博碩士論文 92541009 詳細資訊




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姓名 李銘寰(Ming-Huan Lee)  查詢紙本館藏   畢業系所 電機工程學系
論文名稱 以場可程式邏輯閘陣列為基礎之穩態視覺誘發電位腦波人機介面系統設計與實現
(Design and Implementation of FPGA-Based Steady-State Visual Evoked Potential Brain–Computer Interface System)
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摘要(中) 本論文提出一低單價以場可程式邏輯閘陣列(FPGA)為基礎之大腦人機介面(BCI)系統,有別於其它相關研究採用腦電波(EEG)訊號擷取儀器與個人電腦等相關周邊儀器設備。本論文所提之低單價大腦人機介面系統主要包含有:1)以發光二極體(LED)為基礎之閃光誘發面板、2)腦電波訊號擷取電路、與3)以場可程式邏輯閘陣列為基礎之腦電波訊號處理單元。論文所提系統具有即時腦電波訊號處理之能力,並且以穩態視覺誘發電位(SSVEP)作為大腦人機介面之輸入訊號。使用發光二極體能較傳統陰極射線管(CRT)與液晶平面顯示器(LCD)誘發出較強的穩態視覺誘發電位。為了提高腦電波訊號量測的正確性與穩定性並降低環境相關因數等干擾,自製之腦電波訊號擷取電路具有自動停帶調整控制與自動輸出增益/準位調整控制等功能。為了使用較少的閃光誘發頻率來實現多個穩態視覺誘發選項,腦電波訊號處理單元採用單頻多相位編碼技術驅動閃光誘發面板以及辨識所擷取到的穩態視覺誘發電位。透過本系統,使用者可以只藉由其自身腦電波訊號來控制周邊多媒體等裝置。論文所實現之大腦人機介面系統其平均傳輸率(ITR)可達 24.67 位元/秒,並且由實驗結果可知所提之系統具有高達 89.29% 的腦電波訊號辨識正確率。
摘要(英) This dissertation proposes a low-cost field-programmable gate array (FPGA)-based brain–computer interface (BCI) multimedia control system, different from the BCI system, which uses bulky and expensive electroencephalography (EEG) measurement equipment, personal computer, and commercial real-time signal-processing software. The proposed system combines a customized stimulation panel, a brainwave-acquisition circuit, and an FPGA-based real-time signal processor and allows users to use their brainwave to communicate with or control multimedia devices by themselves. This study also designs a light-emitting diode (LED) stimulation panel instead of cathode ray tube (CRT) or liquid-crystal display (LCD) used in existing studies, to induce a stronger steady-state visual evoked potential (SSVEP), a kind of EEG, used as the input signal of the proposed BCI system. Implementing a prototype of the SSVEP-based BCI multimedia control system verifies the effectiveness of the proposed system. The proposed system has reached an average transfer rate about 24.67 bits/min for normal subjects. Experimental results show that the subjects’ SSVEP can successfully control the multimedia device through the proposed BCI system with high identification accuracy.
關鍵字(中) ★ 場可程式邏輯閘陣列
★ 穩態視覺誘發電位
★ 大腦人機介面
★ 浮點運算單元
關鍵字(英) ★ Field-programmable gate array (FPGA)
★ Floating-point
★ Brain–computer interface (BCI)
★ Steady-state visual-evoked potential (SSVEP)
論文目次 摘要 I
ABSTRACT II
LIST OF FIGURES VI
LIST OF TABLES X
ABBREVIATION XI
CHAPTER 1 INTRODUCTION 1
1.1. Background and Motivation 1
1.2. Objectives of Dissertation 2
1.3. Survey of Previous Work 2
1.4. Organization of Dissertation 4
CHAPTER 2 BRAIN–COMPUTER INTERFACE 5
2.1. Brain–Computer Interface 5
2.1.1 Electroencephalogram 8
2.1.2 Invasive BCI and Non-Invasive BCI 10
2.1.3 Artifacts 10
2.2. SSVEP-Based Brain–Computer Interface 11
2.2.1 Frequency Coding 13
2.2.2 Phase Coding 14
2.3. Summary 17
CHAPTER 3 MATERIALS AND METHODS 18
3.1. SSVEP Signal Acquisition and Pre-Processing 18
3.1.1 Electrodes 18
3.1.2 Pre-Amplifier 19
3.1.3 Band-Pass Filter 23
3.1.4 Notch Filter 27
3.1.5 Post-Amplifier and Output Adjustment 31
3.1.6 Automatic Notch Filter and Output Adjustment Control 32
3.1.7 SSVEP Signal Acquisition and Pre-Processing PCB 36
3.2. FPGA Hardware Implementation for SSVEP Signal Processing 37
3.2.1 FPGA Design Flow 38
3.2.2 Floating-Point Arithmetic Units 40
3.2.3 IIR Band-Pass Filter 44
3.2.4 Signal Averaging Process 46
3.2.5 Phase Identification 47
3.3. Summary 48
CHAPTER 4 DEVELOPMENT OF FPGA-BASED SSVEP BCI MULTIMEDIA CONTROL SYSTEM 49
4.1. Hardware Design and Implementation 50
4.1.1 System Configuration 50
4.1.2 Visual Stimulation 51
4.1.3 SSVEP Acquisition Circuit 54
4.1.4 ADC Module Board 56
4.1.5 SSVEP Signal Processor 57
4.1.6 Other Circuits 65
4.2. Experimental Verifications 68
4.2.1 Experiments of Automatic Notch Filter Control 70
4.2.2 Experiments of Automatic Post-Amplifier and Output Adjustment Control 73
4.2.3 Experiment of SSVEP Acquisition Module Board 76
4.2.4 Experiment of Proposed System 77
4.3. Summary 80
CHAPTER 5 CONCLUSIONS 82
5.1. Conclusions 82
5.2. Future Work 83
REFERENCES 84
LIST OF PUBLICATIONS 91
VITA 93
參考文獻 [1] T. Nishimura, M. Nakashige, T. Akashi, Y. Wakasa, and K. Tanaka, “Eye interface for physically impaired people by genetic eye tracking,” in Proc. Annual Conference on Society of Instrumentation and Control Engineers, Sep. 2007, pp. 828–833.
[2] Z. O. Abu-Faraj, M. J. Mashaalany, H. C. Sleiman, J. L. Heneine, and W. M. Al Katergi, “Design and development of a low-cost eye tracking system for the rehabilitation of the completely locked-in patient,” in Proc. IEEE Annual International Conference on Engineering in Medicine and Biology Society, Aug. 2006, pp. 4905–4908.
[3] Y. L. Chen, “Application of tilt sensors in human–computer mouse interface for people with disabilities,” IEEE Trans. Neural Systems Rehabilitation Engineering, vol. 9, no. 3, pp. 289–294, Sep. 2001.
[4] J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, “Brain–computer interfaces for communication and control,” Clinical neurophysiology, vol. 113, no. 6, pp. 767–791, Jun. 2002.
[5] E. E. Sutter, “The brain response interface: Communication through visually-induced electrical brain responses,” Journal of Microcomputer Applications, vol. 15, no. 1, pp. 31–45, Jan. 1992.
[6] D. Regan, Human Brain Electrophysiology: Evoked Potentials and Evoked Magnetic Fields in Science and Medicine, New York: Elsevier, 1989.
[7] M. Cheng, X. R. Gao, S. K. Gao, and D. F. Xu, “Design and implementation of a brain–computer interface with high transfer rates,” IEEE Trans. Biomedical Engineering, vol. 49, no. 10, pp. 1181–1186, Oct. 2002.
[8] 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 Systems Rehabilitation Engineering, vol. 14, no. 2, pp. 234–239, Jun. 2006.
[9] G. R. Müller-Putz and G. Pfurtscheller, “Control of an electrical prosthesis with an SSVEP-based BCI,” IEEE Trans. Biomedical Engineering, vol. 55, no. 1, pp. 361–364, Jan. 2008.
[10] O. Friman, T. Luth, I. Volosyak, and A. Graser, “Spelling with steady-state visual evoked potentials,” in Proc. International IEEE Engineering Medicine Biology Society Conference Neural Engineering, May 2007, pp. 354–357.
[11] D. Valbuena, M. Cyriacks, O. Friman, I. Volosyak, and A. Graser, “Brain–computer interface for high-level control of rehabilitation robotic systems,” in Proc. IEEE 10th International Conference Rehabilitation Robotics, Jun. 2007, pp. 619–625.
[12] C. Jia, H. L. Xu, B. Hong, X. R. Gao, Z. G. Zhang, and S. K. Gao, “A human–computer interface using SSVEP-based BCI technology,” Foundations Augmented Cognition, vol. 4565, pp. 113–119, Jul. 2007.
[13] X. R. Gao, D. F. Xu, M. Cheng, and S. K. Gao, “A BCI-based environmental controller for the motion-disabled,” IEEE Trans. Neural Systems Rehabilitation Engineering, vol. 11, no. 2, pp. 137–140, Jun. 2003.
[14] P. L. Lee, J. J. Sie, Y. J. Liu, C. H. Wu, M. H. Lee, C. H. Shu, P. H. Li, C. W. Sun, and K. K. Shyu, “An SSVEP-actuated brain–computer interface using phase-tagged flickering sequences: A cursor system,” Annals Biomedical Engineering, vol. 38, no. 7, pp. 2383–2397, Jul. 2010.
[15] Y. J. Wang, X. R. Gao, B. Hong, C. A. Jia, and S. K. Gao, “Brain–computer interfaces based on visual evoked potentials,” IEEE Engineering Medicine Biology Magazine, vol. 27, no. 5, pp. 64–71, Sep./Oct. 2008.
[16] T. Kluge and M. Hartmann, “Phase coherent detection of steady-state evoked potentials: Experimental results and application to brain–computer interfaces,” in Proc. IEEE Annual International Conference Engineering Medicine Biology Society, May 2007, pp. 425–429.
[17] R. Scherer, G. R. Müller, C. Neuper, B. Graimann, and G. Pfurtscheller, “An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate,” IEEE Trans. Biomedical Engineering, vol. 51, no. 6, pp. 979–984, Jun. 2004.
[18] B. Blankertz, M. Krauledat, G. Dornhege, J. Williamson, R. Murray-Smith, and K.-R. Müller, “A note on brain actuated spelling with the Berlin brain–computer interface,” Lecture Notes in Computer Science, vol. 4555, pp. 759–768, Jul. 2007.
[19] J. D. Bayliss, “Use of the evoked potential P3 component for control in a virtual apartment,” IEEE Trans. Neural Systems Rehabilitation Engineering, vol. 11, no. 2, pp. 113–116, Jun. 2003.
[20] B. Rebsamen, E. Burdet, C. Guan, H. Zhang, C. L. Teo, Q. Zeng, M. Ang, and C. Laugier, “A brain-controlled wheelchair based on P300 and path guidance,” in Proc. IEEE/RAS-EMBS International Conference Biomedical Robotics Biomechatronics, Feb. 2006, pp. 1101–1106.
[21] J. Millan, F. Renkens, J. Mourino, and W. Gerstner, “Noninvasive brain-actuated control of a mobile robot by human EEG,” IEEE Trans. Biomedical Engineering, vol. 51, no. 6, pp. 1026–1033, Jun. 2004.
[22] D. Taylor, S. Tillery, and A. Schwartz, “Direct cortical control of 3D neuroprosthetic devices,” Science, vol. 296, no. 5574, pp. 1829–1832, 2002.
[23] E. C. Lalor, S. P. Kelly, C. Finucane, R. Burke, R. Smith, R. B. Reilly, and G. McDarby, “Steady-state VEP-based brain–computer interface control in an immersive 3D gaming environment,” EURASIP Journal on Applied Signal Processing, vol. 2005, no. 19, pp. 3156–3164, 2005.
[24] R. Leeb, C. Keinrath, D. Friedman, C. Guger, R. Scherer, C. Neuper, M. Garau, A. Antley, A. Steed, and M. Slater, “Walking by thinking: The brainwaves are crucial, not the muscles!,” Presence: Teleoperators and Virtual Environments, vol. 15, no. 5, pp. 500–514, Oct. 2006.
[25] P. R. Kennedy, R. A. E. Bakay, M. M. Moore, K. Adams, and J. Goldwaithe, “Direct control of a computer from the human central nervous system,” IEEE Trans. Rehabilitation Engineering, vol. 8, no. 2, pp. 198–202, Jun. 2000.
[26] M. J. Black, E. Bienenstock, J. P. Donoghue, M. Serruya, W. Wu, and Y. Gao, “Connecting brains with machines: the neural control of 2D cursor movement,” in Proc. First International IEEE EMBS Conference Neural Engineering, 2003, pp. 580–583.
[27] E. Donchin, K. M. Spencer, and R. Wijensinghe, “The mental prosthesis: assessing the speed of a P300-based brain–computer interface,” IEEE Trans. Rehabilitation Engineering, vol. 8, no. 2, pp. 174–179, Jun. 2000.
[28] J. V. Odom, M. Bach, C. Barber, M. Brigell, M. F. Marmor, A. P. Tormene, G. E. Holder, and Vaegan, “Visual evoked potentials standard,” Documenta Ophthalmologica, vol. 108, no. 2, pp. 115–123, 2004.
[29] G. Pfurtscheller, C. Neuper, D. Flotzinger, and M. Pregenzer, “EEG-based discrimination between imagination of right and left hand movement,” Electroencephalography clinical neurophysiology, vol. 103, no. 6, pp. 642–651, Dec. 1997.
[30] G. Pfurtscheller, C. Neuper, C. Guger, W. Harkam, H. Ramoser, A. Schlögl, B. Obermaier, and M. Pregenzer, “Current trends in graz brain–computer interface (BCI) research,” IEEE Trans. Rehabilitation Engineering, vol. 8, no. 2, pp. 216–219, Jun. 2000.
[31] W. D. Penny, S. J. Roberts, E. A. Curran, and M. J. Stokes, “EEG-based communication: A pattern recognition approach,” IEEE Trans. Rehabilitation Engineering, vol. 8, no. 2, pp. 214–215, Jun. 2000.
[32] J. R. Wolpaw, D. J. McFarland, G. W. Neat, and C. A. Forneris, “An EEG-based brain–computer interface for cursor control,” Electroencephalography and Clinical Neurophysiology, vol. 78, no. 3, pp. 252–259, Mar. 1991.
[33] J. R. Wolpaw and D. J. McFarland, “Multichannel EEG-based brain–computer communication,” Electroencephalography and Clinical Neurophysiology, vol. 90, no. 6, pp. 444–449, Jun. 1994.
[34] J. R. Wolpaw, D. J. McFarland, and T. M. Vaughan, “Brain–computer interface research at the Wadsworth center,” IEEE Trans. Rehabilitation Engineering, vol. 8, no. 2, pp. 222–226, Jun. 2000.
[35] N. Birbaumer, A. Kübler, N. Ghanayim, T. Hinterberger, J. Perelmouter, J. Kaiser, I. Iversen, B. Kotchoubey, N. Neumann, and H. Flor, “The thought translation device (TTD) for completely paralyzed patients,” IEEE Trans. Rehabilitation Engineering, vol. 8, no. 2, pp. 190–193, Jun. 2000.
[36] B. Blankertz, K. R. Müller, G. Curio, T. M. Vaughan, G. Schalk, J. R. Wolpaw, A. Schlögl, C. Neuper, G. Pfurtscheller, T. Hinterberger, M. Schröder, and N. Birbaumer, “The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials,” IEEE Trans. Biomedical Engineering, vol. 51, no. 6, pp. 1044–1051, Jun. 2004.
[37] S. P. Levine, J. E. Huggins, S. L. BeMent, R. K. Kushwaha, L. A. Schuh, M. M. Rohde, E. A. Passaro, D. A. Ross, K. V. Elisevich, and B. J. Smith, “A direct brain interface based on event-related potentials,” IEEE Trans. Rehabilitation Engineering, vol. 8, no. 2, pp. 180–185, Jun. 2000.
[38] P. Berg and M. Scherg, “A multiple source approach to the correction of eye artifacts,” Electroencephalography and clinical Neurophysiology, vol. 90, no. 3, pp. 229–241, Mar. 1994.
[39] R. N. Vigario, “Extraction of ocular artifacts from EEG using independent component analysis,” Electroencephalography and clinical Neurophysiology, vol. 103, no. 3, pp. 395–404, 1997.
[40] R. Spehlmann, Evoked potential primer. Boston, MA: Butterworth, 1985.
[41] Burr-Brown, Precision, low power instrumentation amplifiers data sheet, 2005. [Online]. Available: http://focus.ti.com/lit/ds/symlink/ina128.pdf
[42] Analog Devices, 1-/2-/4-Channel Digital Potentiometers data sheet, 2005. [Online]. Available: http://www.analog.com/static/imported-files/data_sheets/AD8400_8402_8403.pdf
[43] J. J. Rodriguez-Andina, M. J. Moure, and M. D. Valdes, “Features, design tools, and application domains of FPGAs,” IEEE Trans. Industrial Electronics, vol. 54, no. 4, pp. 1810–1823, Aug. 2007.
[44] E. Monmasson and M. N. Cirstea, “FPGA design methodology for industrial control systems–A review,” IEEE Trans. Industrial Electronics, vol. 54, no. 4, pp. 1824–1842, Aug. 2007.
[45] R. X. Chen, L. G. Chen, and L. Chen, “System design consideration for digital wheelchair controller,” IEEE Trans. Industrial Electronics, vol. 47, no. 4, pp. 898–907, Aug. 2000.
[46] T. N. Chang, B. Cheng, and P. Sriwilaijaroen, “Motion control firmware for high-speed robotic systems,” IEEE Trans. Industrial Electronics, vol. 53, no. 5, pp. 1713–1722, Oct. 2006.
[47] K. Sridharan and T. K. Priya, “The design of a hardware accelerator for real-time complete visibility graph construction and efficient FPGA implementation,” IEEE Trans. Industrial Electronics, vol. 52, no. 4, pp. 1185–1187, Aug. 2005.
[48] H. Abu-Rub, J. Guzinski, Z. Krzeminski, and H. A. Toliyat, “Predictive current control of voltage-source inverters,” IEEE Trans. Industrial Electronics, vol. vol. 51, no. 3, pp. 585–593, Jun. 2004.
[49] M. Aime, G. Gateau, and T. Meynard, “Implementation of a peak current control algorithm within a field programmable gate array,” IEEE Trans. Industrial Electronics, vol. 54, no. 1, pp. 406–418, Feb. 2007.
[50] IEEE Standard for Binary Floating Point Arithmetic, ANSI/IEEE Standard 745-1985, Aug. 1985.
[51] N. Shirazi, A. Walters, and P. Athanas, “Quantitative analysis of floating point arithmetic on FPGA based custom computing machines,” in Proc. IEEE Symposium on FPGAs for Custom Computing Machines, 1995, pp. 155–162.
[52] K. K. Shyu, M. H. Lee, Y. T. Wu, and P. L. Lee, “Implementation of pipelined FastICA on FPGA for real-time blind source separation,” IEEE Trans. Neural Networks, vol. 19, no. 6, pp. 958–970, Jun. 2008.
[53] Zhenghua Wu, Yongxiu Lai, Yang Xia, Dan Wu, and Dezhong Yao, “Stimulator selection in SSVEP-based BCI,” Medical Engineering & Physics, vol. 30, no. 8, pp. 1079–1088, Oct. 2008.
[54] MicroChip, MCP3201 2.7V 12-bit A/D converter with SPI serial interface data sheet, 2008. [Online]. Available: http://ww1.microchip.com/downloads/en/DeviceDoc/21290e.pdf
[55] “Cyclone II Device Handbook,” Altera, 2008.
[56] “Quartus II Version 5.0 Handbook,” Altera, 2005.
[57] J. J. Sie, “Implementation of a high-performance steady-state visual evoked potential (SSVEP)-based brain computer interface using frequency and phase encoding flash lights,” M.S. dissertation, National Central University, Jhong-Li, Taoyuan, Taiwan, Jul. 2007.
[58] MicroChip, 25LC1024 1 Mbit SPI Bus Serial EEPROM data sheet, 2008. [Online]. Available: http://ww1.microchip.com/downloads/en/DeviceDoc/22064C.pdf
[59] MicroChip, MCP4921 12-Bit DAC with SPI™ interface data sheet, 2007. [Online]. Available: http://ww1.microchip.com/downloads/en/DeviceDoc/21897B.pdf
[60] National Semiconductor, LM386 Low voltage audio power amplifier data sheet, 2000. [Online]. Available: http://www.national.com/ds/LM/LM386.pdf
[61] S. P. Kelly, E. C. Lalor, R. B. Reilly, and J. J. Foxe, “Visual spatial attention tracking using high-density SSVEP data for independent brain–computer communication,” IEEE Trans. Neural Systems Rehabilitation Engineering, vol. 13, no. 2, pp. 172–178, Jun. 2005.
指導教授 徐國鎧、李柏磊
(Kuo-Kai Shyu、Po-Lei Lee)
審核日期 2010-7-20
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