近年來，腦機介面(Brain–computer interface, BCI)在穿戴式裝置發展之相關應用上成為熱門的研究主題。
功率頻譜分析與典型相關分析是應用於基於穩態視覺誘發電位腦機介面的主流方法，然而腦電波是一種不穩定的、非線性的以及容易受到外在雜訊所干擾的訊號，腦機介面的辨識準確率往往隨著時間窗縮短下降，而時間窗的長短會影響腦機介面的操作速度，因此如何兼顧腦機介面的操作速度與辨識準確率是該領域的重要課題。
本研究提出一個應用於基於穩態視覺誘發電位腦機介面的分類辨識演算法，結合了功率頻譜分析與典型相關分析，同時萃取腦電訊號時域與頻域上的特徵以增加特徵空間的資訊量，透過串級支持向量機改善短時間窗下非線性可分割資料群的辨識準確率，同時我們也提出一個訊號品質評估方法，去偵測當輸入訊號品質不佳、容易被分類器誤判時，取消該次判定結果以防止誤判的發生從而改善錯誤辨識，並且回饋一個警示提醒使用者提高專注度於下一輪的判定。
離線實驗結果顯示本研究提出之方法其辨識效能優異於傳統方法以及基於傳統改良的方法，四位受試者在時間窗三秒以上辨識五個目標的腦機介面可以達到平均八成以上的辨識準確率。

In recent years, Brain–computer interfaces (BCIs) have been widely studied and become popular research topics on the applications of many fields.
Power spectral density analysis (PSDA) and canonical correlation analysis (CCA) are two of the most widely used detection methods for SSVEP based brain computer interfaces. However, EEG signals are non-stationary, nonlinear and noisy so the recognition accuracy of a BCI usually decreases with time window getting shorter. And the length of time window is a tradeoff between recognition accuracy and operation speed for brain computer interfaces. Hence, it is an important issue to keep the brain computer interfaces with a high recognition accuracy when operated at short time window.
In this study, we propose to combine both PSDA and CCA for SSVEP feature extraction in order to increase the information in the feature space. Cascade support vector machine is applied to classification so as to improve the recognition accuracy at short time window. Moreover, we present a signal quality evaluation method that cancels the decision of the classifier when signal quality is low and prone to be misclassified. A feedback alarm would be given to the user in order to increase user’s attention when data, which was prone to be misclassified, was detected by signal quality evaluation unit. Making no decision could reduce the cost of making a wrong decision so as to improve the error rate.
Results show that our proposed method outperforms the standard CCA method in classifying SSVEP responses of five frequencies across four subjects. Above 80 % recognition accuracy is achieved when the time window is above three seconds.

中文摘要 i
Abstract ii
致謝 iii
目錄 iv
圖目錄 vi
表目錄 ix
第1章 緒論 1
1.1 前言 1
1.2 研究動機與目的 4
1.3 章節簡介 4
第2章 文獻回顧 5
2.1 穩態視覺誘發電位 5
2.2 基於穩態視覺誘發電位之腦機介面 12
2.3 分析方法 14
2.4 提出之方法 20
第3章 系統架構 22
3.1 系統規格 22
3.2 實驗環境與實驗方法 23
3.3 分類辨識演算法流程 32
3.4 訊號前處理 33
3.5 特徵擷取 36
3.6 特徵降維 37
3.7 分類 40
3.8 訊號品質評估 46
3.9 決策 48
3.10 交叉驗證 49
第4章 實驗結果與討論 50
4.1 效能評估參數 50
4.2 穩態視覺誘發電位反應 52
4.3 特徵離散度分析 55
4.4多頻段濾波 55
4.5 串級支持向量機分類器 58
4.6 訊號品質評估結果 65
4.7 表現比較與討論 70
第5章 結論與未來展望 73
參考文獻 75

[1] B. E. Swartz,“The advantages of digital over analog recording techniques,”Electroencephalography and Clinical Neurophysiology, vol. 106, no. 2, pp. 113–117, 1998.
[2] L. F. Haas,“Hans Berger (1873-1941), Richard Caton (1842-1926), and electroencephalography,”Journal of Neurology Neurosurgery and Psychiatry, vol. 74, no. 1, pp. 9, 2003.
[3] 關尚勇，林吉合，“破解腦電波＂，藝軒圖書出版社，24-36、74-128頁，民國九十一年。
[4] J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan,“Brain-computer interfaces for communication and control,＂Clin Neurophysiol, vol. 113, no. 6, pp. 767–791, Jun 2002.
[5] Ed Grabianowski,“How Brain-computer Interfaces Work,＂HowStuffWorks.com, http://computer.howstuffworks.com/brain-computer-interface1.htm, 2007.
[6] Y. M. Chi, Y. T. Wang, Y. Wang, C. Maier, T. P. Jung, and G. Cauwenberghs,“Dry and Noncontact EEG Sensors for Mobile Brain–Computer Interfaces,＂Neural Systems and Rehabilitation Engineering, IEEE Transactions on, vol. 20, no. 2, pp. 228–235, March 2012.
[7] R. Ortner, B. Z. Allison, G. Korisek, H. Gaggl, and G. Pfurtscheller,“An SSVEP BCI to Control a Hand Orthosis for Persons With Tetraplegia,＂Neural Systems and Rehabilitation Engineering, IEEE Transactions on, vol. 19, no. 1, pp. 1–5, Feb. 2011.
[8] P. L. Lee, H. C. Chang, T. Y. Hsieh, H. T. Deng, and C. W. Sun,“A Brain-Wave-Actuated Small Robot Car Using Ensemble Empirical Mode Decomposition-Based Approach,＂Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, vol. 42, no. 5, pp. 1053–1064, Sept. 2012.
[9] K. K. Shyu, Y. J. Chiu, P. L. Lee, M. H. Lee, J. J. Sie, C. H. Wu, Y. T. Wu, and P. C. Tung,“Total Design of an FPGA-Based Brain–Computer Interface Control Hospital Bed Nursing System,＂Industrial Electronics, IEEE Transactions on, vol. 60, no. 7, pp. 2731–2739, July 2013.
[10] W. O. Tatum,“Ellen R. Grass Lecture: Extraordinary EEG,＂Neurodiagnostic Journal, vol. 54, no. 1, pp. 3–21, 2014.
[11] G. Pfurtscheller,“Event-related synchronization (ERS): an electrophysiological correlate of cortical areas at rest,＂Electroencephalography and Clinical Neurophysiology, vol. 83, no. 1, pp. 62–69, July 1992.
[12] G. Pfurtscheller, and F. H. Lopes da Silva,“Event-related EEG/MEG synchronization and desynchronization: basic principles,＂Clinical Neurophysiology, vol. 110, no. 11, pp. 1842–1857, November 1999.
[13] L. Penghai, and W. Baikun,“A Study on EEG Alpha Wave-based Brain-Computer Interface Remote Control System,＂Mechatronics and Automation, 2007. ICMA 2007. International Conference on, pp. 3179–3184, Aug. 2007.
[14] K. K. Ang, C. Guan, K. S. Chua, B. T. Ang, C. Kuah, C. Wang, K. S. Phua, Z. Y. Chin, and H. Zhang,“A clinical study of motor imagery-based brain-computer interface for upper limb robotic rehabilitation,＂Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE, pp. 5981–5984, 3-6 Sept. 2009.

[15] C. Wang, K. S. Phua, K. K. Ang, C. Guan, H Zhang, R. Lin, K. S. G. Chua, B. T. Ang, and C. W. K. Kuah,“A feasibility study of non-invasive motor-imagery BCI-based robotic rehabilitation for Stroke patients,＂Neural Engineering, 2009. NER '09. 4th International IEEE/EMBS Conference on, pp. 271–274, April 29 2009-May 2 2009.
[16] T. W. Picton, S. Bentin, P. Berg, E. Donchin, S. A. Hillyard, R. Johnson, G. A. Miller, W. Ritter, D. S. Ruchkin, M. D. Rugg, and M. J. Taylor,“Guidelines for using human event-related potentials to study cognition: Recording standards and publication criteria,＂Psychophysiology, vol. 37, pp. 127–152, 2000.
[17] D. E. Linden,“The p300: where in the brain is it produced and what does it tell us?,＂Neuroscientist, vol. 11, no. 6, pp. 563–576, Dec. 2005.
[18] J. Polich,“Updating P300: an integrative theory of P3a and P3b.＂Clinical neurophysiology, vol. 118, no. 10, pp. 2128–2148, 2007.
[19] L. A. Farwell and E. Donchin,“Talking off the top of your head: Toward a mental prosthesis utilizing event-related brain potentials,＂Electroencephalogr. Clin. Neurophysiol., vol. 70, no. 6, pp.510–523, 1988.
[20] N. Chumerin, N. V. Manyakov, M. van Vliet, A. Robben, A. Combaz, and M. Van Hulle,“Steady-State Visual Evoked Potential-Based Computer Gaming on a Consumer-Grade EEG Device,＂Computational Intelligence and AI in Games, IEEE Transactions on, vol. 5, no. 2, pp. 100–110, June 2013.
[21] L. Bi, X. Fan, and Y. Liu,“EEG-Based Brain-Controlled Mobile Robots: A Survey,＂Human-Machine Systems, IEEE Transactions on, vol. 43, no. 2, pp.161–176, March 2013.
[22] C. Jia, X. Gao, B. Hong, and S. Gao,“Frequency and Phase Mixed Coding in SSVEP-Based Brain--Computer Interface,＂Biomedical Engineering, IEEE Transactions on, vol. 58, no. 1, pp.200–206, Jan. 2011.
[23] Y. Punsawad, and Y. Wongsawat,“Multi-command SSVEP-based BCI system via single flickering frequency half-field stimulation pattern,＂Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE , pp.1101–1104, Aug. 30 2011-Sept. 3 2011.
[24] Z. Yan, X. Gao, G. Bin, B. Hong, and S. Gao,“A half-field stimulation pattern for SSVEP-based brain-computer interface,＂Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE, pp. 6461–6464, 3-6 Sept. 2009.
[25] Y. Zhang , P. Xu , T. Liu , J. Hu , R. Zhang and D. Yao,“Multiple frequencies sequential coding for SSVEP-based brain computer interface,＂Plos One, vol. 7, no. 3, pp. e29519, 2012.
[26] K. K. Shyu, P. L. Lee, Y. J. Liu, and J. J. Sie,“Dual-frequency steady-state visual evoked potential for brain computer interface,＂Neurosci. Lett., vol. 483, no. 1, pp. 28–31, 2010.
[27] E. Yin, Z. Zhou, J. Jiang, Y. Yu, and D. Hu,“A dynamically optimized SSVEP brain-computer interface (BCI) speller,＂Biomedical Engineering, IEEE Transactions on, vol. PP, no. 99, pp. 1, 2014.
[28] Z. İşcan, and Z. Dokur,“A novel steady-state visually evoked potential-based brain–computer interface design: Character Plotter,＂Biomedical Signal Processing and Control, vol. 10, pp. 145–152, March 2014.
[29] A. Itai, and A. Funase,“Spectrum based feature extraction using spectrum intensity ratio for SSVEP detection,＂Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE, pp. 5947–5950, Aug. 28 2012-Sept. 1 2012.
[30] A. Luo and T. J. Sullivan,“A user-friendly SSVEP-based brain–computer interface using a time-domain classifier,＂J Neural Eng, vol. 7, no. 2, pp.26010, Apr. 2010.
[31] L. Zhao, P. Yuan, L. Xiao, Q. Meng, D. Hu, and H. Shen,“Research on SSVEP feature extraction based on HHT,＂Fuzzy Systems and Knowledge Discovery (FSKD), 2010 Seventh International Conference on , vol. 5, pp. 2220–2223, 10-12 Aug. 2010.
[32] Z. Zhang, X. Li, and Z. Deng,“A CWT-based SSVEP classification method for brain-computer interface system,＂Intelligent Control and Information Processing (ICICIP), 2010 International Conference on, pp.43–48, 13-15 Aug. 2010.
[33] S. N. Resalat, and S. K. Setarehdan,“A Study on the Effect of the Inter-Sources Distance on the Performance of the SSVEP-Based BCI Systems,＂Science and Academic Publishing, American Journal of Biomedical Engineering, vol. 2, no. 1, pp. 24–31, 2012.
[34] M. Borga,“Canonical Correlation a Tutorial,＂2001. [online] Available: http://people.imt.liu.se/magnus/cca
[35] Z. Lin, C. Zhang, W. Wu, and X. Gao,“Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs,＂Biomedical Engineering, IEEE Transactions on , vol. 54, no. 6, pp.1172–1176, June 2007.
[36] Y. Li, G. Bin, X. Gao, B. Hong, and S. Gao,“Analysis of phase coding SSVEP based on canonical correlation analysis (CCA),＂Neural Engineering (NER), 2011 5th International IEEE/EMBS Conference on, pp.368–371, April 27 2011-May 1 2011.

[37] Z. M. Zhang and Z. D. Deng,“A kernel canonical correlation analysis based idle-state detection method for SSVEP-based brain-computer interfaces,＂Proc. 2nd Int. Conf. on Material and Manufacturing Technology, vol. 341-342, pp. 634–640, 2012.
[38] Y. Zhang, G. X. Zhou, Q. B. Zhao, A. Onishi, J. Jin, X. Y. Wang and A. Cichocki,“Multiway canonical correlation analysis for frequency components recognition in SSVEP-based BCIs,＂Proc. 18th Int. Conf. on Neural Information Processing, pp. 287–295, 2011.
[39] J. Pan, X. Gao, F. Duan, Z. Yan and S. Gao,“Enhancing the classification accuracy of steady-state visual evoked potential- based brain-computer interfaces using phase constrained canonical correlation analysis,＂J. Neural. Eng., vol. 8, no. 3, pp. 036027, 2011.
[40] Y. Zhang, J. Jin, X. Y. Qing, B. Wang and X. Y. Wang,“LASSO based stimulus frequency recognition model for SSVEP BCIs,＂Biomed. Signal Process. Control, vol. 7, pp. 104–111, 2012.
[41] Q. Liu, K. Chen, Q. Ai, and S-Q Xie,“Recent Development of Signal Processing Algorithms for SSVEP-based Brain Computer Interfaces,＂Journal of Medical and Biological Engineering, vol. 34, no. 4, pp. 299–309, 2013.
[42] M. Smondrk, M. Vozda, J. Krohova, B. Hrvolova, T. Peterek, and M. Penhaker,“A comparison of success and time consumption of the most common methods for detection of the SSVEP,＂Telecommunications and Signal Processing (TSP), 2013 36th International Conference on, pp.657–661, 2-4 July 2013.
[43] Q. Wei, M. Xiao, and Z. Lu,“A Comparative Study of Canonical Correlation Analysis and Power Spectral Density Analysis for SSVEP Detection, ＂Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2011 International Conference on, vol. 2, pp. 7–10, 26-27 Aug. 2011.
[44] G. Bin, X. Gao, Z. Yan, B. Hong, and S. Gao,“An online multi-channel SSVEP-based brain–computer interface using a canonical correlation analysis method,＂J. Neural Eng., vol. 6, pp. 046002, 2009.
[45] Y. J. Wang, R. P. Wang, X. R. Gao, B. Hong, and S. K. Gao“A practical VEP-based brain--computer interface,＂IEEE Trans. Neural Syst. Rehabil. Eng., vol. 14, no. 2, pp.234–239, 2006.
[46] D. Zhu, J. Bieger, G. G. Molina, and R. M. Aarts“A survey of stimulation methods used in SSVEP-based BCIs,＂Comput. Intell. Neurosci., vol. 2010, pp.702357-1–702357-12, 2010.
[47] B. Zhang, J. Wang, and T. Fuhlbrigge,“A review of the commercial brain-computer interface technology from perspective of industrial robotics,＂Automation and Logistics (ICAL), 2010 IEEE International Conference on, pp.379–384, 16-20 Aug. 2010.
[48] A. T. Campbell,“NeuroPhone: Brain-Mobile Phone Interface Using a Wireless EEG Headset,＂Proc. 2nd ACM SIGCOMM Wksp. Networking, Sys., and Apps. on Mobile Handhelds, 2010.
[49] Y. Liu, X. Jiang, T. Cao, F. Wan, P-U Mak, P-I Mak, and M-I Vai,“Implementation of SSVEP based BCI with Emotiv EPOC,＂Virtual Environments Human-Computer Interfaces and Measurement Systems (VECIMS)＂2012 IEEE International Conference on, pp.34–37, 2-4 July 2012.
[50] H. Ekanayake,“P300 and Emotiv EPOC: Does Emotiv EPOC capture real EEG?,＂http://neurofeedback.visaduma.info/emotivresearch.htm, Oct. 2011.
[51] L. Yang and H. Leung,“An online BCI game based on the decoding of users' attention to color stimulus,＂Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, pp. 5267–5270, 3-7 July 2013.
[52] C. M. Bishop, Neural Networks for Pattern Recognition, New York, NY, USA: Oxford University Press, Inc., 1995.
[53] C. C. Burges,“A Tutorial on Support Vector Machines for Pattern Recognition, ＂in Proceeds of Int. Conference on Data Mining and Knowledge Discovery, vol. 2, pp. 121–167, 1988.
[54] R. Fletcher, Practical methods of optimization (2nd ed.), Wiley-Interscience, New York, NY, 1987.
[55] B. Scholkopf, and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press, Cambridge, MA, 2002.
[56] R. O. Duda, P. E. Hart, and D. G. Stork,Pattern Classification,Wiley InterScience, New York, 2002.
[57] C. McCormick, K-Fold Cross-Validation, With MATLAB Code, Computer Vision and Machine Learning Projects and Tutorials, 2013 https://chrisjmccormick.wordpress.com/2013/07/31/k-fold-cross-validation-with-matlab-code/
[58] T. M. Vaughan,“Guest editorial brain-computer interface technology: a review of the second international meeting,＂Neural Systems and Rehabilitation Engineering, IEEE Transactions on, vol. 11, no. 2, pp. 94–109, June 2003.
[59] C. Zhang, Y. Kimura, H. Higashi, T. Tanaka,“A simple platform of brain-controlled mobile robot and its implementation by SSVEP,＂Neural Networks (IJCNN), The 2012 International Joint Conference on, pp. 1–7, 10-15 June 2012.
[60] Y. Zhang, P. Xu, K. Cheng, and D. Yao,“Multivariate synchronization index for frequency recognition of SSVEP-based brain-computer interface,＂Journal of Neuroscience Methods, vol. 221, pp. 32–40, 15 January 2014.