對於全身癱瘓的患者，如脊髓損傷病人，目前的科技逐漸有希望在患者大腦未受損的情況下，以其m波之變化做為輔具或電刺激之控制源。但要將m波之變化當成控制源，必須先能夠準確的鑑別出m波的變化，但在感覺運動區卻同時可偵測到m波和來自枕葉a波的變化，因此有必要發展腦波的前處理技巧以消除a波的影響。
因此本研究之目的在於建構一人機介面系統，偵測受測者C3、C4區域腦波的變化，應用演算法則，將感覺運動a波的雜訊過濾，以期得到正確m波變化，提高動作之辨識率。前處理的方法採用拉氏運算和適應性雜訊消除，比較兩者的優劣並和原始訊號作比較。動作辨識模擬則採用樣版和相關係數的方法，偵測姆指是否有自主動作發生。
現有文獻對於偵測m波的實驗時間都很短，因此均未提到假命中的部分，若實驗中檢視有眨眼的情況發生，則捨棄該次實驗數據，這樣的人機系統進入應用階段較不符合實際，因此本研究也討論假命中的機率。人體實驗結果證實經由前處理之m波抑制現象比原始數據明顯，並且能降低假命中誤判的次數，但自然、睜眼和閉眼利用樣版相關係數法辨識率並不會有很大的差別。

For the totally paralyzed patients, if their brain cortex is not damaged, they might be able to control the prosthesis with the m wave. Before the m wave can be taken as a control source, we must detect the variations of m wave very precisely. However, in the primary sensory-motor area local m waves and a waves originating form the occipital cortex are detected simultaneously.
The objective of this thesis is to construct a brain-computer interface to detect the variation of the brainwave of the C3 and C4 area. Some algorithms are utilized to reduce the effect of a waves on m wave identification. Two methods, namely, Lapacian operation and adaptive noise cancellation are utilized for preprocessing EEG to reduce the a-wave and for comparison with the original EEG. A novel template and correlation coefficient method were developed for simulating thumb movement identification.
In existing researches, the experiment time of a single trial to detect the m waves is very short, therefore, the false positive results were not discussed. If the subject blinks the eyes in a trial, the set of experiment data is discarded without consideration about the false positive ratio may yield an impractical brain-machine interface. Thus in this thesis the false positive ratio would be used for evaluating the performance of a pattern recognition algorithm. Human test results indicated that the the m wave suppression phenomenon after preprocessing is more obvious than before preprocessing and the number of false positive can be reduced. On the other hand the status of subjects, i.e., nature, open eyes and close eyes, does not has effect on success ratio of a template correlation method developed in this work.

[1] C. Neuper, A. Schlogl, G. Pfurtscheller, “Enhancement of left-right sensorimotor EEG differences during feedback-regulated motor imagery,” Clin. Neurophysio., Vol. 16, No. 4, pp. 373-382, 1999.

[2] 王政道，“大拇指自主動作對腦波不同頻寬之影響”，中國機械工程學會第十六屆全國學術研討會論文集，pp. 469-472。

[3] C. H. Huang, “Influence of passive thumb movements on m wave,” Journal of Medical and Biological Engineering, Vol. 23(1), pp. 37-43, 2003.

[4] C. W. Chen, “Real-time identification of m wave with wavelet neural networks,” 1st International IEEE EMBS Special Topic Conference on Neural Engineering, 2003.

[5] J. R. Wolpaw, N. Birbaumer, “Brain-Computer Interface Technology: A Review of the First International Meeting,” IEEE Transaction On Rehabilitation Engineering, Vol. 8, No. 2, pp. 164-173, 2000.

[6] G. Pfurtscheller, C. Neuper, “Motor Imagery and Direct Brain-Computer Communication,” IEEE, Vol. 89, No. 7, pp. 1123-1134, 2001.

[7] J. R. Wolpaw, D. J. McFarland, G. W. Neat, and C. Forneris, “An EEG-based brain–computer interface for cursor control,” Electroencephalogr. Clin. Neurophysiol., Vol. 78, pp. 252–259, 1991.

[8] J. R.Wolpaw, D. J. McFarland, T. M.Vaughan, “Brain–computer interface research at the Wadsworth Center,” IEEE Trans Rehabil Eng., Vol. 8, pp. 222-225, 2000.

[9] 江部充，本間伊佐子，林景福主譯，江逸仁協譯，圖解腦波入門， 合記圖書出版社，1980。

[10] G. Pfurtscheller, F. H. Lopes and Sulva da, “Event-related EEG/MEG synchronization and desynchronization: basic principles,” Electroencephalogr. Clin. Neurophysiol., Vol. 100, pp. 1842-1857, 1999.

[11] G. Pfurtscheller, “Graphical display and statistical evaluation of event-related desynchronization (ERD),” Electroencephalogr. Clin. Neurophysiol., Vol. 43, pp. 757-760, 1977.

[12] G. Pfurtscheller and A. Berghold, “Patterns of cortical activation during planning of voluntary movement,” Electroenceph. Clin. Neurophysiol., Vol. 72, pp. 250-258, 1989.

[13] C. Andrew and G. Pfurtscheller, “On the existence of different alpha band in the hand area of man,” Neuroscience Letters, pp. 203-206, 1999.

[14] J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, T. M. Vaughan, “Brain-computer interfaces for communication and control,” Clin. Neurophysiol., Vol. 113, pp. 767-791, 2002.

[15] S. V. Narasimhan, D. Narayana Dutt, “Application of LMS adaptive predictive flitering for muscle artifact (noise) cancellation from EEG signals.” Computers Elect. Eng., Vol. 22, No. 1, pp. 13-30, 1996.

[16] G. Pfurtscheller, K. Zalaudek, C. Neuper, “Event-related beta synchronization after wrist, finger and thumb movement,” Electroencephalogr. Clin. Neurophysiol., Vol. 109, pp. 154-160, 1998.

[17] T. Inouye, K. Shinosaki, S. Toi, Y. Matsumoto, N. Hosaka, “Potential flow of a activity in the human electroencephalogram,” Neuroscience letters, Vol. 187, pp. 29-32, 1995.

[18] R. Srinivasan, “Spatial structure of the human alpha rhythm: global correlation in adults and local correlation in children,” Clin. Neurophysiol., Vol. 110, pp. 1351-1362, 1999.

[19] S. Haykin, “Adaptive filter theory,” 3rd ed., Prentice-Hall, 1996.

[20] S. A. Glantz, “Primer of bio-statistics,” 4th ed., McGraw Hill, 1997.

[21] S. P. Levine, J. E. Huggins, S. L. BeMent,R. K. Kushwaha, L. A. Schuh, E. A. Passaro, M. M. Rohde, and D. A. Ross, “Identification of Electrocorticogram patterns as the basis for a direct brain interface,” J. Clin. Neurophysiol, Vol.16, pp.139-147, 1999.

[22] J. E. Huggins, S. P. Levine, S. L. BeMent, R. K. Kushwaha, L. A. Schuh, E. A. Passaro, M. M. Rohde, D. A. Ross, K. V. Elisevich, and B. J. Smith, “Detection of event-related potentials for development of a direct brain interface,” J. Clin. Neurophysiol, Vol.16, pp.138-155, 1999.