortex populations[J]. J Neurosci.2010,30(29): 9659-9669.
[65]Hendrix C M, Mason C R, Ebner T J. Signaling of grasp dimension and grasp force in dorsal premotor cortex and primary motor cortex neurons during reach to grasp in the monkey [J]. J Neurophysiol.2009,102(1):132-145.
[66]Pohlmeyer E A, Oby E R, Perreault E J, et al. Toward the restoration of hand use to a paralyzed monkey:brain-controlled functional electrical stimulation of forearm muscles[J]. PLoS One.2009,4(6):e5924.
[67]Aggarwal V, Acharya S, Tenore F, et al. Asynchronous decoding of dexterous finger movements using M1 neurons[J]. IEEE Trans Neural Syst Rehabil Eng.2008,16(1):3-14.
[68]Lebedev M A, O'Doherty J E. Nicolelis M A. Decoding of temporal intervals from cortical ensemble activity[J]. J Neurophysiol.2008,99(1):166-186.
[69]Mulliken G H, Musallam S. Andersen R A. Decoding trajectories from posterior parietal cortex ensembles[J]. J Neurosci.2008,28(48):12913-12926.
[70]Nuyujukian P, Fan J M, Gilja V. et al. Monkey models for brain-machine interfaces:the need for maintaining diversity[J]. Conf Proc IEEE Eng Med Biol Soc.2011,2011:1301-1305.
[71]Van Essen D C, Lewis J W, Drury H A. et al. Mapping visual cortex in monkeys and humans using surface-based atlases[J]. Vision Res.2001,41(10-11):1359-1378.
[72]Walsh G P, Tan E V, Dela C E, et al. The Philippine cynomolgus monkey (Macaca fasicularis) provides a new nonhuman primate model of tuberculosis that resembles human disease[J]. Nat Med.1996,2(4):430-436.
[73]Orban G A, Van Essen D, Vanduffel W. Comparative mapping of higher visual areas in monkeys and humans[J]. Trends in Cognitive Science.2004,8(7):315-324.
[74]Crist R E, Li W, Gilbert C D. Learning to see:experience and attention in primary visual cortex[J]. Nat Neurosci.2001,4(5):519-525.
[75]Sperling H G, Harwerth R S. Red-green cone interactions in the increment-threshold spectral sensitivity of primates[J]. Science.1971,172(3979):180-184.
[76]Loop M S, Crossman D K. High color-vision sensitivity in macaque and humans[J]. Vis Neurosci.2000,17(1):119-125.
[77]Ogihara N, Usui H, Hirasaki E, et al. Kinematic analysis of bipedal locomotion of a Japanese macaque that lost its forearms due to congenital malformation[J]. Primates.2005,46(1): 11-19.
[78]Nakatsukasa M. Hirasaki E, Ogihara N. Energy expenditure of bipedal walking is higher than that of quadrupedal walking in Japanese macaques[J]. American Journal of Physical Anthropology.2006,131(1):33-37.
[79]Keefer E W, Botterman B R, Romero M I, et al. Carbon nanotube coating improves neuronal recordings[J]. Nature Nanotechnology.2008,3(7):434-439.
[80]Jackson A, Fetz E E. Compact movable microwire array for long-term chronic unit recording in cerebral cortex of primates[J]. J Neurophysiol.2007,98(5):3109-3118.
[81]Nicolelis M A, Dimitrov D, Carmena J M, et al. Chronic, multisite, multielectrode recordings in macaque monkeys[J]. Proc Natl Acad Sci U S A.2003,100(19):11041-11046.
[82]Williams J C, Rennaker R L, Kipke D R. Long-term neural recording characteristics of wire microelectrode arrays implanted in cerebral cortex[J]. Brain Research Protocols.1999,4(3): 303-313.
[83]Maynard E M, Nordhausen C T, Normann R A. The Utah intracortical Electrode Array:a recording structure for potential brain-computer interfaces[J]. Electroencephalogr Clin Neurophysiol.1997,102(3):228-239.
[84]Wang Y, Paiva A R C, Principe J C, et al. Sequential Monte Carlo Point-Process Estimation of Kinematics from Neural Spiking Activity for Brain-Machine Interfaces[J]. Neural Computation. 2009,21(10):2894-2930.
[85]张韶岷,陈卫东,孙超,等.动物脑电-行为同步记录及分析系统[J].浙江大学学报(工学版).2009,43(11)：2028-2033.
[86]Johnson J L, Welsh J P. Independently movable multielectrode array to record multiple fast-spiking neurons in the cerebral cortex during cognition[J]. Methods.2003,30(1):64-78.
[87]Chestek C A, Gilja V, Nuyujukian P, et al. Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex[J]. Jouranl of Neural Engineering.2011,8:1-11.
[88]Schaffelhofer S, Scherberger H. A new method of accurate hand-and arm-tracking for small primates[J]. Jouranl of Neural Engineering.2012,9(2):1-13.
[89]Peikon I D, Fitzsimmons N A, Lebedev M A, et al. Three-dimensional, automated, real-time video system for tracking limb motion in brain-machine interface studies[J]. J Neurosci Methods. 2009,180(2):224-233.
[90]Georgopoulos A P, Kalaska J F, Caminiti R, et al. On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex[J]. J Neurosci. 1982,2(11):1527-1537.
[91]Hatsopoulos N, Joshi J, O'Leary J G. Decoding continuous and discrete motor behaviors using motor and premotor cortical ensembles[J]. J Neurophysiol.2004,92(2):1165-1174.
[92]Mulick J A, Leitenberg H, Rawson R A. Alternative response training, differential reinforcement of other behavior, and extinction in squirrel monkeys (Saimiri sciureus)[J]. J Exp Anal Behav.1976,25(3):311-320.
[93]Nathan M A, Smith O J. Differential conditional emotional and cardiovascular responses-a training technique for monkeys[J]. J Exp Anal Behav.1968,11(1):77-82.
[94]Plautz E J, Milliken G W, Nudo R J. Effects of repetitive motor training on movement representations in adult squirrel monkeys:role of use versus learning[J]. Neurobiol Learn Mem. 2000,74(1):27-55.
[95]Koralek A C, Jin X, Long Ii J D, et al. Corticostriatal plasticity is necessary for learning intentional neuroprosthetic skills[J]. Nature.2012,483(7389):331-335.
[96]Bichot N P, Heard M T, Desimone R. Stimulation of the nucleus accumbens as behavioral reward in awake behaving monkeys[J]. J Neurosci Methods.2011,199(2):265-272.
[97]Barrow S, Luschei E, Nathan M, et al. A training technique for the daily chairing of monkeys[J]. J Exp Anal Behav.1966,9(6):680.
[98]Li Z, O'Doherty J E, Hanson T L, et al. Unscented Kalman filter for brain-machine interfaces[J]. PLoS One.2009,4(7):1-18.
[99]Suner S, Fellows M R, Vargas-Irwin C, et al. Reliability of signals from a chronically implanted, silicon-based electrode array in non-human primate primary motor cortex[J]. IEEE Trans Neural Syst Rehabil Eng.2005,13(4):524-541.
[100]Adams D L, Economides J R, Jocson C M, et al. A biocompatible titanium headpost for stabilizing behaving monkeys[J]. J Neurophysiol.2007,98(2):993-1001.
[101]Sanes J N, Donoghue J P. Plasticity and Primary Motor Cortex [J]. Annual Review of Neuroscience.2000,23(1):393-415.
[102]Nudo R J, Milliken G W, Jenkins W M, et al. Use-dependent alterations of movement representations in primary motor cortex of adult squirrel monkeys[J]. J Neurosci.1996,16(2): 785-807.
[103]Paxinos G, Huang X, Toga A W. The Rhesus monkey brain in stereotaxic coordinates[M]. New York:Academic Press,1999.
[104]Rousche P J, Normann R A. Chronic recording capability of the Utah Intracortical Electrode Array in cat sensory cortex [J]. J Neurosci Methods.1998,82(1):1-15.
[105]Nordhausen C T, Maynard E M, Normann R A. Single unit recording capabilities of a 100 microelectrode array[J].1996,726(1-2):129-140.
[106]Chhatbar P Y, von Kraus L M, Semework M, et al. A bio-friendly and economical technique for chronic implantation of multiple microelectrode arrays[J], J Neurosci Methods.2010,188(2): 187-194.
[107]Eriksson A R, Albrektsson T. Temperature threshold levels for heat-induced bone tissue injury:a vital-microscopic study in the rabbit[J]. J Prosthet Dent.1983,50(1):101-107.
[108]Musallam S, Bak M J. Troyk P R. et al. A floating metal microelectrode array for chronic implantation[J]. J Neurosci Methods.2007,160(1):122-127.
[109]Polikov V S, Tresco P A, Reichert W M. Response of brain tissue to chronically implanted neural electrodes[J].2005,148(1):1-18.
[110]Brown E N, Frank L M, Tang D, et al. A Statistical Paradigm for Neural Spike Train Decoding Applied to Position Prediction from Ensemble Firing Patterns of Rat Hippocampal Place Cells [J]. The Journal of Neuroscience.1998,18(18):7411-7425.
[111]Yu Y, Zhang S, Zhang H, et al. Neural decoding based on probabilistic neural network[J]. Journal of Zhejiang University-Science B.2010,11(4):298-306.
[112]Amirikian B, Georgopoulos A P. Directional tuning profiles of motor cortical cells[J]. Neurosci Res.2000,36(1):73-79.
[113]Georgopoulos A P, Schwartz A B, Kettner R E. Neuronal population coding of movement direction[J]. Science.1986,233(4771):1416-1419.
[114]Roweis S T, Saul L K. Nonlinear dimensionality reduction by locally linear embedding[J]. Science.2000,290(5500):2323-2326.
[115]Belkin M, Niyogi P. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation [J]. Neural Computation.2003,15(6):1373-1396.
[116]David S A P N. A recurrent neural network for closed-loop intracortical brain-machine interface decoders[J]. Journal of Neural Engineering.2012,9(2):1-10.
[117]Bansal A K, Truccolo W, Vargas-Irwin C E, et al. Decoding 3D reach and grasp from hybrid signals in motor and premotor cortices:spikes, multiunit activity, and local field potentials [J]. Journal of Neurophysiology.2012,107(5):1337-1355.
[118]Taylor D M, Tillery S I H, Schwartz A B. Direct Cortical Control of 3D Neuroprosthetic Devices [J]. Science.2002,296(5574):1829-1832.
[119]Kuncheva L I. Editing for the k-nearest neighbors rule by a genetic algorithm [J]. Pattern Recognition Letters.1995,16(8):809-814.
[120]Keller J M, Gray M R, Givens J A. A Fuzzy K-nearest Neighbor Algorithm[J]. IEEE Transactions on Systems Man and Cybernetics.1985,15(4):580-585.
[121]Cover T, Hart P. Nearest neighbor pattern classification[J]. IEEE Transactions on Information Theory.1967,13(1):21-27.
[122]Chapelle O, Haffner P, Vapnik V N. Support vector machines for histogram-based image classification[J]. IEEE Trans Neural Netw.1999,10(5):1055-1064.
[123]Drucker H, Wu D, Vapnik V N. Support vector machines for spam categorization[J]. IEEE Trans Neural Netw.1999,10(5):1048-1054.
[124]Burges C J C. A Tutorial on Support Vector Machines for Pattern Recognition[J]. Data Mining and Knowledge Discovery.1998,2(2):121-167.
[125]Scholkopf B, Kah-Kay S, Burges C J C, et al. Comparing support vector machines with Gaussian kernels to radial basis function classifiers[J]. IEEE Transactions on Signal Processing. 1997,45(11):2758-2765.
[126]Sain S R. The Nature of Statistical Learning Theory[J]. Technometrics.1996,38(4):409.
[127]Jrad N, Congedo M, Phlypo R, et al. sw-SVM:sensor weighting support vector machines for EEG-based brain-computer interfaces [J]. Journal of Neural Engineering.2011,8(5):1-11.
[128]Gaston F M F, Irwin G W. Systolic Kalman filtering:an overview[J]. Control Theory and Applications, IEE Proceedings D.1990,137(4):235-244.
[129]Malik W Q, Truccolo W, Brown E N, et al. Efficient Decoding With Steady-State Kalman Filter in Neural Interface Systems [J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering.2011,19(1):25-34.
[130]Srinivasan L, Eden U T, Mitter S K, et al. General-purpose filter design for neural prosthetic devices[J]. Jouranl of Neurophysiology.2007,98(4):2456-2475.
[131]Wu W, Hatsopoulos N G. Real-time decoding of nonstationary neural activity in motor cortex[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering.2008,16(3): 213-222.
[132]Specht D F. A general regression neural network[J]. IEEE Transactions on Neural Networks. 1991,2(6):568-576.
[133]Vidaurre C, Sannelli C, Muller K R, et al. Co-adaptive calibration to improve BCI efficiency[J]. Journal of Neural Engineering.2011,8:1-8.
[134]Dangi S, Gowda S, Heliot R, et al. Adaptive Kalman Filtering for Closed-Loop Brain-Machine Interface Systems[C].2011 5th International IEEE/EMBS Conference on Neural Engineering, NEW YORK:IEEE,2011,609-612.
[135]Li Z, O'Doherty J E, Lebedev M A, et al. Adaptive Decoding for Brain-Machine Interfaces Through Bayesian Parameter Updates[J]. Neural Computation.2011,23(12):3162-3204.
[136]Vidaurre C, Sannelli C, Muller K R, et al. Machine-Learning-Based Coadaptive Calibration for Brain-Computer Interfaces[J]. Neural Computation.2010,23:791-816.
[137]Digiovanna J, Mahmoudi B, Fortes J, et al. Coadaptive Brain-Machine Interface via Reinforcement Learning[J]. IEEE Transactions on Biomedical Engineering.2009,56(1):54-64.
[138]Gage G J, Ludwig K A, Otto K J, et al. Naive coadaptive cortical control[J]. J Neural Eng. 2005,2(2):52-63.
[139]Miller L E, Weber D J. Brain Training:Cortical Plasticity and Afferent Feedback in Brain-Machine Interface Systems[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering.2011,19(5):465-467.
[140]Madisen L, Mao T, Koch H, et al. A toolbox of Cre-dependent optogenetic transgenic mice for light-induced activation and silencing[J]. Nature Neuroscience.2012,15(5):793-802.
[141]Liu X, Ramirez S, Pang P T, et al. Optogenetic stimulation of a hippocampal engram activates fear memory recall[J]. Nature.2012,484(7394):381-385.
[142]Tye K M, Deisseroth K. Optogenetic investigation of neural circuits underlying brain disease in animal models[J]. Nat Rev Neurosci.2012,13(4):251-266.
[143]Suminski A J, Tkach D C, Fagg A H, et al. Incorporating Feedback from Multiple Sensory Modalities Enhances Brain-Machine Interface Control[J]. Journal of Neuroscience.2010.30(50): 16777-16787.
[144]Zhang S M, Jiang B, Zhu J M, et al. A Study on Combining Local Field Potential and Single Unit Activity for Better Neural Decoding[J]. International Journal of Imaging Systems and Technology.2011,21(2SI):165-172.