近年來，腦電波常被用來幫助肢體障礙或者脊椎受損的人們作為輔助的用具，但因腦電波裝置太過龐大以及昂貴，以至於並非普及，因此本研究為開發設計一套價格低廉的線上即時大腦人機介面系統。透過腦電波訊號處理技術應用於四方向辨識之研究，提供肢體障礙人士利用大腦意識做出控制的實用輔具。本研究利用顏色以及閃光頻率刺激的視覺回授來達成四方向的辨識，其採用的電極位置包含了大腦的頂葉(Cz)以及大腦的枕葉(Oz, O1, O2)。利用收集到的腦波資料經過前處理(數位濾波器以及獨立成分分析)將其雜訊先做去除，再利用快速傅立葉轉換觀察其顏色以及閃光頻率刺激的差異性，並採用統計方法、主成分分析法以及獨立成分分析法抽取特徵，最後投入機器學習中的支持向量機，達到辨識四方向的效果。本實驗共採用3名受測者進行測試，其離線分析最高分類率可以達到77.5%，而平均也有75%；線上即時分類率平均也可以達到68.33%，已具初步的實用價值。

In recent years, the technology of Electroencephalography (EEG) is used for the handicapped for tool. Because the EEG equipment is expensive and bulky, the users are not very popular. This research proposes an on-line real-time Brain Computer Interface (BCI) system. We use Electroencephalography (EEG) signal processing methods for directional controls, which may be used for the handicapped for useful tool. This research uses the optical response of the stimulus of the color and frequency of light flashes as feedback to achieve directional acquisition. The electric pole locations include the frontal lobe and occipital lobe of the human brain. The EEG data is processed to filter the unwanted noise, and then the data is processed through Fast-Fourier-Transform (FFT) to observe the difference of the signals of different color and frequency. The FFT data is then analyzed through statistics, Principal Component Analysis (PCA) and Independent Component Analysis (ICA) methods to extract the features. In the end, the data is put into a Supper Vector Machine (SVM) of a machine learning to achieve directional acquisition. This research used 3 test subjects, which the best off-line classification rate could achieve 78.3% correctness, and the mean classification rate could achieve 75.4% correctness. Then, the on-line real time classifier accuracy could achieve 68.33% correctness, which has reached a preliminary practicability.

[1] S. Sutton, M. Braren, J. Zubin and E. R. John, “ Evoked-potential correlates of stimulus uncertainty,” Science 26 November 1965, vol.150, pp.1187-1188, 1965.
[2] 廖宇璁，「想像幾何旋轉動作與數學心算之腦電波分析」，國立台灣師範大學機電科技學系碩士論文，2009。
[3] G. Dornhege, J. d. R. Millán, T. Hinterberger, D. J. McFarland, and K. R. Müller, “Toward brain-computer interfacing,” Cambridge, Mass.: MIT Press., 2007.
[4] S. G. Mason and G. E. Birch, “A brain-controlled switch for asynchronous control applications,” IEEE Trans. Biomed. Eng., vol.47, pp.1297-1307, 2000.
[5] J. d. R. Millán and J. Mouriño, “Asynchronous BCI and local neural classifiers: an overview of the adaptive brain interface project,” IEEE Trans. Rehabil. Eng., vol.11, pp.159-161, 2003.
[6] http://ibru.vghtpe.gov.tw/chinese/eeg.htm
[7] R. Caton, “The electric currents of the brain,” British Medical Journal., vol.2, pp.278, 1875.
[8] H. Berger, “Über das elektrenkephalogramm des menschen,” European Archives of Psychiatry and Clinical Neuroscience., vol.87, pp.527-570, 1929.
[9] http://www.dls.ym.edu.tw/neuroscience/functional_c.htm
[10] http://faculty.washington.edu/chudler/functional.html
[11] H. Jasper, “Report of committee on methods of clinical exam in EEG,” Electroencephalogr. Clin. Neurophysiol., vol.10, pp.370-375, 1958.
[12] E. H. Chudler, “Neuroscience for kids,” available at the links for on-line courses at the author’s homepage at http://faculty.washington.edu/chudler/1020.html, 1996-2008.
[13] E. N. Meyer and F. L. da Silva, “Electroencephalography, chapter 32,” LippincottWilliams&Wilkins, pp.637–655, 1999.
[14] 李郁德，「圖象色彩組合對主觀偏好與辨識率之影響及腦波（EEG）評估」，國立台灣科技大學工業管理系碩士論文，2003。
[15] S. Sanei and J. A. Chambers, “EEG signal processing,” John Wiley & Sons, Ltd., 2007.
[16] J. Bhattacharya and H. Petsche,“Phase synchrony analysis of EEG during music perception reveals changes in functional connectivity due to musical expertise,” Signal Processing., vol.85, pp.2161-2177, 2005.
[17] J. R. Wolpaw, N. Birbaumer, W. J. Heetderks, D. J. McFarland, P. H. Peckham, G. Schalk, E. Donchin, L. A. Quatrano, C. J. Robinson, and T. M. Vaughan, “Brain-computer interface technology: A review of the first intenational meeting,” IEEE Transactions Rehabilitation Engineering, vol.8, pp.164-173, 2000.
[18] http://www.internetactu.net/wp-content/documents/bci_brainlab.jpg
[19] 高尚凱，「淺談腦機介面接口的發展現況及挑戰」，中國生物醫學工程學報，pp.1-6，2007。
[20] G. Townsend, B. Graimann, and G. Pfurtscheller., “Continuous eeg classification during motor imagery–simulation of an asynchronous bci,”IEEE Trans Neural Syst Rehabil Eng, vol.12, pp.258–65, 2004.
[21] 劉昀松，「結合多尺度主成分分析法與支持向量機在想像彩色圖像與中文文字之腦電波差異分析」，國立臺灣師範大學機電科技學系碩士論文，2010。
[22] M. Murugappan, M. Rizon, R. Nagarajan, S. Yaacob, D. Hazry, and I. Zunaidi, “Time-frequency analysis of EEG signals for human emotion detection,” Springer-Verlag Berlin Heidelberg., pp.262-265, 2008.
[23] A. Nigam, J. E. Hoffman, and R. F. Simons, “N400 to semantically anomalous pictures and words,” Journal of Cognitive Neuroscience Massachusetts Institute of Technology., vol.4, pp.15-22, 1992.
[24] 張菀珍，蔡俊明，葉榮木，「『是非題』作答之腦電波辨識與『選擇題』作答之腦電波分析」，Journal of Science and Engineering Technology, vol.5, pp.29-42, 2009.
[25] G. Bin, X. Gao and Y. Wang, B. Hong, ”VEP-Based Brain-Computer Interfaces: Time, Frequency, and Code Modulations,” IEEE Computational Intelligence Magazine, pp.22-26, 2009.
[26] 楊立才，李伯敏，李光林等，「腦機接口技術綜述」，電子學報，pp.1234-1241，2005。
[27] P.L. Lee, C.H. Wu, J.C. Hsieh and Y.T. Wu, “Visual evoked potential actuated brain computer interface: a brain-actuated cursor system,“ electronics letters, pp.832–834, 2005.
[28] P.L. Lee, C.H. Wu, J.C. Hsieh and Y.T. Wu, “Visual evoked potential actuated brain computer interface: a brain-actuated cursor system, “electronics letters, pp.832–834, 2005.
[29] E. NiederMeyer and F. L. da. Silval, “Electroencephalography, chapter 9,” LippincottWilliams&Wilkins., pp.149–173, 1999.
[30] A. Greco, N. Mammone, F. C. Morabito and M. Versaci, “Kurtosis, Renyi’s Entropy and Independent Component Scalp Maps for the Automatic Artifact Rejection from EEG data”, Journal of Information and Communication Engineering, pp.2-4, 2006.
[31] A. Delorme, S. Makeig, T. Sejnowski, “Automatic artifact rejection for EEG data using high-order statistics and independent component analysis,” Proceedings of the 3rd International Workshop on ICA, pp.457–462, 2001.
[32] C. Brunner, M. Naeem, R. Leeb, B. Graimann, G. Pfurtscheller, “Spatial filtering and selection of optimized components in four class motor imagery EEG data using independent components analysis,” Pattern Recognition Letters 28, pp.957-964, 2007.
[33] O. AlZoubi, I. Koprinska, and R. A. Calvo, “Classification of brain-computer interface Data,” Seventh Australasian Data Mining Conference., pp.9, 2008.
[34] R. C. Holte, “Very simple classification rules perform well on most commonly used datasets,” Machine Learning., vol.11, pp.63-91, 1993.
[35] J. R. Quinlan, “Induction of decision trees,” Machine Learning., vol.1, pp.81-106, 1986.
[36] D. W. Aha, D. Kibler, and M. K. Albert, “Instance-based learning algorithms,” Machine Learning., vol.6, pp.37-66, 1991.
[37] J. Moody, and C. J. Darken, “Fast learning in networks of locally-tuned processing units,” Neural computation., vol.1, pp.281-294, 1989.
[38] J. C. Platt, “Fast training of support vector machines using sequential minimal optimization,” Advances in kernel methods: support vector learning., pp.185-208, 1999.
[39] S. Le Cessie, and J. C. Van Houwelingen, “Ridge estimators in logistic regression,” Applied Statistics., vol.41, pp.191-201, 1992.
[40] Y. Freund, and R. E. Schapire, “Experiments with a new boosting algorithm,” Machine Learning: Proceedings of the Thirteenth International Conference., pp.1-9, 1996.
[41] L. Breiman, “Bagging Predictors, ”Machine Learning. vol.24, pp.123-140, 1996.
[42] D. H. Wolpert, “Stacked generalization,” Elsevier. Neural networks, vol.5, pp.231-259, 1992.
[43] L. Breiman, “Random Forests,” Machine Learning . vol.45, pp.5-32, 2001.
[44] 陳志瑋，「研究以小波神經網路作μ波即時鑑別」，國立成功大學機械工程學系碩士論文，2002。
[45] 林志穎，「數位音訊廣播系統中轉換器之電路設計」，國立成功大學電機工程學系碩士論文，2001。
[46] Aleix M. Martinez, and Avinash C.Kak, “PCA versus LDA,” IEEE trans on Pattern Analysis and Machine Intelligence, vol.23, pp.228-233, 2001
[47] 張志豪、陳鴻彬、陳柏琳，「資料導向縣性特徵轉換於中文大詞囊連續語音之辨識之應用」，2005年全國計算機會議，2005。
[48] T. W. Lee, M. S. Lewicki and T. J. Sejnowski, “ICA mixture models for unsupervised classification of non-gaussian classes and automatic context switching in blind signal separation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol.22, pp.1078-1089, 2000.
[49] R. Vigário, J. Särelä, V. Jousmäki, M. Hämäläinen, and E. Oja, “Independent Component Approach to the Analysis of EEG and MEG Recording,” IEEE Trans. Biomed. Eng., vol.47, pp.589-593, 2000.
[50] 吳維軒，「獨立成份分析法於即時心電訊號萃取應用」，國立中央大學電機工程研究所，2008。
[51] F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche, and B. Arnaldi, “A review of classification algorithms for EEG-based brain computer interface,” Journal of Neural Engineering., vol.4, pp.1-13, 2007.
[52] J. A. K. Suykens, and J. Vandewalle, “Least squares support vector machine classifiers,” Neural processing letters., vol.9, pp.293-300, 1999.
[53] A. Yoto, T. Katsuura, K. Iwanaga and Y. Shimomura, “Effects of Object Color Stimuli on Human Brain Activities in Perception and Attention Referred to EEG Alpha Band Response,” Journal of physiological anthropology, vol.26, pp.373-379, 2007.
[54] 許育財，「多通道腦電波量測系統之研製」，國立臺灣師範大學機電科技學系碩士論文，2008。
[55] Microchip, “MCP414X/416X/424X/426X Datasheet,” http://www.microchip.com, 2007.
[56] National Instruments, “Low-Cost Multifunction DAQ for USB,” http://www.ni.com, 2006.
[57] M. T. Al-maqtari, Z. Taha, and M. Moghavvemi, “Steady state-VEP based BCI for control gripping of a robotic hand,” Technical Postgraduates (TECHPOS), pp.1-3, 2009.