想像運動腦電波之偵測與二元分類

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姓名

林世國(Shih-Kuo Lin) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

想像運動腦電波之偵測與二元分類
(Detection and Binary Classification of Motor Imagery Electroencephalography)

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摘要(中)

大腦人機界面主要目標在於偵測，擷取及分析與事件相關的腦電波，並以此腦波作為觸發訊號來控制週遭的裝置以達到溝通的目的。本論文主要在於發展一套演算法來偵測發生想像運動的那一秒鐘並對此想像運動進行分類（左手食指或右手食指上抬）。而我們的受試者均為第一次參與這類實驗且在實驗之前僅接受不到10分鐘的訓練。首先利用模列小波轉換來估算每位受試者適合想像運動的腦波頻率範圍。每位受試者適當的特殊頻率範圍將用來作為對腦電波進行帶通濾波的依據，並將濾波後的腦電波經振幅調變分析法獲得其包絡線。同時設計一個長度為一秒的移動視窗掃瞄每次試驗，利用共同空間樣本的方法來偵測有發生想像運動的時間區段，接著利用主要成份分析法搭配線性區分分析法對想像右手或左手食指上抬進行分類。我們邀請四位健康的受試者參與實驗，受試者1、2、3及4分類的準確度分別為73.13%、71.46%、91.15%及80.51%。整體的偵測率及分類率為90.27% 1%及87.27%。我們同時利用軟體CURRY（版本 4.6）對神經活動的位置及強度進行定位以證明我們演算法偵測及分類的結果。訊號源定位的結果證明了所估測的訊號源的確位於與手指上抬相關的運動皮質區內。

摘要(英)

The goal of brain computer interface（BCI）is to detect, extract and analyze the event-related brain waves which can be used as trigger signals to control peripheral device for communication. This work aims at developing an algorithm to detect the imagery finger movement within an one-second time interval and classify the right or left imagery finger lifting performed by subjects who were naive to the experiments and trained less than 10 minutes prior to the experiments. The Morlet wavelet transform was first employed to estimate a suitable frequency band of imagery movement for each subject. The subject-specific frequency band was used to bandpass filter the EEG data and extract the envelop of reactivity via Amplitude Modulation（AM）method for subsequent analysis. As one-second sliding window was designed to scan each epoch, the CSP method was applied to detect the time interval of imagery finger movement and then the PCA as well as LDA were utilized to classify the imagery right or left index finger lifting. Four healthy subjects are invited to participate in our experiment. The classification rates of subject 1, 2, 3, and 4 are 73.13%, 71.46%, 91.15%, and 80.51%, respectively. The overall detection rate and classification rate were 90.27% 1% and 87.27%, respectively. We also utilized the software CURRY (version 4.6) to localize the neural activities and strengths for verifying the detection and classification results. The result of source localization demonstrates that the estimated sources were localized within sensorimotor area corresponding to finger lifting.

關鍵字(中)

★ 大腦人機介面
★ 腦電波
★ 共同樣本空間
★ 訊號源定位
★ 主要成份分析法
★ 線性區分分析法

關鍵字(英)

★ Principle Component Analysis
★ electroencephalography（EEG）
★ Common Spatial Patterns
★ source localization
★ Brain Computer Interface
★ Linearly Discriminant Analysis

論文目次

Chapter 1 Introduction 1
1.1 Motivation and Goal 1
1.2 Literature Review 2
1.2.1 Brain Waves 2
A. Event-Related Potential 4
B. Event-Related Desynchronization (ERD) and
Event-Related Synchronization (ERS) 4
1.2.2 Electroencephalography (EEG) Montage 4
1.2.3 Brain Computer Interface (BCI) 5
A. Using Steady State Visual-evoked Response
as the Control Signal 6
B. Using P300 as the Control Signal 7
C. Using Slow Cortical Potential as the Control
Signal 7
D. Using the Sensorimotor Rhythm as the
Control Signal 8
Chapter 2 Materials and Methods 10
2.1 Materials 10
2.1.1 Subjects 11
2.1.2 Equipments 11
2.1.3 Data Acquisition 12
2.2 Methods 14
2.2.1 Quantification of Event-Related EEG
Synchronization 15
A. Amplitude Modulation Method 15
2.2.2 Morlet Wavelet Transform 16
2.2.3 Detection of Finger Lifting 21
A. Common Spatial Patterns（CSP） 21
B. Computation of the Projection Matrix P 24
C. Movement Detection within Good Epochs 26
2.2.4 Classification of Right or Left Finger Lifting 28
A. Principle Component Analysis（PCA） 28
B. Linear Discriminant Analysis（LDA） 30
2.2.5 Source Localization and ICA Pre-processing 33
Chapter 3 Results 37
3.1 Motor Imagery Movement Experiment 37
3.2 Source Localization 39
Chapter 4 Discussion 51
4.1 Number of Channels Used in CSP 51
4.2 Selection of the Subject-Specific Frequency 51
4.3 A Comparison with the work of Guger’s group 52
Chapter 5 Conclusions 55
References 57

參考文獻

1. A. Schloegl, D. Flotzinger, and G. Purtcheller. "Adaptive autoregressive modeling used for single-trial EEG classification," Biomed. Tech., vol. 42, pp. 162-167, 1997.
2. B. J. Fisch. "Fisch & Spehlmann’s EEG Primer," -3rd ed. Elsevier, p. 4-5, 1999.
3. B. D. Mensh, J. Werfel, and H. S. Seung, "BCI Competition 2003—Data Set Ia: Combining Gamma-Band Power With Slow Cortical Potentials to Improve Single-Trial Classification of Electroencephalographic Signals, " IEEE Trans. Biomed. Eng., vol. 51, Issue. 6, pp. 1052-1056, June 2004.
4. C. Patrice, J. M. Fontbonne, and P. Etevenon. "A new method for quantifying EEG event-related desynchronization: amplitude envelope analysis," Electroencephalography & Clinical Neuro- physiology. 98:126-129, 1996.
5. C. Neuper, G. R. Muller, A. Kubler, N. Birbaumer, G. Pfurtscheller. "Clinical application of an EEG-based brain-computer interface: a case study in a patient with severe motor impairment," Clinical Neurophysiology, vol. 114, pp. 399-409, 2003.
6. C. Guger, H. Ramoser, and G. Pfurtscheller, "Real-Time EEG Analysis with Subject-Specific Spatial Patterns for a Brain–Computer Interface (BCI)," IEEE Trans. Rehabl. Eng, vol. 8, No. 4, pp. 447-457, 2000.
7. E. Donchin, K. M. Spencer, and R. Wijesinghe, "The mental prosthesis: assessing the speed of a P300-based brain-computer interface," IEEE Trans. Rehabl. Eng, vol. 8, pp. 174-179, 2000.
8. E. N. Marieb, R.N., Jon Mallatt. "Human Anatomy," Benjamin/ Cummings, pp. 325, 1996.
9. G. Pfurtscheller, "ERD as an index of anticipatory," Handbook of electroencephalography and clinical neurophysiology revised series, vol. 6, pp. 203-217, 1999.
10. G. Pfurtscheller, and F. H. Lopes da Silva, "Event-related EEG/MEG synchronization and desynchronization: basic principles," Clin. Neurophysiol, vol. 110, pp. 1842-1857, 1999.
11. G. Pfurtscheller, C. Guger, G. Muller, G. Krausz, and C. Neuper, "Brain oscillations control hand orthosis in a tetraplegic," Neurosci. Lett., vol. 292, pp. 211-214, 2000.
12. G. Pfurtscheller and A. Berghold, "Patterns of cortical activation during planning of voluntary movement," Electroencephalogr Clin. Neurophysiol, vol. 72, pp.250-258, 1989.
13. G. Pfurtscheller, C. Neuper, A. Schlogl, and K. Lugger. "Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters," IEEE Trans. Rehabil. Eng., vol. 63, p.316-325, 1998.
14. G. Pfurtscheller,and A. Aranibar. "Evaluation of event-related desynchronization (ERD) preceding and following voluntary self-paced movements," Electroencephalography and Clin. Neurophysiology. vol. 46, pp.138-146, 1979.
15. G. Pfurtscheller, A. Stancak Jr, and C. Neuper. "Post-movement beta synchronization. A correlate of an idling motor area?" Electroencephalography and Clin. Neurophysiology, vol. 98, pp.281-293, 1996.
16. G. Schalk D. J. McFarland, T. Hinterberger, N. Birbaumer, and J. R.Wolpaw. "BCI2000: A General-Purpose Brain-Computer Interface (BCI) System," IEEE Trans. Biomed. Eng., vol. 51, Issue. 6, pp. 1034-1043, June 2004.
17. G. Blanchard, and B. Blankertz, "BCI Competition 2003—Data Set IIa: Spatial Patterns of Self-Controlled Brain Rhythm Modulations," IEEE Trans. Biomed. Eng., vol. 51, Issue. 6, pp. 1062-1066, June 2004.
18. G. E. Birch, Z. Bozorgzadeh, and S. G. Mason. "Initial On-Line Evaluations fo the LF-ASD Brain-Computer Interface with Able-Bodied and Spinal-Cord Subjects Using Imagined Voluntary Motor Potentials," IEEE Trans. Neural System and Rehab. Eng., vol. 10, No. 4, December 2002.
19. 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 international meeting," IEEE Trans Rehabil Eng, vol. 8, pp. 164-173, 2000.
20. J. R. Wolpaw and D. J. McFarland, "Multichannel EEG-based brain-computer communication," Electroencephalography Clin. Neurophysiology, vol. 90, pp. 444-449, 1994.
21. J. R. Wolpaw, D. J. McFarland, G.W. Neat, and C. A. Forneris. "An EEG-based brain–computer interface for cursor control," Electroencephalography Clin. Neurophysiology, vol. 78, p.252–259, 1991.
22. J. R. Wolpaw, D. J. McFarland, and T. M. Vaughan. "Brain–computer interface research at the Wadsworth Center," IEEE Trans. Rehabil. Eng., vol. 8, p.222–225, 2000.
23. J. R. Wolpaw, N. Birbaumer, D. J. McFarlanda, G. Pfurtschellere, and T. M. Vaughana. "Brain–computer interfaces for communication and control (invited review), " Clinical Neurophysiology, vol. 113, p.767–791, 2002.
24. J. R. Wolpaw, N. Birbaumer, D. J. McFarlanda, T. M. Vaughana, and G. schalk. "The Wadsworth Center Brain–Computer Interface (BCI) Research and Development Program," IEEE Trans. Neural Systems and Rehabil. Eng., vol. 11, No. 2, pp. 204-207, June 2003
25. J. Muller-Gerking, G. Pfurtscheller, and H. Flyvbjerg. "Designing optimal spatial filters for single-trial EEG classification in a movement task," Clinical Neurophysiology, vol.110, pp. 787-798, 1999.
26. J. R. Millán, J Mouriño. "Asynchronous BCI and Local Neural Classifiers: An Overview of the Adaptive Brain Interface Project," IEEE Trans. Neural Systems and Rehabil. Eng., vol. 11, No. 2, pp. 159-161, June 2003
27. J. R. Millán, J Mouriño, M. Franzé, F. Cincotti, M. Varsta, J. Heikkonen, and F. Babiloni. "A Local Neural Classifier for the Recognition of EEG Patterns Associated to Mental Tasks," IEEE Trans. Neural Networks, vol. 13, No. 3, pp. 678-686, May 2002.
28. M. Middendorf, G. McMillan, G. Calhoun, and K. S. Jones, "Brain-computer interfaces based on the steady-state visual-evoked response," IEEE Trans. Rehabil. Eng., vol. 8, pp. 211-214, 2000.
29. M. Fuchs, R. Drenckhahn, H. A. Wischmann, and M. Wanger, "An Improved Boundary Element Method for Realistic Volume-Conductor Modeling," IEEE Trans.Biomed. Eng., vol. 45, No. 8, pp. 980-997, August 1998.
30. N. S. Lab., "CURRY: User Guide," Neurosoft, pp. 152-178, April 1999.
31. N. Birbaumer, N. Ghanayim, T. Hinterberger, I. Iversen, B. Kotchoubey, A. Kübler, J. Perelmouter, E. Taub, and H. Flor, “A spelling device for the paralyzed,” Nature, vol. 398, pp. 297–298, 1999.
32. N. Birbaumer, "The thought translation device (TTD) for completely paralyzed patients," IEEE Trans. Rehabil. Eng., vol. 8, pp. 190-193, 2000.
33. N. Xu, X Gao, B. Hong, X. Miao, S. Gao, and F.Yang. "BCI Competition 2003—Data Set IIb: Enhancing P300 Wave Detection Using ICA-Based Subspace Projections for BCI Applications," IEEE Trans. Biomed. Eng., vol. 51, Issue. 6, pp. 1067-1072, June 2004.
34. R. O. Duda, P. E. Hart, and D. G. Stork. "Pattern Classification,"-2nd Ed, John Wiley &Sons, Inc., pp.117-120, 2001.
35. P. L. Lee, Y. T. Wu, L. F. Chen, Y. S. Chen, C. M. Cheng, T. C. Yeh, L. T. Ho, M. S. Chang, and J. C. Hsieh. "ICA-based spatiotemporal approach for single-trial analysis of post-movement MEG beta synchronization," NeuroImage. vol. 20, pp. 2010-2030, 2003.
36. P. R. Kennedy and K. D. Adams. "A Decision Tree for Brain–Computer Interface Devices," IEEE Trans. Neural Systems and Rehabil. Eng., vol. 11, No. 2, pp. 148-150, June 2003.
37. P. Sykacek, S. J. Roberts, and M. Stokes. "Adaptive BCI Based on Variational Bayesian KalmanFiltering: An Empirical Evaluation," IEEE Trans. Biomed. Eng., vol. 51, No. 5, May 2004.
38. P. Lucas, A. Chris, T. Akaysha, P. Barak, Y. Nick, O. Allen, and S. Paul. "Linear Spatial Integration for Single-Trial Detection in Encephalography," NeuroImage, vol. 17, pp. 223-230, 2002.
39. S. G. Mason, and G. E. Birch. "A Brain-Controlled Switch for Asynchronous Control Application," IEEE Trans Biomed. Eng., vol. 47, No. 10, pp. 1297-1307, October 2000.
40. T. Ebrabimi, J. M. Vesin, and G. Garcia. "Brain-Computer Interface in Multimedia Communication," IEEE Signal Proce. Magaz., vol. 3, pp. 14-24, January 2003.
41. T. Hinterberger, S. Schmidt, N. Neumann, J. Mellinger, B. Blankertz, Gabriel Curio, and N. Birbanumer. "Brain-Computer Communcation and Slow Cortical Potentials," IEEE Trans Biomed. Eng., vol. 51, No. 6, pp. 1011-1018, June 2004.
42. Y. T. Wu, P. L. Lee, L. F. Chen, T. C. Yeh, and J. C. Hsieh. "Single-trial quantification of imagery beta-band Mu rhythm in finger lifting task using independent component analysis (ICA)," In Proceedings, BioMag 13th International Conference on Biomagnetism, pp. 1045–1047, 2002.
43. Y. Meyer, "Wavelets: Algorithms and Applications," Siam, pp. 5-7,1993.
44. Y. T. Elad, and G. F. Inbar. "Feature Selection for the Classification of Movements From Single Movement-Related Potentials," IEEE Trans. Neural System and Rehab. Eng., vol. 10, No. 3, pp. 170-176, September 2002.
45. 林景福等編譯，圖解腦波入門，1985年4 月。

指導教授

徐國鎧(Kuo-Kai Shyu)

審核日期

2004-7-13

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