博碩士論文 985201097 詳細資訊

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姓名 鄧華婷(Hua-Ting Deng)  查詢紙本館藏   畢業系所 電機工程學系
論文名稱 應用獨立成份分析法於大腦運動區近紅外光譜分析
(Analysis of movement-related brain near-infrared signal using independent component analysis)
★ 使用梳狀濾波器於相位編碼之穩態視覺誘發電位腦波人機介面★ 應用電激發光元件於穩態視覺誘發電位之腦波人機介面判斷
★ 智慧型手機之即時生理顯示裝置研製★ 多頻相位編碼之閃光視覺誘發電位驅動大腦人機介面
★ 以經驗模態分解法分析穩態視覺誘發電位之大腦人機界面★ 利用經驗模態分解法萃取聽覺誘發腦磁波訊號
★ 明暗閃爍視覺誘發電位於遙控器之應用★ 使用整體經驗模態分解法進行穩態視覺誘發電位腦波遙控車即時控制
★ 使用模糊理論於穩態視覺誘發之腦波人機介面判斷★ 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
★ 智慧型心電圖遠端監控系統★ 使用隱馬可夫模型於穩態視覺誘發之腦波人機介面判斷 與其腦波控制遙控車應用
★ 使用類神經網路於肢體肌電訊號進行人體關節角度預測★ 使用等階集合法與影像不均勻度修正於手指靜脈血管影像切割
★ 應用小波編碼於多通道生理訊號傳輸★ 結合高斯混合模型與最大期望值方法於相位編碼視覺腦波人機介面之目標偵測
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摘要(中) 手部的運動是人類日常生活中最複雜的運動行為,而控制該部位運作的大腦運動區,牽扯複雜的神經系統,所以成為科學家研究的重點。為了探討大腦運動皮質區的激活反應,研究學者藉由腦造影技術來分析作用區域及生理反應,而近紅外光譜(Near-infrared spectroscopy,NIRS)為近期學界熱烈討論的腦造影技術,因為它具有靈敏的時間解析度及高空間解析度,且能在開放空間中作動態實驗。
  本篇研究提出以近紅外光譜作為握力實驗的大腦掃描方式,套用高階統計學的演算法-獨立成份分析法(Independent Component Analysis,ICA),將訊號分解成許多獨立成份(Independent component,IC),我們先以傳統的平均方法,建立訊號樣版,接著以訊號樣版作為選擇獨立成份的依據,選擇適當的獨立成份,進行訊號重建以萃取出與運動相關的紅外光血流訊號。本研究量測執行左手或右手握拳運動的紅外光訊號,並比較握力量級的振幅變化。本篇研究的實驗結果證實,獨立成份分析法可有效的分離出手部運動訊號,提升訊號訊雜比。
摘要(英) Hand movements is the most complicated motor behaviors in our daily life. The motor cortex, controlling the human motor functions, is a complex system whose working mechanism is an important research issue for scientists. To investigate the functions of motor cortex during movements, researchers analyze hemodynamic and neural activities using brain imaging techniques. The near-infrared spectroscopy (NIRS) has drawn great attention due to its high temporal and spectral resolutions, and can be used to perform experiments in open-space environments.
  In this study, NIRS has been adopted to study motor cortex function in human brain during hand-grip task. The independent component analysis (ICA) was chosen for extracting oxygen and deoxygen hemodynamic responses. For each subject, a temporal template was first constructed for each subject from traditional averaging process. Each independent component (IC) was correlated with the temporal template, and those ICs with high correlation values were chosen as task-related components for constructing noise-suppressed hemodynamic response. This thesis studied the NIRS signals during performing right and left grip tasks with different force levels. Our study results have shown the ICA is an effective tool to improve the signal-to-noise ratio (SNR) of motor-related NIRS signals.
關鍵字(中) ★ 獨立成份分析法
★ 近紅外光譜
關鍵字(英) ★ Near-infrared spectroscopy(NIRS)
★ Independent Component Analysis(ICA)
論文目次 中文摘要 I
英文摘要 II
致謝 III
目錄 IV
圖目錄 VI
表目錄 IX
第一章、緒  論 1
1-1  前言 1
1-2  研究動機 2
1-3  文獻探討 2
1-4  研究目的 2
1-5  論文架構 3
第二章、理論簡介 4
2-1  大腦與運動系統 4
2-1-1 大腦結構與功能 4
2-1-2 運動神經傳遞 6
2-2  擴散光學技術簡介 10
2-2-1 近紅外光譜原理 10
2-2-2 Beer-Lambert law 12
第三章、研究設計與方法 14
3-1  系統架構 14
3-2  實驗設備與儀器 15
3-2-1 近紅外光量測設備 15
3-2-2 電極帽 17
3-2-3 運動紀錄元件 18
3-3  獨立成份分析法 20
3-3-1 獨立成份分析法理論 20
3-3-2 資料前處理 23
3-3-3 獨立成份及去相關矩陣 24
3-3-4 訊號重建 29
第四章、實驗與討論 30
4-1  實驗設計 30
4-2  運動區紅外光訊號 31
4-3  訊號分析 34
4-3-1 訊號前處理 34
4-3-2 建立握拳模型 38
4-3-3 獨立成份分析法 43
4-4  實驗結果 44
4-4-1 ICA實驗結果 44
4-4-2 受測者比較 50
4-4-3 握力量級比較 53
第五章、結論與未來展望 54
參考文獻 55
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指導教授 李柏磊(Po-Lei Lee) 審核日期 2012-7-26
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