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研究生: 廖宇璁
論文名稱: 想像幾何旋轉動作與數學心算之腦電波分析
指導教授: 葉榮木
Yeh, Zong-Mu
蔡俊明
Tsai, Chun-Ming
學位類別: 碩士
Master
系所名稱: 機電工程學系
Department of Mechatronic Engineering
論文出版年: 2009
畢業學年度: 97
語文別: 中文
論文頁數: 96
中文關鍵詞: 腦電波大腦人機系統認知科學
英文關鍵詞: Electroencephalography (EEG), Brain-Computer Interface (BCI), Cognitive Science, Multiscale Entropy, Empirical Mode Decomposition
論文種類: 學術論文
相關次數: 點閱:227下載:6
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  • 大腦進行運作的機制一向是科學與醫學的研究重點,本論文使用快速傅立葉轉換(FFT)和多尺度熵(MSE)的方法,分析大腦進行數學認知行為的空間能量分佈特徵,兩類分析方法的實驗結果同時指出,大腦前額葉與頂葉為數學認知行為的主要激發區域,大腦枕葉的能量特徵相對較不顯著,而四類基礎數學運算以加法及乘法運算對大腦負荷度較低,減法及除法對大腦負荷度則相對較高。本研究另外探討當我們數學計算時,常伴隨出現的空間旋轉概念(如:建築工程、向量計算…等)的空間能量特徵,發現其大腦特徵與數學運算時高度相似,都是以大腦前額葉為顯著特徵區域,而我們也使用適用於非穩態訊號的經驗模態分解法(EMD)進行濾波,及線性鑑別分析法(LDA)與最近鄰居法(NNR)辨識想像順時針旋轉與逆時針旋轉的腦電波,實驗結果以FZ電極與FCZ電極的組合辨識率最高,四位受測者平均辨識率可達80.7%,辨識率較想像四肢活動雖無顯著提升,但證實了想像幾何旋轉動作為有效的想像辨識標的。

    The mechanism of how the brain functions and processes the data has always been the major research concern in both scientific and medical fields. This thesis presents the spatial energy distribution characteristics when the brain doing mathematical cognitive behaviors via the application of FFT and MSE. And the results of both analyses had pointed out that frontal lobe and parietal lobe of the brain are the major stimulating areas for mathematical cognitive behaviors, while the occipital lobe holds comparatively less obvious energy characteristics. Meanwhile, among the four basic operations of arithmetic,
    subtraction and division put more loadings to our brain, compared to addition and multiplication. This research also probes into the spatial energy distribution characteristics for 3D rotating concepts that we applied while doing math
    calculations, such as building construction programs and vector quantity calculations. We have discovered a highly similarity as doing arithmetic. There are both more energetic performances on the frontal lobe of the brain. We also use LDA and NNR, to recognize the EEGs while imagining clockwise rotations and anticlockwise ones. The outcomes show that the combination of FZ electrode and FCZ electrode has the best recognition rate, an average of 80.7% among our
    experimental subjects. Though the rate doesn’t suggest a higher recognizablity than picturing the limb activities, it proves that imagining geometric rotation surely is a valid alternative for imagining recognition.

    致謝..................................................I 摘要..................................................II Abstract..............................................III 目錄...................................................V 圖目錄.................................................VI 表目錄.................................................IX 第一章 緒論.............................................1 1.1 研究動機..........................................1 1.2 研究目的..........................................3 1.3 常見腦電波分析域與控制訊號..........................5 1.4 研究架構..........................................8 第二章 文獻探討.........................................10 2.1 數學認知行為研究概況...............................10 2.2 應用於大腦人機介面的想像各類動作....................20 第三章 實驗設計與流程....................................23 3.1 數學認知行為實驗..................................22 3.2 想像幾何旋轉實驗..................................24 3.3 實驗設備與環境....................................27 3.4 腦電波訊號前處理..................................31 第四章 實驗方法.........................................29 4.1 數學認知研究腦電波訊號前處理........................29 4.2 數學認知研究腦電波訊號後處理........................37 4.3 想像幾何旋轉研究腦電波前處理........................43 4.4 想像幾何旋轉研究腦電波後處理........................47 第五章 實驗結果與分析....................................51 5.1 數學心算結果分析...................................51 5.2 想像幾何旋轉動作結果分析............................71 5.3 數學運算與幾何旋轉特徵比較..........................80 第六章 結論.............................................81 參考文獻

    [1] 陳志瑋,「研究以小波神經網路作μ波即時鑑別」,國立成功大學機械工
    程學系碩士論文,2002。
    [2] 陳致仰,「改良式對角化主要成分分析法應用於兩類別想像動作腦電波的
    分類」,國立台灣師範大學機電科技學系碩士論文,2007。
    [3] http://faculty.washington.edu/chudler/1020.html
    [4] A. Nijholt, D. Yan, “Brain-Computer Interfacing for
    Intelligent Systems,” IEEE Intelligent Systen, vol.
    23, pp. 72-79, 2008.
    [5] http://www.dls.ym.edu.tw/neuroscience/functional_c.htm
    [6] T. M. Vaughan, D. J. McFarland, G. Schalk, W. A.
    Sarnacki, D. J. Krusienski, E. W. Sellers, and J. R.
    Wolpaw, “The Wadsworth BCI Research and Development
    Program: At Home With BC,” IEEE Trans. Eng.,
    vol. 14, no. 2, pp. 229-233, 2006.
    [7] M. Adjouadi, M. Cabrarizo, “Interpreting EEG
    Functional Brain Activity,” IEEE
    Potentials, pp. 8-13, 2004
    [8] http://ibru.vghtpe.gov.tw/chinese/fMRI.htm
    [9] H.Mizuhara, L. Q. Wang, K. Kobayashi, and Y.
    Yamaguchi, “Long-range EEG phase synchronization
    during an arithmetic task indexes a coherent
    cortical network simultaneously measured by fMRI,”
    Neuroimage, Vol.27, pp.553-563, 2005.
    [10] M. Aschcraft, H. Stazyk, “Mental addition: a test of
    three verification models,” Memoring Cogn., Vol. 9,
    pp. 185–196, 1981.
    [11] M. Niedeggen, F. Rosler, “N400 effects reflect
    activation spread during retrieval of arithmetic
    facts,” Psychol. Sci. , Vol. 10 , pp. 271–276, 1999.
    [12] D. Szucs, V. Csepe, ” The effect of numerical
    distance and stimulus probability on ERP components
    elicited by numerical incongruencies in mental
    addition,” Cogn. Brain Res, Vol. 22, pp. 289–300,
    2005.
    [13] M. Isabel, C. Escera, “An event-related brain
    potential study of the arithmetic split
    effect. ”International Journal of Psychophysiology,
    Vol.64, pp.165-173, 2007.
    [14] H. D. Critchley, D.R. Corfield, “Cerebral correlates
    of autonomic cardiovascular arousal: a functional
    neuroimaging investigation,” J. Physiol., Vol.523,
    pp.259–270, 2000.
    [15] M. A. Gary, P. Taggart, “A cortical potential
    reflecting cardiac function,” Proc. Natl. Acad. Sci.
    U. S. A, Vol. 35, pp. 6818–6823, 2007.
    [16] X. Yu, J. Zhang, D. Xie, “Relationship between scalp
    potential and autonomic nervous activity during a
    mental arithmetic task,” Autonomic Neuroscience:
    Basic and Clincal, Vol. 146, pp.81-86, 2009.
    [17] http://www.babylon.com/definition/AUTONOMIC_NERVOUS
    _SYSTEM/English
    [18] B. Kamousi, L. Zhongming, and Bin He, Fellow,
    IEEE, “Classification of Motor Imagery Tasks for
    Brain-Computer Interface Applications by Means of Two
    Equivalent Dipoles Analysis,” IEEE Transactions On
    Neural Systems And Rehabilitation Engineering, vol.
    13, no.2, June 2005.
    [19] 方偉力,「以主成分分析法和線性鑑別分析法辨識想像左右手動」,國
    立台灣師範大學機電科技學系碩士論文,2007。
    [20] M. Phothisonothai, M. Nakagawa, “EEG-based
    classification of new imagery tasks using three-layer
    feedforward neural network classifier for Brain–
    Computer Interface, “ Journal of Physical Socirty of
    Japan, 2006.
    [21] J. V. Baldo, N. F. Dronkers, “Neural correlates of
    arithmetic and language comprehension:A common
    substrate? ” Neuropsychologia, Vol.45, pp.229-235,
    2007.
    [22] A. Ischebeck, L. Zamarian, K. Egger, “Imaging early
    practice effects in Arithmetic, ” NeuroImage, Vol.
    36, pp.993-1003, 2007.
    [23] F. T. Rocha, A. F. Rocha, “Brain mapping of
    arithmetic processing in children and adults,”
    Cognitive Brain Research, Vol. 22, pp.359-372, 2005.
    [24] C. Cooley, W. James, J. W. Tukey, 1965, “An algorithm
    for the machine calculation of complex Fourier
    series, ” Math. Comput., Vol. 19, pp.297–301, 1965.
    [25] http://mathworld.wolfram.com/FastFourierTransform.html
    [26] M. Vetterli, “Fast Fourier transforms: a tutorial
    review and a state of the art,”Signal Processing ,
    Vol. 19, pp. 259–299, 1990.
    [27] 李天龍,「以FFT為架構建立之諧波參數建立方法」,國立中山大學電
    機工程學系研究所碩士論文,1999。
    [28] M. Costa, A. L. Goldberger, “Multiscale Entropy
    Analysis,” Phys. Rev. Lett., 2002.
    [29] S. K. Mitra, “Digital signal processing,”
    McGraw.Hill international edtion, 2006.
    [30] N. E. Huang, Z. Shen, S. R. Long, et al., “The
    Empirical Mode Decomposition and the Hilbert Spectrum
    for Nonlinear and Nonstationary Time Series
    Analysis, ” Proc. R. Soc. Lond. A, Vol. 454, 1998,
    pp.903-995.
    [31] N. E. Huang, M. C. Wu, S. R. Long, et al., “A
    Confidence Limit for the Empirical Mode Decomposition
    and Hilbert Spectrum Analysis, ” Proc. R. Soc. Lond.
    A, vol. 459, 2003, pp.2317-2345.
    [32] P. Gonçalvés, P. Abry, G. Rilling et.al, “Fractal
    Dimension Estimation: Empirical Mode Decomposition
    Versus Wavelets, ” IEEE International Conference on
    Acoustics, Speech and Signal Processing ICASSP 2007,
    Honolulu, Hawaii, 15-20 Apr. 2007.
    [33] Y. Jieping and Qi Li, “A Two-Stage Linear
    Discriminant Analysis via QR-Decomposition,” IEEE
    Transactions on Pattern Analysis and Machine
    Intelligence, vol. 27, pp. 929 – 941, 2005.
    [34] 洪倩玉,「建立動態線性鑑別分析於線上人臉辨識與驗證」,國立成功
    大學資,訊工程學系碩士論文,2003。
    [35] S. Noushath, Kumar G. Hemantha, and
    P.Shivakumara, “(2D)2LDA: An
    efficient approach for face recognition,” Pattern
    Recognition, vol 39, pp.
    1396 – 1400, 2006.
    [36] 陳若涵,「以音樂內容為基礎的情緒分析與辨識」,國立清華大學資訊
    與應用系統學系碩士論文,2006。

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