用于穴位电刺激的脑—机接口反馈训练技术研究
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摘要
近年来,因脑血管疾病而导致上肢瘫痪的患者人数日趋增长,这些患者因不能控制肢体正常活动,导致上肢肌肉萎缩严重,不仅影响了生活质量,而且增大了康复难度。穴位电刺激(Electrical Aqupoint Stimulation,EAS)将欧美国家的电神经刺激疗法,与传统针灸穴位相结合,能够起到恢复肌肉张力,辅助康复的效果。但现今的EAS技术均由医护人员进行操作,患者的自主性与参与度不足,无法达到理想康复效果。
脑-机接口(Brin-Computer Interface,BCI)技术的兴起和迅速发展使EAS的自动控制成为可能。本文利用了BCI技术中的想像动作诱发事件相关去同步(Event Related Desynchronization,ERD)现象,设计了一个脑-机接口控制穴位电刺激的BCICEAS系统。该系统控制平台由信号采集模块、信号分析模块和设备控制模块组成。使用者按照系统界面所给出的提示,执行想象或者静息任务,系统采集相应的脑电信号,经脑电放大器和信号采集卡分别进行放大滤波和A/D转换后输入计算机;然后在LabVIEW平台上完成基于Fisher参数的特征频段提取信号处理,使用马氏分类器进行分类;最后将分类信号转换为控制信号完成对穴位电刺激器的控制。
针对目前脑-机接口研究中缺乏对于训练-脑电特征相关性的探索,尤其是缺乏统计学意义上训练如何影响特征变化的相关研究的问题,本文对训练过程和反馈环节的引入进行了相应的研究,以期提高系统的使用效果。为测试系统性能以及研究训练过程对脑电控制信号的影响,研究中设计了可实时显示脑电功率谱值的动态液柱图用于反馈调整想象状态,将12名受试者分成有反馈组和无反馈组两个小组进行了一系列的训练实验。结果表明,该训练对于脑电特征有显著的增强作用,两个小组的受试者在经过完整的训练过程后其脑电特征均表现出显著的增强;另外,反馈环节的引入可以提高训练的效果,有反馈组的脑电特征增强幅度要明显高于无反馈组。
最后,本文对受试者采用反馈训练后进行在线BCICEAS的系统测试,受试者的实验正确率可达到76.7%以上,其中最高正确率可达90.9%,从而验证了本文所设计BCICEAS系统的可行性。以上实验结果表明文中所设计结合反馈训练技术的BCICEAS系统可以有效实现将想像动作转化为穴位刺激自主控制的功能,在残疾患者上肢康复方面具有较高的应用前景,未来有望获得进一步推广。
In recent years, the number of sufferers with paralyzed arms for cerebrovascular disease is increasing dramatically. These patients are not able to control their limbs for normal activities, which will possibly lead to severe upper-limb muscle atrophy, as a result, will not only debase the quality of life, but also increase the difficulty of patients’recovery. Electrical Acupoint Stimulation (EAS) which combined the electrical nerve stimulation therapy of Europe &USA with traditional acupuncture points can play an important role in restoring the muscle tension and assisting the recovery effect. However, under the current EAS technology, these are all carried by the medical staff, while the patients with poor degree of autonomy and participation which may lead to unsatisfying recovery effect.
The rapid rise and development of Brain-Computer-Interface (BCI) technology makes the automatic control of EAS possible. This thesis made use of Time-Related Desynchronization phenomenon evoked by imaginary movement to design an BCI-controls-EAS (BCICEAS) system. The system consisted of signal acquisition module, signal analysis module and the device control module. In accordance with the system’s cues, the user performed imagining action or resting. System acquired the corresponding EEG, and put it into computer after the EEG amplifier and signal acquisition card for filtering, amplification and A/D conversion, after which processed the extracted signal on feature frequency bands based on the Fisher parameter analysis in the Labview platform. Then, the signal went through Mahalanobis Classifier to get classification signal which finally changed into a control signal for controlling the EAS system.
Against the absence of the exploration of the relativity of EEG features for training, especially for the absence of the relative research problems about how training effects the change of these features, this thesis made a corresponding study of the introduction of training process and feedback tache with a view to enhancing the effect of system. In order to test the system performance and study the effect of training process caused to the BCI system, this research designed a dynamic liquid column chart which could show the real-time display of EEG power spectral values adjusting the imaginary statement, and performed a series of experiments to 12 subjects who were divided into 2 groups by with or without feedback. The results showed that: training has a significant enhancement of EEG features and the subjects of both groups all show significant enhancement of EEG features after the whole training process. In addition, the introduction of feedback tache can improve the effect of training; also the EEG features of the subjects in the group with feedback were obviously better than those in the group without feedback.
In conclusion, the subjects were tested by an on-line system after feedback training and the highest experimental accuracy rate of them could reach above 76.7% equally, the highest one can reach 90.9%, which proved the system of BCICEAS designed in this thesis is feasible. The results of these experiments showed that the designed system of BCICEAS in this research can realize the function of transforming imaginary movement into self-acupoint stimulization and possess higher application prospects of serving disabled people to recove their upper limbs, which was worth further research and extension.
脑-机接口(Brin-Computer Interface,BCI)技术的兴起和迅速发展使EAS的自动控制成为可能。本文利用了BCI技术中的想像动作诱发事件相关去同步(Event Related Desynchronization,ERD)现象,设计了一个脑-机接口控制穴位电刺激的BCICEAS系统。该系统控制平台由信号采集模块、信号分析模块和设备控制模块组成。使用者按照系统界面所给出的提示,执行想象或者静息任务,系统采集相应的脑电信号,经脑电放大器和信号采集卡分别进行放大滤波和A/D转换后输入计算机;然后在LabVIEW平台上完成基于Fisher参数的特征频段提取信号处理,使用马氏分类器进行分类;最后将分类信号转换为控制信号完成对穴位电刺激器的控制。
针对目前脑-机接口研究中缺乏对于训练-脑电特征相关性的探索,尤其是缺乏统计学意义上训练如何影响特征变化的相关研究的问题,本文对训练过程和反馈环节的引入进行了相应的研究,以期提高系统的使用效果。为测试系统性能以及研究训练过程对脑电控制信号的影响,研究中设计了可实时显示脑电功率谱值的动态液柱图用于反馈调整想象状态,将12名受试者分成有反馈组和无反馈组两个小组进行了一系列的训练实验。结果表明,该训练对于脑电特征有显著的增强作用,两个小组的受试者在经过完整的训练过程后其脑电特征均表现出显著的增强;另外,反馈环节的引入可以提高训练的效果,有反馈组的脑电特征增强幅度要明显高于无反馈组。
最后,本文对受试者采用反馈训练后进行在线BCICEAS的系统测试,受试者的实验正确率可达到76.7%以上,其中最高正确率可达90.9%,从而验证了本文所设计BCICEAS系统的可行性。以上实验结果表明文中所设计结合反馈训练技术的BCICEAS系统可以有效实现将想像动作转化为穴位刺激自主控制的功能,在残疾患者上肢康复方面具有较高的应用前景,未来有望获得进一步推广。
In recent years, the number of sufferers with paralyzed arms for cerebrovascular disease is increasing dramatically. These patients are not able to control their limbs for normal activities, which will possibly lead to severe upper-limb muscle atrophy, as a result, will not only debase the quality of life, but also increase the difficulty of patients’recovery. Electrical Acupoint Stimulation (EAS) which combined the electrical nerve stimulation therapy of Europe &USA with traditional acupuncture points can play an important role in restoring the muscle tension and assisting the recovery effect. However, under the current EAS technology, these are all carried by the medical staff, while the patients with poor degree of autonomy and participation which may lead to unsatisfying recovery effect.
The rapid rise and development of Brain-Computer-Interface (BCI) technology makes the automatic control of EAS possible. This thesis made use of Time-Related Desynchronization phenomenon evoked by imaginary movement to design an BCI-controls-EAS (BCICEAS) system. The system consisted of signal acquisition module, signal analysis module and the device control module. In accordance with the system’s cues, the user performed imagining action or resting. System acquired the corresponding EEG, and put it into computer after the EEG amplifier and signal acquisition card for filtering, amplification and A/D conversion, after which processed the extracted signal on feature frequency bands based on the Fisher parameter analysis in the Labview platform. Then, the signal went through Mahalanobis Classifier to get classification signal which finally changed into a control signal for controlling the EAS system.
Against the absence of the exploration of the relativity of EEG features for training, especially for the absence of the relative research problems about how training effects the change of these features, this thesis made a corresponding study of the introduction of training process and feedback tache with a view to enhancing the effect of system. In order to test the system performance and study the effect of training process caused to the BCI system, this research designed a dynamic liquid column chart which could show the real-time display of EEG power spectral values adjusting the imaginary statement, and performed a series of experiments to 12 subjects who were divided into 2 groups by with or without feedback. The results showed that: training has a significant enhancement of EEG features and the subjects of both groups all show significant enhancement of EEG features after the whole training process. In addition, the introduction of feedback tache can improve the effect of training; also the EEG features of the subjects in the group with feedback were obviously better than those in the group without feedback.
In conclusion, the subjects were tested by an on-line system after feedback training and the highest experimental accuracy rate of them could reach above 76.7% equally, the highest one can reach 90.9%, which proved the system of BCICEAS designed in this thesis is feasible. The results of these experiments showed that the designed system of BCICEAS in this research can realize the function of transforming imaginary movement into self-acupoint stimulization and possess higher application prospects of serving disabled people to recove their upper limbs, which was worth further research and extension.
引文
[1] JR Wolpaw, N Birbaumer, DJMC Farland. Brain-computer interface for communication and control. Clinical Neurophysiology, 2002,113 (6): 769～791
[2] B Blankertz, G Dornhege, C Sch?fer. Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis. IEEE Transaction on Neural Systems andRehabilitation Engineering, 2003 ,11 (2): 127～130
[3] N Neumann, A Kübler. Training Locked-in Patients: A challenge for the use of brain-computer interfaces. IEEE Transaction on Neural Systems and Rehabilitation Engineering, 2003 ,11 (2): 169～172
[4] Hesheng Liu, Xiaorong Gao, Fusheng Yang. Imagined hand movement identification based on spatio-temporal pattern recognition of EEG. Proc of the 1st International IEEE EMBS Conference on Neural Engineering,Capri island:IEEE , 2003,8(3):599～602
[5] PR Kennedy, KD Adams. A decision tree for brain-computer interface devices. IEEE Transaction on Neural Systems and Rehabilitation Engineering, 2003, 11(2): 148～150
[6] Blankertz B, Muller KR, Krusienski DJ, et al. The BCI Competition III: validating alternative approaches to actual BCI problems. IEEE Trans Neural Syst Rehabil Eng, 2006,14:153～159
[7] Fried I, Latz S, McCarthy, G et al. Functional organization of human supplementary motor cortex studied by electrical stimulation. Neurosici, 1991, 11: 36~56
[8] Atsushi Miki, Jonathan Raz, Theo G, et al. Reproducibility of visual activation in functional MR imaging and effects of postprocessing. American Journal of Neuroradiology, 2000, 21: 910
[9]程明,高上凯,张琳,基于脑电信号的脑-计算机接口,北京生物医学工程,2000,19(2),113～118
[10] Birbaumer N. Brain-computer-interface research:coming of age. clin Neurophysiol, 2006, 117: 479～483
[11] Labedev MA, Nicolelis MAL. Brain-machine interfaces:past, present and future. Trends Neurosci, 2006, 29: 536～546
[12] Steven G. Mason, Gary E. Birch. A General Frame work for Brain-computer Interface Design. IEEE Trans. Rehab. Eng, vol.11, No.1, MARCH 2003, 70～85
[13]高扬,基于脑电Alpha波的脑-机接口系统设计:[硕士学位论文],天津;天津大学,2003
[14] J.R.Wolpaw. Brain-computer interface: singals, method, and goals. Proceedings of the 1st international IEEE EMBS conference on Neural Engineering, 2003, 2～22
[15] Naeem M, Brunner C, Leeb R, et al. Seperability of four-class motor imagery data using independent components analysis. J Neural Eng, 2006, 3: 208～216
[16] J.R. Wolpaw, et al. The wads worth center brain-computer interface(BCI) research and development program. IEEE Trans. Neural syst. Rchab. Eng, 2003,11(2): 204～207
[17] Andrea Kübler, et al. Brain-computer communication: unlock the locked in. psych Bull, 2001,127(3): 358～375
[18] Andrea Kübler, etal. The thought translation device (TTD) for completely paralyzed patients. IEEE Trans.Rehab.Eng, 2000,8(2): 190～193
[19] Sutton S, Braren M, Zubin J, et al. Evoked correlates of stimulus uncertainty. Science, 1965,150: 443～446
[20] Dondhin E, etal. The mental prosthesis: assessing the speed of a P300-based brain-computer interface. IEEE Trans. Rehab. Eng, 2000, 8(2): 174～179
[21]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):152～154
[22] McMilan G.R., Calhoun G.L. Direct brain interface utilizing self-regulation of steady-state visual evoked response (ssver). Proceedings of RESNA, 1995,14(7): 693～695
[23] Dewan E.M.. Occipital alpha rhythm eye position and lens accommodation. Nature, 1967, 214: 975～977
[24] Kirkup L., Searle A., Craig A., et al. EEG-based system for rapid on-off switching without prior learning. Med Biol Eng Comput, 1997, 35: 504～509
[25] G. Pfurtscheller, C. Neuper, C. Guger, et al.. Current trends in Graz brain -computer interface(BCI) research.. IEEE Trans. Rehab. Eng, 2000,8 (2): 222～226
[26] Keirn ZA, Aunon Jl. A new mode of communication between man and his surroundings. IEEE Trans Biomed Eng, 1990,37(12): 1209～1214
[27] Matthew Middendorf. Brain-computer interfaces based on the steady-state visual-evoked response. IEEE Trans. Rehab. Eng, 2000,8(2): 211～214
[28]赵香花,基于脑-机接口的残疾人环境控制装置的设计,中国康复医学杂志,2004,19(5):324～326
[29] The International 10-20 System Electrode Placement. http://www. telemedx. com/ protocol/section7.2.html.
[30]谭郁玲,临床脑电图与脑地形图学,北京:人民卫生出版社,1999
[31]魏景汉,罗跃嘉,认知事件相关脑电位教程,经济日报出版社,2002
[32] .Wei Lu, Jagath C. Rajapakse. Approach and applications of constrained ICA. IEEE Transactions on Neural Networks, 2005, 16(1): 58～62
[33] Lucas Parra, Paul saida. Converging evidence of linear independent components in EEG. Neural Engineering 2003,conference proceedings,First International IEEE EMBS conference on, 20-22 March 2003, 525～528
[34] S. kely, D. Burke, P de Chazal, R. Reilly. Parametric models and spectral analysis for classification in brain-computer interfaces. Proceeding of 14th International conference on Digital signal processing, Greece, 2002
[35] Schl?gl, The electroencephalogram and the adaptive autoregressive model: theory and applications, Shaker Verlag, Aachen, Germany, 2000
[36] Schl?gl A, Neuper C. Pfurtscheller G. Estimating the mutual information of an EEG-based brain-computer interface. Biomedizinische Techink, 2002, 47(1-2): 3～8
[37] MAIIAT S, ZHANG Z.Matching pursuits with time-frequency dictionaries.IEEE Trans Signal Processing, 1993, 41(12): 3397～3415
[38] DURKAP J, BLINOWSKA K J. Analysis of EEG transients by means of matching pursuit Annals Biom Eng, 1995, 23: 608～611
[39]裴晓梅,运动相关脑电信号分析及脑机接口技术研究,西安:西安交通大学生命科学与技术学院,2006
[40]汪敏生译著,LabVIEW基础教程,北京:电子工业出版社,2002
[41]李昆,周晓兰,基于Mahalanobis距离的运动意识分类研究,计算机工程与设计,2007(7):1601～1603
[42]李玉榕,项国波,一种基于马氏距离的线性判别分析分类算法,计算机仿真,2006(8):86～8
[2] B Blankertz, G Dornhege, C Sch?fer. Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis. IEEE Transaction on Neural Systems andRehabilitation Engineering, 2003 ,11 (2): 127～130
[3] N Neumann, A Kübler. Training Locked-in Patients: A challenge for the use of brain-computer interfaces. IEEE Transaction on Neural Systems and Rehabilitation Engineering, 2003 ,11 (2): 169～172
[4] Hesheng Liu, Xiaorong Gao, Fusheng Yang. Imagined hand movement identification based on spatio-temporal pattern recognition of EEG. Proc of the 1st International IEEE EMBS Conference on Neural Engineering,Capri island:IEEE , 2003,8(3):599～602
[5] PR Kennedy, KD Adams. A decision tree for brain-computer interface devices. IEEE Transaction on Neural Systems and Rehabilitation Engineering, 2003, 11(2): 148～150
[6] Blankertz B, Muller KR, Krusienski DJ, et al. The BCI Competition III: validating alternative approaches to actual BCI problems. IEEE Trans Neural Syst Rehabil Eng, 2006,14:153～159
[7] Fried I, Latz S, McCarthy, G et al. Functional organization of human supplementary motor cortex studied by electrical stimulation. Neurosici, 1991, 11: 36~56
[8] Atsushi Miki, Jonathan Raz, Theo G, et al. Reproducibility of visual activation in functional MR imaging and effects of postprocessing. American Journal of Neuroradiology, 2000, 21: 910
[9]程明,高上凯,张琳,基于脑电信号的脑-计算机接口,北京生物医学工程,2000,19(2),113～118
[10] Birbaumer N. Brain-computer-interface research:coming of age. clin Neurophysiol, 2006, 117: 479～483
[11] Labedev MA, Nicolelis MAL. Brain-machine interfaces:past, present and future. Trends Neurosci, 2006, 29: 536～546
[12] Steven G. Mason, Gary E. Birch. A General Frame work for Brain-computer Interface Design. IEEE Trans. Rehab. Eng, vol.11, No.1, MARCH 2003, 70～85
[13]高扬,基于脑电Alpha波的脑-机接口系统设计:[硕士学位论文],天津;天津大学,2003
[14] J.R.Wolpaw. Brain-computer interface: singals, method, and goals. Proceedings of the 1st international IEEE EMBS conference on Neural Engineering, 2003, 2～22
[15] Naeem M, Brunner C, Leeb R, et al. Seperability of four-class motor imagery data using independent components analysis. J Neural Eng, 2006, 3: 208～216
[16] J.R. Wolpaw, et al. The wads worth center brain-computer interface(BCI) research and development program. IEEE Trans. Neural syst. Rchab. Eng, 2003,11(2): 204～207
[17] Andrea Kübler, et al. Brain-computer communication: unlock the locked in. psych Bull, 2001,127(3): 358～375
[18] Andrea Kübler, etal. The thought translation device (TTD) for completely paralyzed patients. IEEE Trans.Rehab.Eng, 2000,8(2): 190～193
[19] Sutton S, Braren M, Zubin J, et al. Evoked correlates of stimulus uncertainty. Science, 1965,150: 443～446
[20] Dondhin E, etal. The mental prosthesis: assessing the speed of a P300-based brain-computer interface. IEEE Trans. Rehab. Eng, 2000, 8(2): 174～179
[21]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):152～154
[22] McMilan G.R., Calhoun G.L. Direct brain interface utilizing self-regulation of steady-state visual evoked response (ssver). Proceedings of RESNA, 1995,14(7): 693～695
[23] Dewan E.M.. Occipital alpha rhythm eye position and lens accommodation. Nature, 1967, 214: 975～977
[24] Kirkup L., Searle A., Craig A., et al. EEG-based system for rapid on-off switching without prior learning. Med Biol Eng Comput, 1997, 35: 504～509
[25] G. Pfurtscheller, C. Neuper, C. Guger, et al.. Current trends in Graz brain -computer interface(BCI) research.. IEEE Trans. Rehab. Eng, 2000,8 (2): 222～226
[26] Keirn ZA, Aunon Jl. A new mode of communication between man and his surroundings. IEEE Trans Biomed Eng, 1990,37(12): 1209～1214
[27] Matthew Middendorf. Brain-computer interfaces based on the steady-state visual-evoked response. IEEE Trans. Rehab. Eng, 2000,8(2): 211～214
[28]赵香花,基于脑-机接口的残疾人环境控制装置的设计,中国康复医学杂志,2004,19(5):324～326
[29] The International 10-20 System Electrode Placement. http://www. telemedx. com/ protocol/section7.2.html.
[30]谭郁玲,临床脑电图与脑地形图学,北京:人民卫生出版社,1999
[31]魏景汉,罗跃嘉,认知事件相关脑电位教程,经济日报出版社,2002
[32] .Wei Lu, Jagath C. Rajapakse. Approach and applications of constrained ICA. IEEE Transactions on Neural Networks, 2005, 16(1): 58～62
[33] Lucas Parra, Paul saida. Converging evidence of linear independent components in EEG. Neural Engineering 2003,conference proceedings,First International IEEE EMBS conference on, 20-22 March 2003, 525～528
[34] S. kely, D. Burke, P de Chazal, R. Reilly. Parametric models and spectral analysis for classification in brain-computer interfaces. Proceeding of 14th International conference on Digital signal processing, Greece, 2002
[35] Schl?gl, The electroencephalogram and the adaptive autoregressive model: theory and applications, Shaker Verlag, Aachen, Germany, 2000
[36] Schl?gl A, Neuper C. Pfurtscheller G. Estimating the mutual information of an EEG-based brain-computer interface. Biomedizinische Techink, 2002, 47(1-2): 3～8
[37] MAIIAT S, ZHANG Z.Matching pursuits with time-frequency dictionaries.IEEE Trans Signal Processing, 1993, 41(12): 3397～3415
[38] DURKAP J, BLINOWSKA K J. Analysis of EEG transients by means of matching pursuit Annals Biom Eng, 1995, 23: 608～611
[39]裴晓梅,运动相关脑电信号分析及脑机接口技术研究,西安:西安交通大学生命科学与技术学院,2006
[40]汪敏生译著,LabVIEW基础教程,北京:电子工业出版社,2002
[41]李昆,周晓兰,基于Mahalanobis距离的运动意识分类研究,计算机工程与设计,2007(7):1601～1603
[42]李玉榕,项国波,一种基于马氏距离的线性判别分析分类算法,计算机仿真,2006(8):86～8