中風可能會造成單側肢體偏癱、感覺喪失、顏面麻痺或言語障礙等，復健治療之目的是對神經系統產生誘發，讓大腦受到損傷的鄰近部分可以取代失去的功能。近年BCI復健機器人的研究和應用顯示對於復健治療的效用，加上機器人高度彈性及精密感測系統，治療師可調整治療程序且得到量化資料檢視復健成效，給予病患更好的治療方案。
本研究以肩肘復健機器人為基礎結合腦波辨識，建立新的人腦電腦介面(Brain Computer Interface，BCI)系統輔助復健治療，透過BCI游標控制驅動機器人。共招募常人二位及中風病患一位進行八週訓練，結果顯示經訓練後常人的想像成功率一位可達90%以上且首次想像成功時間可在5秒以內，另一位常人則慣用手成功率可達90%，非慣用手的成功率雖然未超過50%，但動作完成率提高至30%，首次想像成功到達時間縮短。中風病患的想像成功率變動較大，最高可以到80%最低則完全沒有成功，但健側成功率比患側好。訓練後的復健臨床評估FMA和MAS有進步，而功能性核磁造影(fMRI)顯示於訓練前後在運動感覺區有所變化。
本研究證明BCI復健訓練對中風病患的復健治療是有效用的，但是目前只有一位病患做BCI訓練，未來仍需招募更多中風病人進行訓練增加此復健方法之可信度。

Stroke may cause unilateral paralysis, loss of sensation, facial paralysis or speech impairment. The purpose of rehabilitation is to induce neighboring parts of the nervous system to replace the lesion parts. In recent years, the research and application of rehabilitation robotics with Brain-Computer-Interface( BCI) shows promising outcome. Patients were given better treatment because the robot is a flexible and precision system, the therapist can adjust the treatment and quantify the effect of treatment.
This study combined EEG-based BCI system with a shoulder-elbow robot to create a new rehabilitation assisted therapy, through controlling the cursor movement and then drive the robot. We recruited two healthy subjects and a stroke patient to participate in the training of using BCI system for 8 weeks. One of the healthy subjects can reach the target zone in 5 seconds and the success rate is 90%, the other has a success rate of 90% for the dominant side but 50% for the non-dominant side, though his completion rate is 30% and the first arrival time of cursor movement is decreased. The stroke patients’ success rate fluctuated from session to session, the highest is 80% and the lowest is zero. The intact side is better than the affected side. In addition, the patient also received clinical assessment and functional magnetic resonance imaging (fMRI) before and after training. The FMA and MAS are improved after training, and the result of fMRI shows the reorganization does occur in the primary motor cortex.
Although only one stroke patient was treated, the results show the BCI-controlled shoulder-elbow robot might be a promising rehabilitation. Therapy is necessary to recruit more stroke patients to validate the applicability of the new method.

[1] Alex Pollock, Gill Baer, Valerie M Pomeroy,P. Langhorne, 2007, Physiotherapy treatment approaches for the recovery of postural control and lower limb function following stroke. John Wiley & Sons, Ltd, Cochrane Database of Systematic Reviews.
[2] J. Carr, R. Shepherd,J. Gordon, 1987, Movement science : Foundations for Physical Therapy in Rehabilitation. Aspen Publishers, Rockville, Md.
[3] A. Shumway-Cook,M.H. Woollacott, 2001, Motor control : theory and practical applications. Lippincott Williams & Wilkins, Philadelphia.
[4] S.S. Adler, B.Dominiek,M. Buck., 2008, PNF in practice : an illustrated guide. Springer, Heidelberg.
[5] 胡名霞, 2009, 動作控制與動作學習. 金名圖書有限公司, 台北.
[6] B.B. Johansson, 2011, "Current trends in stroke rehabilitation. A review with focus on brain plasticity." Acta Neurologica Scandinavica, 123(3): pp. 147-159.
[7] B.T. Volpe, D. Lynch, A. Rykman-Berland, M. Ferraro, M. Galgano, N. Hogan,H.I. Krebs, 2008, "Intensive sensorimotor arm training mediated by therapist or robot improves hemiparesis in patients with chronic stroke." Neurorehabilitation and Neural Repair, 22(3): pp. 305-310.
[8] D.J. Reinkensmeyer, L.E. Kahn, M. Averbuch, A. McKenna-Cole, B.D. Schmit,W.Z. Rymer, 2000, "Understanding and treating arm movement impairment after chronic brain injury: Progress with the ARM guide." Journal of Rehabilitation Research and Development, 37(6): pp. 653-662.
[9] L. Dipietro, H.I. Krebs, B.T. Volpe, J. Stein, C. Bever, S.T. Mernoff, S.E. Fasoli,N. Hogan, 2012, "Learning, Not Adaptation, Characterizes Stroke Motor Recovery: Evidence From Kinematic Changes Induced by Robot-Assisted Therapy in Trained and Untrained Task in the Same Workspace." IEEE Transactions on Neural Systems and Rehabilitation Engineering, 20(1): pp. 48-57.
[10] R.R. Seeley, T.D. Stephens,P. Tate, 2006, Essentials of Anatomy & Physiology. McGraw-Hill Higher Education, New York
[11] C. Starr, C.A. Evers,L. Starr, 2006, Biology: Concepts and Application. Thomsom Brooks/Cole, Australia.
[12] P. Brunner, L. Bianchi, C. Guger, F. Cincotti,G. Schalk, 2011, "Current trends in hardware and software for brain-computer interfaces (BCIs)." Journal of Neural Engineering, 8(2): 025001.
[13] G. Pfurtscheller,A. Aranibar, 1977, "Event-Related Cortical Desynchronization Detected by Power Measurements of Scalp EEG." Electroencephalography and Clinical Neurophysiology, 42(6): pp. 817-826.
[14] C. Neuper,G. Pfurtscheller, 2001, "Event-related dynamics of cortical rhythms: frequency-specific features and functional correlates." International Journal of Psychophysiology, 43(1): pp. 41-58.
[15] V. Morash, O. Bai, S. Furlani, P. Lin,M. Hallett, 2008, "Classifying EEG signals preceding right hand, left hand, tongue, and right foot movements and motor imageries." Clinical Neurophysiology, 119(11): pp. 2570-2578.
[16] A. Vuckovic,F. Sepulveda, 2008, "Quantification and visualisation of differences between two motor tasks based on energy density maps for brain-computer interface applications." Clinical Neurophysiology, 119(2): pp. 446-458.
[17] C.-W. Chen, C.-C.K. Lin,M.-S. Ju, 2007, "Detecting movement-related EEG change by wavelet decomposition-based neural networks trained with single thumb movement." Clinical Neurophysiology, 118(4): pp. 802-814.
[18] M. Tombini, F. Zappasodi, L. Zollo, G. Pellegrino, G. Cavallo, F. Tecchio, E. Guglielmelli,P.M. Rossini, 2009, "Brain activity preceding a 2D manual catching task." Neuroimage, 47(4): pp. 1735-1746.
[19] H. Yuan, T. Liu, R. Szarkowski, C. Rios, J. Ashe,B. He, 2010, "Negative covariation between task-related responses in alpha/beta-band activity and BOLD in human sensorimotor cortex: An EEG and fMRI study of motor imagery and movements." Neuroimage, 49(3): pp. 2596-2606.
[20] L.A. Farwell,E. Donchin, 1988, "Talking Off the Top of Your Head - Toward a Mental Prosthesis Utilizing Event-Related Brain Potentials." Electroencephalography and Clinical Neurophysiology, 70(6): pp. 510-523.
[21] N. Birbaumer, 1999, "Slow cortical potentials: Plasticity, operant control, and behavioral effects." Neuroscientist, 5(2): pp. 74-78.
[22] J.R. Wolpaw, H. Ramoser, D.J. McFarland,G. Pfurtscheller, 1998, "EEG-based communication: improved accuracy by response verification." Rehabilitation Engineering, IEEE Transactions on, 6(3): pp. 326-333.
[23] D.J. McFarland, W.A. Sarnacki, G. Schalk,J.R. Wolpaw, 2003, "EEG - based brain - computer interface ( BCI ) : real - time adaptation of feature weights." Society for Neuroscience Abstract Viewer and Itinerary Planner, 2003pp. Abstract No. 607.601.
[24] J.R. Wolpaw,D.J. McFarland, 2004, "Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans." Proceedings of the National Academy of Sciences of the United States of America, 101(51): pp. 17849-17854.
[25] D.J. McFarland, W.A. Sarnacki,J.R. Wolpaw, 2010, "Electroencephalographic (EEG) control of three-dimensional movement." Journal of Neural Engineering, 7(3): 036007.
[26] C.-C.K. Lin, M.-S. Ju, C.-W. Hsu,Y.-N. Sun, 2008, "Applying stochastic resonance to magnify mu and beta wave suppression." Computers in Biology and Medicine, 38(10): pp. 1068-1075.
[27] Y. Li, J. Long, T. Yu, Z. Yu, C. Wang, H. Zhang,C. Guan, 2010, "An EEG-Based BCI System for 2-D Cursor Control by Combining Mu/Beta Rhythm and P300 Potential." Biomedical Engineering, IEEE Transactions on, 57(10): pp. 2495-2505.
[28] T.J. Bradberry, R.J. Gentili,J.L. Contreras-Vidal, 2009, Decoding three-dimensional hand kinematics from electroencephalographic signals. Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE.
[29] T.J. Bradberry, R.J. Gentili,J.L. Contreras-Vidal, 2010, "Reconstructing Three-Dimensional Hand Movements from Noninvasive Electroencephalographic Signals." Journal of Neuroscience, 30(9): pp. 3432-3437.
[30] T.J. Bradberry, R.J. Gentili,J.L. Contreras-Vidal, 2011, "Fast attainment of computer cursor control with noninvasively acquired brain signals." Journal of Neural Engineering, 8(3): 036010.
[31] J.J. Daly,J.R. Wolpaw, 2008, "Brain-computer interfaces in neurological rehabilitation." Lancet Neurology, 7(11): pp. 1032-1043.
[32] G. Pfurtscheller, G.R. Muller-Putz, R. Scherer,C. Neuper, 2008, "Rehabilitation with Brain-Computer Interface Systems." Computer, 41(10): pp. 58-65.
[33] G.R. Müller-Putz, R. Scherer, G. Pfurtscheller,C. Neuper, 2010, "Temporal Coding of Brain Patterns for Direct Limb Control in Humans." Frontiers in Neuroscience, 434.
[34] C.-W. Chen, C.-C.K. Lin,M.-S. Ju, 2009, "Hand Orthosis Controlled Using Brain-computer Interface." Journal of Medical and Biological Engineering, 29(5): pp. 234-241.
[35] C.-W. Chen, M.-S. Ju, Y.-N. Sun,C.-C.K. Lin, 2009, "Model analyses of visual biofeedback training for EEG-based brain-computer interface." Journal of Computational Neuroscience, 27(3): pp. 357-368.
[36] B.G. Xu, S. Peng, A.G. Song, R.H. Yang,L.Z. Pan, 2011, "Robot-Aided Upper-Limb Rehabilitation Based on Motor Imagery EEG." International Journal of Advanced Robotic Systems, 8(4): pp. 88-97.
[37] B.B. Johansson, 2012, "Multisensory stimulation in stroke rehabilitation." Frontiers in Human Neuroscience, 6pp. 60.
[38] C.L. Lin, F.Z. Shaw, K.Y. Young, C.T. Lin,T.P. Jung, 2012, "EEG correlates of haptic feedback in a visuomotor tracking task." Neuroimage, 60(4): pp. 2258-2273.
[39] J.J. Daly, R. Cheng, J. Rogers, K. Litinas, K. Hrovat,M. Dohring, 2009, "Feasibility of a New Application of Noninvasive Brain Computer Interface (BCI): A Case Study of Training for Recovery of Volitional Motor Control After Stroke." Journal of Neurologic Physical Therapy, 33(4): pp. 203-211.
[40] M. Gomez-Rodriguez, J. Peters, J. Hill, B. Scholkopf, A. Gharabaghi,M. Grosse-Wentrup, 2011, "Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery." Journal of Neural Engineering, 8(3): 036005.
[41] G. Prasad, P. Herman, D. Coyle, S. McDonough,J. Crosbie, 2010, "Applying a brain-computer interface to support motor imagery practice in people with stroke for upper limb recovery: a feasibility study." Journal of Neuroengineering and Rehabilitation, 760.
[42] 李昆祐, 2009, 腦波觸發控制機器人於中風病人膝髖關節復健之研究. 碩士論文, 國立成功大學機械工程學系.
[43] 呂俊霆, 2010, 發展以簡化希爾伯黃轉換為基礎之即時處理腦波訊號方法及其應用. 碩士論文, 國立成功大學機械工程學系.
[44] 李阮曜, 2011, 腦機介面控制復健機械手於中風病患手部復健及功能性磁振照影評估. 碩士論文, 國立成功大學機械工程學系.
[45] A. Caria, C. Weber, D. Brotz, A. Ramos, L.F. Ticini, A. Gharabaghi, C. Braun,N. Birbaumer, 2011, "Chronic stroke recovery after combined BCI training and physiotherapy: A case report." Psychophysiology, 48(4): pp. 578-582.
[46] P.C. Kung, C.C.K. Lin,M.S. Ju, 2010, "Neuro-rehabilitation robot-assisted assessments of synergy patterns of forearm, elbow and shoulder joints in chronic stroke patients." Clinical Biomechanics, 25(7): pp. 647-654.
[47] M.S. Ju, C.C.K. Lin, D.H. Lin, I.S. Hwang,S.M. Chen, 2005, "A rehabilitation robot with force-position hybrid fuzzy controller: Hybrid fuzzy control of rehabilitation robot." IEEE Transactions on Neural Systems and Rehabilitation Engineering, 13(3): pp. 349-358.
[48] 龔品誠, 2011, 神經復健機器人於中風病患肩肘與前臂關節不正常協同動作之評估及治療. 博士論文, 國立成功大學機械工程學系.
[49] J. Malmivuo,R. Plonsey, 1995, Bioelectromagnetism - Principles and Applications of Bioelectric and Biomagnetic Fields. Oxford University Press, New York.
[50] G. Pfurtscheller,F.H.L. da Silva, 1999, "Event-related EEG/MEG synchronization and desynchronization: basic principles." Clinical Neurophysiology, 110(11): pp. 1842-1857.
[51] D.B. Susan S. Adler, Math Buck., 1993, PNF in practice : an illustrated guide. Springer-Verlag, New York.
[52] J.H. Cauraugh, S.B. Kim,A. Duley, 2005, "Coupled bilateral movements and active neuromuscular stimulation: Intralimb transfer evidence during bimanual aiming." Neuroscience Letters, 382(1-2): pp. 39-44.
[53] 張雅棻, 2009, "改良式制動療法與雙側動作訓練對慢性中風病患之相對效應：運動學分析." 台灣復健醫誌, 37(1): pp. 19-30.
[54] R.W. Bohannon,M.B. Smith, 1987, "Interrater Reliability of a Modified Ashworth Scale of Muscle Spasticity." Physical Therapy, 67(2): pp. 206-207.
[55] C. Neuper, R. Scherer, S. Wriessnegger,G. Pfurtscheller, 2009, "Motor imagery and action observation: Modulation of sensorimotor brain rhythms during mental control of a brain-computer interface." Clinical Neurophysiology, 120(2): pp. 239-247.
[56] S. Koganemaru, K. Domen, H. Fukuyama,T. Mima, 2012, "Negative emotion can enhance human motor cortical plasticity." European Journal of Neuroscience, 35(10): pp. 1637-1645.
[57] G. Pfurtscheller, R. Leeb, C. Keinrath, D. Friedman, C. Neuper, C. Guger,M. Slaterc, 2006, "Walking from thought." Brain Research, 1071(1): pp. 145-152.
[58] H. Yuan, C. Perdoni,B. He, 2010, "Relationship between speed and EEG activity during imagined and executed hand movements." Journal of Neural Engineering, 7(2): 026001.
[59] J. Blum, K. Lutz, R. Pascual-Marqui, K. Murer,L. Jancke, 2008, "Coherent intracerebral brain oscillations during learned continuous tracking movements." Experimental Brain Research, 185(3): pp. 443-451.
[60] T. Bardouille, T.W. Picton,B. Ross, 2010, "Attention modulates beta oscillations during prolonged tactile stimulation." European Journal of Neuroscience, 31(4): pp. 761-769.
[61] J.W. Krakauer, 2005, "Arm function after stroke: From physiology to recovery." Seminars in Neurology, 25(4): pp. 384-395.