某些疾病會中斷大腦與肌肉間的神經肌肉管道，例如腦幹中風、脊髓損傷、肌萎縮性脊髓側索硬化症 (amyotrophic lateral sclerosis) 等等，它們會傷害控制肌肉的神經或直接傷害肌肉，嚴重者可能會失去所有隨意肌的控制能力，包括眼動、呼吸，即陷入閉鎖(lock-in)狀態，人腦電腦介面(Brain Computer Interface, BCI)系統即為幫助這些病患回復與外界溝通能力的方法之一。
　　本研究使用鋼索傳動的方式，建構了一套具抓握功能的義手，人腦電腦介面偵測使用者自主想像動作所刺激的腦波變化，辨識波抑制現象，控制義手幫助原本無法活動的手指進行即時的屈曲和伸張動作，透過感測器回授握力與控制介面過濾腦波辨識命令以恢復手掌抓握功能。完成之義手指套轉動範圍20至100度，指尖出力2Kgw，足可抓握240g瓷杯。

　　Some diseases can disrupt the neuromuscular channels between brain and muscle, such as brainstem stroke, spinal cord injury, amyotrophic lateral sclerosis and so on. Those most severely affected may lose all voluntary muscle control, including eye movements and respiration, or completely locked in to their bodies. Brain computer interface (BCI) is one of the solutions to help these patients restoring function of communicating with the external world.
　　Is this thesis, a tendon-driven prosthetic hand was built, and interfaced to a BCI that can detect the variation of EEG induced by imaginary movement. In particular, the variation of  wave was employed by the BCI to identify the patient’s intent. The prosthetic hand can assist the flexion and extension of the patient’s index finger in real time. Through the force sensor and control interface, the prosthetic hand can measure the gripping force and restore the subject’s gripping function. The finger tube of the prosthetic hand has a range of movement 20 to 100 degrees, and a gripping force ranged from 0 to 2Kgw and the subject is able to fetch a cup with a weight of 240g from a table.

[1] 張丙龍，彭英毅，張月蘭，解剖生理學(第四版) ，南山堂出版社，1996
[2] C. H. Cheng, “Real-time identification of μ wave with wavelet neural networks” 1st International IEEE EMBS Special Topic Conference on Neural Engineering ,2003.
[3] G.. Pfurtscheller, C. Neuper, B. Obermaier , G.. Krausz, “Graz- BCI : State of the Art and Clinical Applications,” IEEE Trans. Neural Syst. Rehab. Eng., vol. 11, No. 2, pp.177-180 ,JUNE 2003.
[4] G. C. Burdea, Force and touch feedback for virtual reality, A Wiley-Interscience Publication, 1996.
[5] C. Guger, A. Schlogl, C. Neuper, D. Walterspacher, T. Strein, and G. Pfurtscheller, “Rapid prototyping of an EEG-based brain-computer interface(BCI),” IEEE Trans. Neural Syst. Rehab. Eng., vol. 9, pp. 49-58 Mar. 2001.
[6] J. Wolpaw, N. Birbaumer, D. McFarland, G.. Pfurtscheller, T. Vaughan, “Brain- computer interfaces for communication and control” Clinical Neurophysiology, vol.113, pp.767-791, 2002.
[7] Eleanor A. Curran, Maria J. Stokes, “Learning to control brain activity: A review of the production and control of EEG components for driving brain-computer interface (BCI) systems” Brain and Cognition, vol. 51, pp. 326-336, 2003.
[8] Niels Birbaumer; Thilo Hinterberger; Andrea Kubler; Nicola Neumann, “The Thought-Translation Device(TTD):Neurobehavioral Mechanisms and Clinical Outcome” IEEE Trans. Neural Syst. Rehab. Eng., vol. 11, No. 2, pp.120-123, June , 2003.
[9] T. Hinterberger, A. Kubler, J. Kaiser, N. Neumann, N. Birbaumer, “A brain-computer interface (BCI) for the lock-in: comparison of different EEG classifications for the thought translation device” Clinical Neurophysiology, vol. 114, pp. 416-425, 2003.
[10] L. Bianchi, F. Babiloni, F. Cincotti, M. Arrivas, P. Bollero, M. Grazia Marciani, “Developing Wearable Bio-Feedback Systems:A General-Purpose Platform,” IEEE Trans. Neural Syst. Rehab. Eng., vol. 11, No. 2, pp. 117-119, JUNE, 2003.
[11] 王政道，林宙晴，朱銘祥, 〝運用μ波為控制源之研究〞，中華民國八十七年醫學工程科技研討會論文集，pp. 3-4.
[12] Gernot R. Muller, Christa Neuper, G. Pfurtscheller, “Implementation of a telemonitoring system for the control of an EEG-based brain-computer interface” IEEE Trans. Neural Syst. Rehab. Eng., vol. 11, No. 1, pp. 54-59, March, 2003.
[13] X. R. Gao, D. F. Xu, M. Cheng, S. K. Gao, “A BCI-based environmental controller for the motion-disabled” IEEE Trans. Neural Syst. Rehab. Eng., vol. 11, No. 2, pp. 137-140, JUNE 2003.
[14] J. D. R.Millan, J. Mourino, “Asynchronous BCI and local neural classifiers: an overview of the adaptive brain interface project” IEEE Trans. Neural Syst. Rehab. Eng., vol. 11, No. 2, pp. 159-161, JUNE, 2003.
[15] B. Obermaier, G.. R. Muller, Member, “Virtual keyboard controlled by spontaneous EEG activity” IEEE Trans. Neural Syst. Rehab. Eng., vol. 11, No. 4, pp. 422-426, December, 2003.
[16] G. Pfurtscheller, Gernot R. Muller, Jorg Pfurtscheller, Hans J. Gerner, R. Rupp, “Thought-control of functional stimulation to restore hand grasp in a patient with tetraplegia,” Neuroscience Letters, vol. 351, pp. 33-36, 2003.
[17] C. M. Light, P. H. Chappell, P. J. Kyberd, “Establish a standardized clinical assessment tool of pathologic and prosthetic hand function: normative data, reliability, and validity,” Arch Phys Med Rehabil, vol. 83, pp. 776-783, June, 2002.