利用正向模型設計空間濾波器應用於視覺誘發電位之大腦人機介面之雜訊消除

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姓名

劉宇涵(Yu-Han Liu) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

利用正向模型設計空間濾波器應用於視覺誘發電位之大腦人機介面之雜訊消除
(Noise reduction in VEP-based BCI using forward-model generated spatial filter)

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摘要(中)

近年來，以視覺誘發電位(visual evoked potential, VEP)為基礎之大腦人機界面(Brain-Computer Interface, BCI)已被廣泛使用，利用不同頻率或相位進行編碼，形成各種控制指令。透過非侵入式腦波訊號(Electroencephalogram, EEG)的擷取及辨識，可使其成為與外界溝通的橋樑，而不需要透過肌肉活動來控制。VEP-BCI具有高傳輸率與少訓練時間等優點，但是腦波訊號屬於非線性(nonlinear )、非穩態(non-stationary)且易受雜訊干擾的隨機程序，不論是60Hz的電訊號，或是量測時其它的生理訊號，如:眼動訊號(Electrooculography, EOG)、肌電訊號(Electromyography, EMG)，皆會嚴重干擾量測結果。
本文提出較傳統EEG處理方式不同的濾波方法，利用訊號空間投影法(Signal Space Projection, SSP) 設計空間濾波器，僅允許大腦皮質視覺區(Visual Cortex) 的訊號通過，相對而言，可濾除雜訊及不必要的生理訊號，並提高SNR，對於VEP及SSVEP皆有顯著效果。

摘要(英)

The brain computer interface (BCI) based on the visual evoked potential (VEP) has been widely used in many applications in recent years. By tagging of flickers with different frequencies and phases, user’s gazed target can be recognized by analyzing the frequencies or phases of evoked VEPs. Though VEP-based BCI has the advantages of high information transfer rate (ITR) and less training time, the extraction of steady state visual evoked potential (SSVEP) is sometimes not complete due to its characteristics of nonlinearity, non-stationary and noise susceptibility. Either 60Hz noise or electrophysiological signals, such as Electrooculography (EOG) and Electromyography (EMG) would disturb Electroencephalogram (EEG) seriously.
Accordingly, this study adopts signal space projection (SSP) to design a spatial filter, passing signals only from visual cortex. The concept of spatial filter is different from the traditional frequency filtering process. In contrast, the method could filter out noise and additional electrophysiological signals, enhancing signal-to-noise ratio (SNR), and it has noticeable effects on VEP and SSVEP.

關鍵字(中)

★ 視覺誘發電位
★ 大腦人機介面
★ 訊號空間投影法
★ 正向模型
★ 空間濾波器

關鍵字(英)

★ Bra
★ Steady-state visual evoked potential (SSVEP)

論文目次

摘要 I
目錄 IV
圖目錄 VII
表目錄 XI
第一章緒論 1
1.1研究動機與背景 1
1.2研究目的 2
1.3論文架構 2
第二章大腦人機介面與視覺誘發電位 4
2.1大腦皮層的電活動 4
2.1.1腦波分類 5
2.1.2腦波量測 6
2.2大腦皮層誘發電位 9
2.2.1視覺誘發電位 10
2.3大腦人機介面 16
第三章研究理論 19
3.1正向模型 20
3.1.1 Spatio-Temporal Dipole Model (STDM) 21
3.1.2三層球狀顱部模型(Three-layer Spherical Head Model) 22
3.1.3 Multipole expansion與解[23] 25
3.1.4 單層球殼模型(One-layer Spherical Model) 31
3.1.5 三層球殼近似模型(Three-layer Spherical Model) 32
3.2訊號空間投影法(Signal space projection)[27] 33
3.2.1 SSP理論 35
3.2.2 SSP總結 36
第四章實驗設計 38
4.1儀器介紹 38
4.1.1 QuickAmp腦波放大器[36-37] 38
4.1.2　3SPACE FASTRAK位置追蹤器[38] 41
4.2實驗流程 43
4.3分析方法 44
4.3.1 EEG訊號量測 44
4.3.2訊號前處理(pre-process) 45
4.3.3波源分析 47
4.3.4正向模型 48
4.3.5空間濾波器設計 49
4.4空間濾波結果 51
4.4.1 SSVEP濾波結果 51
4.4.2 FVEP濾波結果 53
4.5 BCI系統設計 55
4.5.1 SSVEP多相位編碼 55
4.5.2 FVEP隨機編碼 57
第五章實驗結果與討論 61
5.1 SSVEP六相位編碼實驗結果與討論 62
5.2 FVEP隨機編碼實驗結果與討論 71
第六章結論與未來展望 80
參考文獻 82

參考文獻

[1] Artifact correction of the ongoing EEG using spatial filters based on artifact and brain signal topographies.
http://cat.inist.fr/?aModele=afficheN&cpsidt=13675196
[2] 丁慶華，"EEG and Evoked Potential"，取自
http://web.ncyu.edu.tw/~cting/teaching/lectures/brain-computer-interface/
[3] 大腦皮層，取自http://big5.wiki8.com/search?q=%E5%A4%A7%E8%85%A6%E7%9A%AE%E5%B1%A4&d=5&ie=t#3
[4] 音樂輔導與治療參考資料，取自 http://163.24.156.149/210/%E9%9F%B3%E6%A8%82%E8%BC%94%E5%B0%8E/%E9%9F%B3%E6%A8%82%E8%BC%94%E5%B0%8E%E8%88%87%E6%B2%BB%E7%99%82%E5%8F%83%E8%80%83%E8%B3%87%E6%96%99.htm
[5] 台北榮民總醫院教學研究部整合性腦功能實驗室，取自 http://ibru.vghtpe.gov.tw/chinese/bci.htm
[6] The International 10-20 System, http://www.brainmaster.com/generalinfo/electrodeuse/eegbands/1020/1020.html
[7] J. Malmivuo and R. Plonsey, Bioelectromagnetism: Principles and Applications of Bioelectric and Biomagnetic Fields: Oxford University Press, USA, 1995.
[8] W. Yijun, et al., "A practical VEP-based brain-computer interface," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 14, pp. 234-240, 2006.
[9] C. Herrmann, "Human EEG responses to 1–100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena," IEEE Transactions on Experimental Brain Research, vol. 137, pp. 353, 346-353, 346, 2001.
[10] 黃立維，「明暗閃爍視覺誘發電位於遙控器之應用」，國立中央大學電機工程研究所碩士論文，民國九十八年
[11] C. Ming, et al., "Design and implementation of a brain-computer interface with high transfer rates," IEEE Transactions on Biomedical Engineering, vol. 49, pp. 1181-1186, 2002.
[12] P.-L. Lee, et al., "Brain computer interface using flash onset and offset visual evoked potentials," Clinical Neurophysiology, vol. 119, pp. 605-616, 2008.
[13] E. Sutter, "The brain response interface: communication through visually-induced electrical brain responses," J. Microcomput. Appl., vol. 15, pp. 31-45, 1992.
[14] J. C. Mosher, et al., "EEG and MEG: forward solutions for inverse methods," IEEE Transactions on Bio-Medical Engineering, vol. 46, pp. 245-259, 1999.
[15] D. C. Fernandez, et al., "Comparison of different source localization methods," pp. 326-331, 1998.
[16] W. J. Freeman, "EEG spatial filter and method," 1988.
[17] Z. Zhang, "A fast method to compute surface potentials generated by dipoles within multilayer anisotropic spheres," Physics in Medicine and Biology, vol. 40, pp. 335-349, 1995.
[18] E. M. Baillet, et al., "Rapidly re-computable EEG forward models for realistic head shapes," Physics in Medicine and Biology, vol. 46, pp. 1265-1281, 2010.
[19] 行政院國家科學委員會專題研究計畫成果報告-腦部活動之電生理訊號源模型建立、估算、與分析(I)，取自 http://ir.lib.nctu.edu.tw/bitstream/987654321/16249/1/932218E009025.pdf
[20] Mind Trip: Journey into the Brain, http://www.wiredtowinthemovie.com/mindtrip_xml.html
[21] P. Wen and Y. Li, "EEG human head modelling based on heterogeneous tissue conductivity," Australasian Physical & Engineering Sciences in Medicine / Supported by the Australasian College of Physical Scientists in Medicine and the Australasian Association of Physical Sciences in Medicine, vol. 29, pp. 235-240, 2006.
[22] 維基百科，取自http://upload.wikimedia.org/wikipedia/commons/thumb/c/c0/Spherical_with_grid.svg/250px-Spherical_with_grid.svg.png
[23] G. Nolte and G. Dassios, "Analytic expansion of the EEG lead field for realistic volume conductors," Physics in Medicine and Biology, vol. 50, pp. 3807-3823, 2005.
[24] Y. Salu, et al., "An improved method for localizing electric brain dipoles," IEEE Transactions on Biomedical Engineering, vol. 37, pp. 699-705, 1990.
[25] D. A. Brody, et al., "Eccentric Dipole in a Spherical Medium: Generalized Expression for Surface Potentials," IEEE Transactions on Biomedical Engineering, vol. BME-20, pp. 141-143, 1973.
[26] M. Scherg and D. Von Cramon, "Two bilateral sources of the late AEP as identified by a spatio-temporal dipole model," Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section, vol. 62, pp. 32-44, 1985.
[27] M. Uusitalo and R. Ilmoniemi, "Signal-space projection method for separating MEG or EEG into components," Medical and Biological Engineering and Computing, vol. 35, pp. 135-140, 1997.
[28] Y.-S. Chen, et al., "Maximum contrast beamformer for electromagnetic mapping of brain activity," IEEE Transactions on Bio-Medical Engineering, vol. 53, pp. 1765-1774, 2006.
[29] F.-H. Lin, et al., "Linear constraint minimum variance beamformer functional magnetic resonance inverse imaging," NeuroImage, vol. 43, pp. 297-311, 2008.
[30] M. Grosse-Wentrup, et al., "Beamforming in Noninvasive Brain–Computer Interfaces," IEEE Transactions on Biomedical Engineering, vol. 56, pp. 1209-1219, 2009.
[31] J. Gross and A. A. Ioannides, "Linear transformations of data space in MEG," Physics in Medicine and Biology, vol. 44, pp. 2081-2097, 1999.
[32] Y. Xinyi, et al., "Sparse spatial filter optimization for EEG channel reduction in brain-computer interface," pp. 417-420, 2008.
[33] B. D. Van Veen, et al., "Localization of brain electrical activity via linearly constrained minimum variance spatial filtering," IEEE Transactions on Biomedical Engineering, vol. 44, pp. 867-880, 1997.
[34] C. D. Tesche, et al., "Signal-space projections of MEG data characterize both distributed and well-localized neuronal sources," Electroencephalography and Clinical Neurophysiology, vol. 95, pp. 189-200, 1995.
[35] S. Taulu, et al., "The Signal Space Separation method," physics/0401166, 2004.
[36] Brain Products GmbH Solutions for neurophysiological research, www.algolpharma.fi/files/algolpharma/product.../QuickAmp_b1.pdf
[37] The most compact Solution for Neurophysiological Research, http://www.brainproducts.com/productdetails.php?id=14&tab=3
[38] 3SPACE FASTRAK, http://www.vrlogic.com/html/polhemus/3space_fastrak.html#Specs
[39] P.L. Lee, et al., "The Brain Computer Interface Using Flash Visual Evoked Potential and Independent Component Analysis," Annals of Biomedical Engineering, vol. 34, pp. 1641-1654, 2006.
[40] 謝竣傑，「多頻相位編碼之閃光視覺誘發電位驅動大腦人機介面"，國立中央大學電機工程研究所碩士論文」，民國九十六年

指導教授

李柏磊(Po-Lei Lee)

審核日期

2010-7-26

推文