臺灣博碩士論文加值系統

在本論文中，我們主要研究的是一種基於晚期肌萎縮側索硬化症FP1和FP2之 P300-BCI腦波時頻特徵分析與討論，透過P300腦機介面來記錄並觀察正常者與晚期肌萎縮側索硬化患者，在分別接受六張不同圖片的刺激後，大腦內前額葉位置，FP1及FP2 所產生不同能量腦電波圖，並透過HHT來分析其能量特徵再加以討論歸納；其中我們透過P300腦機介面的設備，有觀察記錄到在晚期肌萎縮性脊髓側索硬化患者的部分IMF9的δ波的信號能量的比率分別為11.57％和18.24％，反觀正常人的δ波的總能量IMF9的信號能量的比率分別為20.71％與18.94％，另外發現LSALS症患者腦波特徵均屬於無意識或是淺意識層面之δ波，這種現象是可能是因為LSALS病徵，所引起的中樞神經系統較不敏感所在成的現象。

In this thesis, we analyse the HHT-based characteristics of the frequency-energy distributions of the IMFs of clinical FP1, and FP2 electroencephalogram (EEG) signals recorded from P300-based brain–computer interface (BCI) normal and Late-stage Amyotrophic Lateral Sclerosis (LSALS) subjects observing six different pictures. The ratios of the energy of P300-based BCI with the FP1 and FP2 EEG signals of a LSALS observer to its refereed total energy for IMF9 in the δ wave were 11.57%, and 18.24%, respectively. The ratios of the energy of P300-based BCI with the FP1 and FP2 EEG signals of a normal observer to its refereed total energy for IMF9 in the δ wave were 20.71%, and 18.94%, respectively. Also found that patients with brain wave characteristics LSALS belong unconscious or conscious level of brain waves shallow δ wave. This phenomenon is due to the less sensitive central nervous systems of the LSALS subjects.

誌謝 I
摘要 II
Abstract III
目次 IV
圖次 VI
表次 VIII
第一章 緒論 1
1.1前言 1
1.2研究動機與目的 1
1.3研究方法 2
1.4章節內容概述 2
第二章 研究背景與原理 3
2.1晚期肌萎縮性脊髓側索硬化症(Late-stage Amyotrophic Lateral Sclerosis, LSALS)[4] 3
2.2 腦波和FP1、FP2通道簡介及關連性介紹 3
2.2.1 腦波量測與電極位置 4
2.2.2腦電波的訊號來源[7-8] 4
2.2.3 FP1和FP2腦電波圖簡介 5
2.3 實驗訊號取得步驟[2] 5
2.4希爾伯特-黃轉換(Hilbert-Huang Transform, HHT) 6
2.4.1本質模態函數(Intrinsic Mode Function, IMF) 7
2.4.2經驗摸式拆解(Empirical Mode Decomposition, EMD) 7
2.4.3瞬時頻率(Instantaneous Frequency, IF) 9
2.4.4希爾伯特頻譜(Hilbert Spectrum, HS) 9
2.4.5邊際頻率(Marginal Frequency, MF) 10
第三章 正常者與晚期肌萎縮側索硬化症腦波之希爾伯特-黃轉換分析 11
3.1正常人與LSALS症患者腦波的P300-BCI FP1和FP2原始腦波 11
3.2正常者和LSALS症腦波前額葉P300-BCI FP1和FP2腦波IMF分析 14
3.2.1正常者和LSALS症患者腦波IMF特徵分析 14
3.2.2正常者和LSALS症腦波RF殘餘函數之關聯性比較 27
第四章 一種LSALS症患者P300-BCI FP1、FP2腦波與正常者P300-BCI FP1、FP2腦波之IF及MF分析 30
4.1一種基於LSALS化症患者P300-BCI FP1和FP2腦波IF特徵分析 30
4.2一種基於LSALS症患者P300-BCI FP1和FP2腦波MF特徵分析 41
4.3 MS、LSALS及TBSCI之P300-BCI MF RF特徵關聯分析[24][25] 55
第五章 結論與未來展望 58
5.1結論 58
5.2未來展望 58
參考文獻 59

[1] N. E. Huang, Z. Shen, S. R. Long, M. C.Wu, H. H. Shih, Q. Zheng, N. C. Yen, C.C. Tung and H. H. Liu, “The Empirical Mode Decomposition and the HilbertSpectrum for Nonlinear and NonstationaryTime Series Analysis,” Proc.R. Soc. Lond. A, pp.903-995, 1998.
[2] U. Hoffmann , J. M. Vesin , T. Ebrahimi, and K. Diserens , “An effcient P300-based brain-computer interface for disabled subjects,” J Neurosci Methods, pp.115-125, 2008.
[3] A. Turnip and K. S. Hong, “Classifying mental activities From EEG-P300signals using adaptive neural networks,” International Journalof Innovative Computing,Information and Control, pp.6429-6443, 2012.
[4] https://zh.wikipedia.org/wiki/%E8%82%8C%E8%90%8E%E7%BC%A9%E6%80%A7%E8%84%8A%E9%AB%93%E4%BE%A7%E7%B4%A2%E7%A1%AC%E5%8C%96%E7%97%87
[5] http://en.wikipedia.org/wiki/10-20_system_(EEG)
[6] http://www.bem.fi/book/13/13.htm
[7] http://doc1856.blogspot.tw/2009/12/blog-post_19.html
[8] https://zh.wikipedia.org/zh-tw/%E5%A4%A7%E8%84%91%E7%9A%AE%E8%B4%A8
[9] 陳昆顯 何淑君，TANET2013臺灣網際網路研討會-【論文集】腦波儀研究在各領域之應用，高師大資訊教育所
[10] 李思群，一種基於腦癱FP1和FP2腦波希爾伯特-黃轉換時頻特徵分析 ，國立臺灣海洋大學電機工程學系碩士論文，中華民國103年6月。
[11] http://en.wikipedia.org/wiki/Electroencephalography
[12] R. Yan and R. X. Gao,”A Tour of the Hilbert-Huang Transform: An Empirical Tool for Signal Analysis,” IEEE Instrumentation &; Measurement Magazine, pp.40-45 ,2007.
[13] C. F. Lin, and J. D. Zhu, “Hilbert-Huang Transformation Based Time-frequency Analysis Methods in Biomedical Signal Applications,” Proceedings of the Institution of Mechanical Engineers, Part H, Journal of Engineering in Medicine, pp.208-216, 2012.
[14] M. C. Wu, and N. E. Huang, “Biomedical Data Processing Using HHT:A Review,” Advanced Biosignal Processing, pp.335-352, 2009.

[15] https://zh.wikipedia.org/wiki/%E5%B8%8C%E7%88%BE%E4%BC%AF%E7%89%B9-%E9%BB%83%E8%BD%89%E6%8F%9B

[16] J. D.Bronzino, Biomedical Engineering and Instrumentation Basic Concepts and Applications, pp.149-150, 1986.
[17] 余伍洋，楊寬弘，陳明招，「聽覺誘發波在精神生理學之臨床意義」，中華精神醫學，pp.3-14, 1994。
[18] C. R, Osselton JW, and J. C. Shaw, EEG Technology, 2nd ed., 275 pp. Butterworths, London, 1969.
[19] H.H. Jasper. “The ten-twenty electrode system of the International Federation,” Electroencephalogr ClinNeurophysiol, pp.371-375, 1958.
[20] J. D. Zhu, C. F. Lin, S. H. Chang et al., “Analysis of Spike Waves in Epilepsy Using Hilbert-Huang Transform,” Journal of Medical Systems, 170, 2015.
[21] C. F. Lin, S. W. Yeh, S. H. Chang, T. I. Peng, and Y. Y. Chien. An HHT-based Time-frequency Scheme for Analyzing the EEG Signals of Clinical Alcoholics, pp.1-26, 2011.
[22] C. F. Lin, B. H. Yang, T. I. Peng, S. H. Chang, Y. Y. Chien, and J. H. Wang, “Sharp Wave Based HHT Time-frequency Features with Transmission Error,” Advance in Telemedicine: Technologies, Enabling Factors and Scenarios, pp.149-164. , 2011.
[23] R. T. Lauer, C. A. Stackhouse, P. A. Shewokis , et al.,”A time-frequency based electromyographic analysis technique for use in cerebral palsy", Gait&;Posture, pp.420-427,2007.
[24] 楊少元，一種基於多發性硬化症者P300-BCI FP1和 FP2 之腦波時頻特徵分析，國立臺灣海洋大學電機工程學系碩士論文初稿，中華民國104年6月。
[25] 孫本群，一種基於外傷性腦和脊膸損傷P300-BCI FP1和FP2腦波之希爾 伯特-黃轉換時頻特徵分析，國立臺灣海洋大學電機工程學系碩士論文初稿， 中華民國104 年6月。