| 研究生: |
劉郁汝 Yu-ju Liu |
|---|---|
| 論文名稱: |
雙頻穩態視覺誘發電位系統之研究 Research on Dual-Frequency Steady-State Visual Evoked Potentials Induced System |
| 指導教授: |
徐國鎧
Kuo-kai Shyu 李柏磊 Po-lei Lee |
| 口試委員: | |
| 學位類別: |
博士 Doctor |
| 系所名稱: |
資訊電機學院 - 電機工程學系 Department of Electrical Engineering |
| 論文出版年: | 2013 |
| 畢業學年度: | 101 |
| 語文別: | 英文 |
| 論文頁數: | 77 |
| 中文關鍵詞: | 穩態視覺誘發電位 、雙頻 、相關性方法 、腦電訊號 |
| 外文關鍵詞: | Steady-state visual evoked potential (SSVEP), dual-frequency, correlation method, electroencephalography(EEG) |
| 相關次數: | 點閱:22 下載:0 |
| 分享至: |
| 查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
一般而言,穩態視覺誘發電位(steady state visual evoked potential ,SSVEP)的選項方式,其中的各單一選項使用不同頻率閃爍刺激誘發的方式。而本論文中,提出一種新的穩態視覺誘發電位閃爍方法,本研究在單一選項的閃爍刺激中,使用兩個不同頻率誘發的穩態視覺誘發系統,稱為雙頻穩態視覺誘發電位(dual-frequency SSVEP),並經由多個受測者的實驗結果證明本方法的可行性。在實驗的過程中,將受測者的腦電訊號(electroencephalography , EEG)使用傅立葉轉換(fourier transform, FFT)分析,發現受測者的腦電訊號裡,有時含有除了主要刺激頻率以外的訊號,此多出的頻率訊號與兩個主要誘發頻率有對稱的現象,稱為對稱諧波現象(symmetric harmonic phenomena),有助於加強辨識。本研究亦提出一個新的相關性計算方法(correlation method)處理這種雙頻閃爍刺激誘發的腦電訊號。經由實驗中發現,本研究所提出的方法在辨識率方面優於傳統被大量使用的快速傅利葉轉換的方法。此外,本論文將所提出的雙頻穩態視覺誘發電位,加入了多相位編碼的閃爍序列。然而,大腦是一個非線性系統,而腦電訊號是其輸出的訊號。而這種包含不同頻率甚至不同相位的腦電訊號是否可被成功的分析,亦是本研究所探討的重點。
This dissertation presents a new steady-state visual evoked potential (SSVEP). Different
from the general SSVEP using only one frequency flicker for each selection of flash
stimulators, this work uses a dual-frequency flicker. This dissertation verifies the feasibility of
the proposed method, and the symmetric harmonic phenomena are found in this study. Then
this dissertation proposes a novel correlation method for frequency recognition of
dual-frequency SSVEP. The results further demonstrate that the proposed correlation method has a higher recognition rate than the widely used fast Fourier transform (FFT)method in the proposed system. Moreover, the dual-frequency embedded with the multi-phase flickering sequences stimulation method is proposed. But the brain is a nonlinear dynamic system, and Electroencephalography (EEG) signal can be regarded as its output. The EEG signals in this dissertation include difference frequencies even phases. However, whether this kind of signals is treated as the meaningful signals is researched.
[1] J. J. Vidal, “Toward direct brain-computer communication,” Annual Review of Biophysics &
Bioengineering, vol. 2, pp. 157-180, 1973.
[2] 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, pp. 767-791, 2002.
[3] 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 Transactions on Neural Systems
and Rehabilitation Engineering, vol. 8, no. 2, pp. 164-173, 2000.
[4] J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan,
“Brain-computerinterfaces for communication and control,” Clinical Neurophysiology, vol. 113,
pp. 767-791, 2002.
[5] G. Pfurtscheller, C. Neuper, C. Guger, W. Harkam, H. Ramoser, A. Schlogl, B. Obermaier, and
M. Pregenzer, “Current trends in Graz brain-computer interface (BCI) research,” IEEE
Transactions on Rehabilitation Engineering, vol. 8, no. 2, pp. 216-219, 2000.
[6] G. R. Muller-Putz and G. Pfurtscheller, “Control of an electrical prosthesis with an SSVEP-based
BCI,” IEEE Transactions on Biomedical Engineering, vol. 55, no. 1, pp. 361-364, 2008.
53
[7] Y. J. Wang, X. R. Gao, B. Hong, C. Jia, and S. K. Gao, “Brain-computer interfaces based on
visual evoked potentials,” IEEE Engineering in Medicine and Biology Magazine, vol. 27, no. 5,
pp. 64-71, 2008.
[8] E. Donchin, K. M. Spencer, and R. Wilesinghe, “Themental prosthesis: assessing the speed of a
P300-based brain computer interface”, IEEE Transactions on Rehabilitation Engineering, vol.
8, no. 2, pp. 174-179, 2000.
[9] L. A. Farwell and E. Donchin, “Talking off the top of your head: toward a mental prosthesis
utilizing event-related potentials, Electroencephalography and Clinical”, Neurophysiology, vol.
70, no. 6, pp. 510-523, 1988.
[10] F. Beverina, G. Palmas, S. Silvoni, F. Piccione, and S. Giove, “User adaptive BCIs: SSVEP and
P300 based interfaces”, Psychnology Journal, vol. 1, no. 4, pp. 331-354, 2003.
[11] F. Piccione, F. Giorgi, and P. Tonin, “P300-based brain computer interface: reliability and
performance in healthy and paralysed participants,” Clinical Neurophysiology, vol. 117, pp.
531-537, 2006.
[12] X. Y. Wang, J. Meng, G. L. Tan, and L. X. Zou, “Research on the relation of EEG signal chaos
characteristics with high-level intelligence activity of human brain,” Nonlinear Biomedical
Physics, vol. 4, no. 1, doi: 10.1186/1753-4631-4-2, 2010.
[13] 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, 1992.
54
[14] T. P. Jung, S. Makeig, M. Westerfield, J. Townsend, E. Courchesne, and J. T. Sejnowski,
“Analysis and visualization of single-trial event-related potentials,” Human Brain Mapping, vol.
14, pp. 166-185, 2001.
[15] R. Spehlmann, “Evoked potential primer,” MA: Butterworth Publishers, 1985.
[16] K. E. Misulis and T. Fakhoury, “The transient VEP to diffuse light simuli,” Evoked Potential
Primer, pp. 135-142, 1985.
[17] K. E. Misulis and T. Fakhoury, “VEPs to other stimuli,” Evoked Potential Primer, pp. 144-158,
1985.
[18] A. C. Tang, B. A. Pearlmutter, N. A. Malaszenko, and D. B. Phung, “Independent components
of magnetoencephalography: single-trial response onset times,” Neuroimage, vol. 17, no. 4, pp.
1773-1789, 2002.
[19] H. A. Baseler, E. E. Sutter, S. A. Klein, and T. Carney, “The topography of visual evoked
response properties across the visual field,” Electroencephalography and Clinical
Neurophysiolog, vol. 90, pp. 65-81, 1994.
[20] B. Brown and M. Z. Yu, “Variation of topographic visually evoked potentials across the visual
field,” Ophthalmic and Physiological Optics, vol. 17, no. 1, pp. 25-31, 1997.
[21] E. E. Sutter and D. Tran, “The field topography of ERG components in Man-I. The photopic
luminance response,” Vision Research, vol. 32, pp. 433-446, 1992.
[22] C. S. Herrmann, “Human EEG responses to 1–100 Hz flicker: resonance phenomena in visual
55
cortex and theirpotential correlation to cognitive phenomena,” Experimental Brain Research,
vol. 137, pp. 346-353, 2001.
[23] P. L. Lee, C. H. Wu, J. C. Hsieh, and Y. T. Wu, “Visual evoked potential actuated brain
computer interface: a brain-actuated cursor system,” Electronics Letters, vol. 41, no. 15, pp.
832-834, 2005.
[24] P. L. Lee, J. C. Hsieh, C. H. Wu, K. K. Shyu, S. S. Chen, T. C. Yeh, and Y. T. Wu, “The brain
computer interface using flash visual evoked potential and independent component analysis,”
Annals of Biomedical Engineering, vol. 34, no. 10, pp. 1641-1654, 2006.
[25] P. L. Lee, J. C. Hsieh, C. H. Wu, K. K. Shyu, and Y. T. Wu, “Brain computer interface using
flash onset and offset visual evoked potentials,” Clinical Neurophysiology, vol. 119, no. 3, pp.
605-616, 2008.
[26] 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 Transactions on Biomedical Engineering,
vol. 49, no. 10, pp. 1181-1186, 2002.
[27] S. Hui, Z. Li, B. Yan, and X. Longteng, “Research on SSVEP-based controlling system of
Multi-DoF manipulator,” Lecture Notes in Computer Science, pp. 171-177, 2009.
[28] D. Regan, “Human brain electrophysiology: evoked potentials and evoked magnetic fields in
science and medicine,” New York: Elsevier, 1989.
[29] G. L. Calhoun and G. R. McMillan, “EEG-based control for human computer interaction,”
56
Human Interaction with Complex Systems, pp. 4-9, 1996.
[30] M. Middendorf, G. McMillan, G. Calhoun, and K. S. Jones, “Brain computer interfaces based
on the steady-state visual-evoked response,” IEEE Transactions on Rehabilitation Engineering,
vol. 8, no. 2, pp. 211-214, 2000.
[31] 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,” Annal of Biomedical Engineering, vol. 38, no. 7, pp. 2383-2397, 2010.
[32] Y. J. Wang, R. P. Wang, X. R. Gao, B. Hong, and S. K. Gao, “A practical VEP-based
brain-computer interface,” IEEE Transactions on Neural System and Rehabilitation
Engineering, vol. 14, no. 2, pp. 234-239, 2006.
[33] Z. H. Wu and D. Z. Yao, “Frequency detection with stability coefficient for SSVEP-based
BCIs,” Journal of Neural Engineering, vol. 5, pp. 36-43, 2008.
[34] G. Y. Bin, X. R. Gao, Z. Yan, B. Hong, and S. K. Gao, “An online multi-channel SSVEP-based
brain-computer interface using a canonical correlation analysis method,” Journal of Neural
Engineering, vol. 6, no. 4, 046002, 2009.
[35] G. R. Muller-Putz, R. Scherer, C. Brauneis, and G. Pfurtscheller, “Steady-state visual evoked
potential (SSVEP)-based communication: impact of harmonic frequency components,” Journal
of Neural Engineering, vol. 2, pp. 123-130, 2005.
[36] http://www.neurodevelopmentcenter.com
[37] http://faculty.washington.edu/chudler/1020.html
[38] E. Lalor, S. P. Kelly, C. Finucane, R. Burke, R. B. Reilly, G. Mc-Darby, “Brain computer
interface based on the steady-state VEP for immersive gaming control,” Biomedical Technician,
vol. 49, pp. 63-64, 2004.
[39] Z. H. Wu and D. Z. Yao, “A study on SSVEP-based BCI,” Journal of Electronic Science and
Technology of China, vol. 7, no. 1, pp. 7-11, 2009.
[40] Y. Wang, Y. T. Wang, and T. P. Jung, “Visual stimulus design for
high-rate SSVEP BCI,” Electronics Letters, vol. 46, no. 15, pp. 1057-1058, 2010.
[41] Z. H. Wu, Y. X. Lai, Y. Xia, and D. Z. Yao, “Stimulator selection in SSVEP-based BCI,”
Medical Engineering & Physics, vol. 30, no. 8, pp. 1079-1088, 2008.
[42] L. J. Trejo, R. Rosipal, and B. Matthews, “Brain-computer interfaces for 1-D and 2-D cursor
control: designs using volitional control of the EEG spectrum or steady-state visual evoked
potentials,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 14, no.
2, pp. 225-229, 2006.
[43] P. Gabriel, N. Urbano, and C. B. Miguel, “Statistical spatial filtering for a P300-based BCI: tests
in able-bodied, and patients with cerebral palsy and amyotrophic lateral sclerosis,” Journal of
Neuroscience Methods, vol. 195, no. 2, pp. 270-281, 2011.
58
[44] M. L. Tsaia, W. C. Shannb, W. R. Luob, and C. T. Yen, “Wavelet-based analysis of
low-frequency fluctuations of blood pressure and sympathetic nerve activity in rats,”
Neuroscience Letters, vol. 358, pp. 165-168, 2004.
[45] Z. L. Lin, C. S. Zhang, W. Wu, and X. R. Gao, “Frequency recognition based on canonical
correlation analysis for SSVEP-based BCIs,” IEEE Transactions on Biomedical Engineering,
vol. 53, no. 12, pp. 2610-2614, 2006.
[46] A. Luo and T. J. Sullivan, “A user-friendly SSVEP-based brain-computer interface using a
time-domain classifier,” Journal of Neural Engineering, vol. 7, no. 2, 026010, 2010.
[47] K. K. Shyu, P. L. Lee, Y. J. Liu, and J. J. Sie, “Dual-frequency steady-state visual evoked
potential for brain computer interface,” Neuroscience Letters, vol. 483, pp. 28-31, 2010.
[48] K. K. Shyu, P. L. Lee, Y. J. Liu, and J. J. Sie, “Multi-target stimulator SSVEP using
multi-frequency embedded with multi-phase encoding sequence,” Electronics Letters, vol. 48,
no. 18, pp. 1097-1098, 2012.