臺灣博碩士論文加值系統

近年來，有許多高速公路車禍發生的原因是由於駕駛者精神狀態不佳所造成。因此許多學者都在針對疲勞狀態偵測開發演算法。近年來，許多研究發現疲勞的特徵可以從眨眼的頻率、心跳頻率與腦波中萃取出來。這本研究中，我們開發一套基於腦波的量測與分析的疲勞偵測系統。而以腦波量測與分析的為主的系統最大的挑戰，就是腦波中的雜訊。腦波的量測與分析中不可避免的雜訊是眼動、眨眼、肌肉的雜訊、心跳的雜訊以及室電的干擾。而有研究證明獨立成分分析可以有效的移除多種的雜訊。然而大多數的獨立成份分析都是在電腦上進行離線分析而不是即時線上分析。
本論文以數位訊號處理器為基礎，搭配無線傳輸模組、生理訊號放大器，來實現一套即時可攜式無線腦機介面系統，包含三大發展主軸，分別為「腦電位訊號量測與無線傳輸」、「線上獨立成份分析演算法」及「即時疲勞狀態偵測與提醒演算法」。最後這套系統展示了連續並準確地偵測受測者的腦波中的精神狀態。

Many traffic accidents on the highway are caused by the driver’s inattention due to drowsiness. Hence, many researchers have devoted to develop algorithms to prevent drowsiness. Recently, several studies have shown that drowsiness related information is available in eye closures, heart rate and electroencephalogram (EEG). In this study, we developed a drowsiness detection system based on EEG recordings and data analysis. One of the biggest challenges in EEG-based system lies on the contaminations from inevitable EEG artifacts from eye movements, blinks, muscle, heart, and line noise. Independent component analysis (ICA) has been proven to be an effective technique to remove various types of artifacts. However, most of the ICA was performed offline on personal computers instead of an online analysis. Here, we design, develop and demonstrate an embedded wireless brain computer interface (BCI) including three functional blocks: EEG acquisition, amplification and wireless transmission, on-line ICA process and spectral analysis, and real-time drowsiness detection and feedback delivery to accurately and continuously detect and report subject drowsiness level based on the EEG data.

Abstract ii
中文摘 iii
致謝 iv
Table vii
Figure viii
Chapter 1 Introduction 1
1.1 Motivation and Problems 1
1.2 Organization of Thesis 2
1.3 Notation 3
Chapter 2 Background and Previous Works 6
2.1 Brain Computer Interface 6
2.2 Independent Component Analysis 8
2.3 Drowsiness Detection Methods 9
Chapter 3 Embedded Brain Computer Interface Architecture 11
3.1 System Overview 11
3.2 System Function Block 12
3.3 Requirement Analysis of Hardware and Software 14
3.3.1 EEG Acquisition Circuit 14
3.3.2 Wireless Transmitting Module 15
3.3.3 Embedded Digital Signal Processor 18
3.4 Development of the System 20
3.4.1 Boot Loader 20
3.4.2 Operating System 24
3.4.3 User Program 29
3.5 Optimization of the System 30
3.5.1 Floating Point to Integer 30
3.5.2 Programming 31
3.5.3 Variable Regulation 31
3.6 Comparison to Past BCI Systems 32
Chapter 4 On-line Independent Component Analysis Implementation 34
4.1 Independent Component Analysis 35
4.2 Rejecting Components with Standard Deviation 41
4.3 Testing 43
4.3.1 Testing of Artificial Mixed Data 43
4.3.2 Testing of Real EEG Data 45
Chapter 5 Experiment Designs and Results 48
5.1 The Experiment Design 48
5.1.1 Experimental Setup 48
5.2 Drowsiness Detection Algorithm 50
5.3 The Results 56
5.3.1 Result of 4 Channel Online EEG Analysis System 56
Chapter 6 Conclusions and Future Works 59
References 60

[1] M. Falkenstein, J. Hohnsbein, J. Hoormann, and L. Blanke, “Effects of crossmodal divided attention on late ERP components. II. Error processing in choice reaction tasks,” Electroencephalography and Clinical Neurophysiology, vol. 78, pp. 447-55, 1991.
[2] S. Makeig, M. Westerfield, T.P. Jung, S. Enghoff, J. Townsend, E. Courchesne, and T. J. Sejnowski, “Electroencephalographic Sources of Visual Evoked Responses,” Science, vol. 295, pp. 690-94, Jan. 25,2002.
[3] M.D. Serruya, N.G. Hatsopoulos, L. Paninski, M.R. Fellows, J.P. Donoghue, “Instant neural control of a movement signal,” Nature, vol. 416,pp. 141–142, 2002.
[4] D.M. Taylor, S.I. Tillery, A.B. Schwartz, “Direct cortical control of 3D neuroprosthetic devices,” Science, vol. 296, pp. 1829–1832, 2002.
[5] J.K. Chapin, K.A. Moxon, R.S. Markowitz, M.A.L. Nicolelis, “Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex,” Nature Neuroscience, vol. 2, pp. 664–670, 1999.
[6] J. Wessberg, C.R. Stambaugh, J.D. Kralik, P.D. Beck, M. Laubach, “Realtime prediction of hand trajectory by ensembles of cortical neurons in primates,” Nature, vol. 408, pp. 361–365, 2000.
[7] 連怡仲，“數位腦波機系統之設計與研製”，國立台灣大學，碩士論文，民國八十六年。
[8] 杜益昌，“多通道腦波機系統之設計與裝置”，國立台灣大學，碩士論文，民國八十七年。
[9] 林伯星，“數位腦波機系統之人機介面設計與研發”，國立台灣大學，碩士論文，民國八十七年。
[10] 蔡進寶，“腦波紀錄儀設計與製作”，國立交通大學，碩士論文，民國八十九年。
[11] 楊勝文，“可攜式智慧型腦波記錄器”，國立台灣大學，碩士論文，民國九十年。
[12] 黃名斌，“USB 介面之模組化腦波記錄儀”，國立中原大學，碩士論文，民國九十一年。
[13] 吳炎法，“研製無線化多通道腦波系統並運NAB 方法進行腦波分類之研究”，國立台北科技大學，碩士論文，民國九十二年。
[14] M. Cheng, X. Gao, S.K. Gao, and D.F. Xu, “Design and Implementation of a Brain-Computer Interface With High Transfer Rates,” IEEE Transaction biomedical Engineering, vol. 49, pp. 1181-1186, 2002.
[15] X.R, Gao, D.F. Xu, M. Cheng, and S.K. Gao, “A BCI-Based Environmental Controller for the Motion-Disabled,” IEEE Transaction Neural Systems and Rehabilitation Engineering, vol. 11, pp. 137-140, June 2003.
[16] B. Obermaier, “Design and implementation of an EEG based virtual keyboard using hidden Markov models”, Ph.D. dissertation, Technical University-Graz, Graz, Austria, 2001.
[17] B. Obermaier, C. Neuper, and G. Pfurtscheller, “Information transfer rate in a 5 classes brain-computer interface,” IEEE Transaction Rehabilitation Engineering, vol. 9, pp. 283-288, 2001.
[18] G.. Edlinger, G.. Krausz, F. Laundl, I. Niedermayer, C. Guger, “Architectures of Laboratory-PC and Mobile Pocket PC Brain-Computer Interfaces,” Proceedings of the 2 International IEEE EMBS Conference on Neural Engineering, Arlington, Virginia, March 16 - 19, 2005.
[19] K. Ashwin Whitchurch, B. Han Ashok, R. Vinod Kumaar and K. Sarukesi, and K. Vijay Varadan, “Wireless system for long term EEG monitoring of Absence Epilepsy,” Biomedical Applications of Micro- and Nanoengineering, Proceedings of SPIE, Vol. 4937, pp. 343-349, November 2002.
[20] Y.H. Nam, Z. Halm, Y.J. Chee, K.S. Park, “Development of Remote Diagnosis System Integrating Digital Telemetry for Medicine,” Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Hong Kong, China, October 29-November 1,1998.
[21] W.C. Kao, W.H. Chen, C.K. Yu, C.M. Hong, S.Y. Lin, “A real-time system for portable homecare applications,” Proceedings of IEEE International Symposium Consumer Electronics, Macau, June 14-16, 2005.
[22] F. Lamberti, C. Demartini, “Low-Cost Home Monitoring Using a Java-Based Embedded Computer,” Proceedings of the 4th Annual IEEE Conference on Information Technology Applications in Biomedicine, UK, April 24-26, 2003.
[23] T.P. Jung, S. Makeig, C. Humphries, T.W. Lee, M.J. McKeown,V. Iragui, and T.J. Sejnowski, “Removing Electroencephalographic Artifacts by Blind Source Separation,” Psychophysiology, vol. 37, pp. 163-178, 2000.
[24] T.P. Jung, S. Makeig, W. Westerfield, J. Townsend, E. Courchesne, and T.J. Sejnowski, “Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects,” Clinical Neurophysiology, vol. 111, pp. 1745-1758, 2000.
[25] S. Ulrika,” Blink behaviour based drowsiness detection: method development and validation”, Undergraduate dissertation, Linköping University, Department of Biomedical Engineering, 2004.
[26] B. Otman Adam, B. Jean Pierre, K. Fakhreddine, D. Kristopher, “Drowsiness detection system”, Intelligent Mechatronic Systems Iincorporated, 2004.
[27] S. Matsushita, A. Shiba, K. Nagashima, “A wearable fatigue monitoring system: application of human-computer interaction evaluation”, Proceedings of the 7th Australasian User interface conference, Hobart, Australia, 2006.
[28] C.T. Lin, R.C. Wu, S.F. Liang, W.H. Chao, Y.J. Chen, and T.P. Jung, “EEG-Based Drowsiness Estimation for Safety Driving Using Independent Component Analysis”, IEEE Transactions on Circuits and Systems—I: Regular Papers, vol. 52, pp. 2726-2738, December, 2005.
[29] C.T. Lin, L.W. Ko, I.F. Chung, T.Y. Huang, Y.C. Chen, T.P. Jung, and S.F. Liang, “Adaptive EEG-Based Alertness Estimation System by Using ICA-Based Fuzzy Neural Networks”, IEEE Transactions on Circuits and Systems—I: Regular Papers,vol. 53, pp. 2469-76, November, 2006.
[30] M. Jose. Carmena, A. Mikhail Lebedev, E. Roy. Crist, E. Joseph O'Doherty, M. David Santucci, F. Dragan Dimitrov, G.. Parag Patil, S. Craig Henriquez, A. L. Miguel Nicolelis, “Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates,” PLoS Biology, vol. 1, pp. 193-208, 2003
[31] J. Hendrix, “Fatal crash rates for tractor-trailers by time of day,” Proceedings of the International Truck and Bus Safety Research and Policy Symposium, Tennessee, USA, 2002
[32] H. Ueno, M. Kaneda, and M. Tsukino, “Development of drowsiness detection system,” Proceedings of Vehicle Navigation and Information Systems Conference, Yokohama, Japan, September, 1994.
[33] 黃冠智，“即時DSP嵌入式Linux系統開發及其在腦波訊號處理之應用”，國立交通大學，碩士論文，民國九十三年
[34] 高士政，“以雙核心處理器為基礎之即時嵌入式無線腦波訊號感測與處理系統及其在駕駛員瞌睡偵測之應用”，國立交通大學，碩士論文，民國九十四年。
[35] 謝弘義，“具備多工排程功能之無線嵌入式腦機介面系統及其在即時汽車駕駛員疲勞狀態偵測與提醒之應用”，國立交通大學，碩士論文，民國九十四年。
[36] Ancher technology inc., Wireless UART RF3100 module specification. Ancher
technology inc., Taipei County Taiwan R.O.C. [Online]. Available: http://www.ancher.com.tw/rf3100.html
[37] Ancher technology inc., Wireless UART RF3105 module specification. Ancher
technology inc., Taipei County Taiwan R.O.C. [Online]. Available: http://www.ancher.com.tw/rf3105.html
[38] Q. Li, C. Yao, “Real-Time Concepts for Embedded Systems,” CMP Books, 2003.
[39] Getting Started With Blackfin Processors Rev2.0, Analog Devices, Inc. Sep 2005.
[40] ADSP-BF533 Data Sheet Rev. D, Analog Devices, Inc., Sep 2006.
[41] ADSP-BF533 STAMP v1.2, Analog Devices, Inc.
[42] Blackfin Linux Docs, Open Source Software on the Blackfin Processor, Blackfin Linux Docs. [Online]. Available: http://docs.blackfin.uclinux.org/doku.php?id
[43] LinuxTMBlackfin, Welcome to the Blackfin Linux Project, LinuxTMBlackfin. [Online]. Available: http://blackfin.uclinux.org/
[44] ICA, Independent component analysis from Wikipedia, the free encyclopedia,
Independentcomponent analysis, Wikipedia. [Online]. Available: http://en.wikipedia.org/wiki/Independent_component_analysis
[45] A. Hyvärinen and E. Oja, “Independent Component Analysis: A Tutorial,” Neural Networks, 13(4-5):411-430, 2000.
[46] Lookup table, Lookup table from Wikipedia, the free encyclopedia, Lookup table, Wikipedia. [Online]. Available: http://en.wikipedia.org/wiki/Lookup_table
[47] Loop unwinding, Loop unwinding from Wikipedia, the free encyclopedia, Loop unwinding,Wikipedia. [Online]. Available:
http://en.wikipedia.org/wiki/Loop unrolling
[48] C. Guger, W. Harkam , C. Hertnaes, G. Pfurtscheller, “Prosthetic Control by an EEG-based Brain-Computer Interface (BCI)”, Proceedings of AAATE 5th European Conference for the Advancement of Assistive Technology. Düsseldorf, Germany, November 3-6, 1999.
[49] Negentropy , Negentropy from Wikipedia, the free encyclopedia, Negentropy ,
Wikipedia. [Online]. Available: http://en.wikipedia.org/wiki/Negentropy
[50] A. J. Bell and T. J. Sejnowski, “An informationmaximization approach to blind separation and blind deconvolution,” Neural Computation, Vol. 7, pp. 1129-1 159, 1995.
[51] J.F. Cardoso, “High-order contrasts for independent component analysis,” Neural Computation, Vol. 11, pp. 157-192, 1999.
[52] A. Hyvärinen, “Fast and Robust Fixed-Point Algorithms for Independent Component Analysis” IEEE Transactions on Neural Networks, Vol. 10, pp. 626-634, 1999.
[53] C. Jutten, J. Herault, “Blind separation of sources, Part 1: an adaptive algorithm based on neuromimetic architecture,” Signal Processing, Vol. 24, pp.1-10, 1991.
[54] P. Comon, C. Jutten, J. Herault, “Blind separation of sources, Part 2: problems statement,” Signal Processing, Vol. 24, pp.11-20, 1991.
[55] R. Linsker, “Improved local learning rule for information maximization and related applications,” Neural Networks, Vol. 18, pp.261-265, 2005.
[56] S. Amari, T.P. Chen, A. Cichocki, “Stability analysis of learning algorithms for blind source separation,” Neural Networks, Vol.10, pp.1345-1351, 1997.
[57] T.W. Lee, M. Girolami, T. J.Sejnowski, “Independent Component Analysis using an Extended Infomax Algorithm for Mixed Sub-Gaussian and Super-Gaussian Sources,” Neural Computation, Vol. 11, pp. 417-441, 1999.