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研究生: 楊永毅
Yang, Yung-I
論文名稱: 利用功能性近紅外線光譜及獨立成分分析演算法來評估更新作業的活化腦區
Using Independent Component Analysis Algorithm to Evaluate Activated Brain Regions in Updating Task by Functional Near-Infrared Spectroscopy
指導教授: 劉奕汶
Liu, Yi-Wen
陳新
Chen, Hsin
口試委員: 周育如
Chou, Yu-Ju
陳欣進
Chen, Hsin-Chin
學位類別: 碩士
Master
系所名稱: 電機資訊學院 - 電機工程學系
Department of Electrical Engineering
論文出版年: 2018
畢業學年度: 106
語文別: 中文
論文頁數: 122
中文關鍵詞: 功能性近紅外線光譜前額葉工作記憶快速獨立成分分析
外文關鍵詞: functional near-infrared spectroscopy, prefrontal cortex, working memory, fast independent components analysis
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  • 本論文利用功能性近紅外線光譜(functional near-infrared spectroscopy, fNIRS) 獲取大腦前額葉血紅素濃度的變化,來檢驗前額葉區是否對不同的記憶更新材料,呈現不同的活化區域和活化程度。
    本研究主題為工作記憶更新作業,採用四種刺激材料與更新作業:(1)字母記憶作業、(2)音調監控作業、(3)空間回數作業及(4)數字回數作業。在作業進行時,受試者必須根據新刺激的不斷出現,持續更新記憶中的答案,並在題目結束後回憶正確答案。
    在fNIRS的訊號處理方面,係利用了快速獨立成分分析(fast independent components analysis, fastICA)演算法進行盲訊號分離(blind source separation, BSS),來將血紅素濃度訊號進行特徵提取與資料降維,所得到的獨立成分再利用平均試驗間相關性(Mean inter-trial cross-correlation, MITC)以進行訊號block相關程度的評估,找出最具有block相關性的獨立成分,即為因為神經活化的變化所引發的血紅素濃度改變。
    研究結果主要發現有以下三項:(1)不同於文獻中的功能性磁振造影結果,空間回數作業與數字回數作業比較下,發現有dorsal 及ventral prefrontal cortex的分區活化現象、(2)結果觀察到字母、空間及數字素材偏向右腦活化,音調偏向左腦活化、(3)若依據記憶更新答題的表現,比較最高表現組與最低表現組的受試者的fNIRS訊號結果,在字母記憶作業及數字回數作業中可以發現低行為表現組的prefrontal cortex腦區活化值大於高行為表現組,但在音調監控作業及空間回數作業中則呈現高行為表現組的prefrontal cortex腦區活化值大於低行為表現組。總結來說並沒有發現如文獻所說低行為表現組會有較低的腦部活動,高行為表現組則較多腦部活動。


    This thesis aims to use functional near-infrared spectroscopy (fNIRS) for obtaining changes in the concentration of hemoglobin in the prefrontal cortex, and tests whether the prefrontal cortex showed different activation areas and activation levels for different memory stimulating materials.
    The experimental design is based on working memory updating task, and uses four stimulating materials and updating task: (1) letter memory task, (2) tone monitoring task, (3) spatial N-back task, and (4) digital N-back task. While the task progresses, subjects must continually update the answers in memory based on the constant appearance of new stimuli and recall correct answers after questions end.
    In the signal processing, the fast independent components analysis (fastICA) algorithm is used as the practice of blind source separation (BSS). This method can apply in feature extraction and dimensionality reduction of hemoglobin concentration signal. Independent components(ICs) can be extracted after fastICA, and these ICs can be evaluated by mean inter-trial cross-correlation (MITC) for the correlation between signal events. The block-related ICs can be regarded as hemoglobin concentration changes caused by neural activation.
    The main findings of the study are as follows: (1) Different from the results of functional magnetic resonance imaging in the literature, the spatial and digital memory updating task trigger the activation of dorsal and ventral prefrontal cortex respectively, (2) The results show that letters, spatial and digital materials are lateralized to the right brain and the tone material to the left brain, (3) Comparing the subjects in the high performance group (HPG) and low performance group (LPG), in letter memory task and digital N-back task, the prefrontal cortex activation of LPG is stronger than of HPG. On the other hand, in tone monitoring task and spatial N-back task, the prefrontal cortex activation value of HPG is stronger than that of LPG. In summary, it is not found that LPG had lower brain activity, while HPG had stronger brain activity as in the literature.

    摘要 iv Abstract v 致謝 vii 目錄 viii 圖目錄 x 表目錄 xiv 第一章、緒論 1 1.1 研究背景 1 1.2 研究貢獻 2 1.3 論文架構 3 第二章、文獻回顧 5 2.1 大腦功能影像(Brain Functional Image) 5 2.2 工作記憶 (Working Memory) 9 2.3 大腦血流動力學(Cerebral hemodynamic) 11 2.4 快速獨立成分分析演算法(fastICA Algorithm) 12 2.4.1 中心化(Centering) 14 2.4.2 主成分分析(Principal Components Analysis) 14 2.4.3 白化(Whitening) 14 2.4.4 獨立成分分析(Independent Component Analysis) 15 第三章、研究材料與方法 17 3.1 研究假設 17 3.2 研究設計 17 3.3 功能性近紅外線光譜(functional near infrared spectroscopy, fNIRS) 17 3.3.1 血紅素濃度轉換演算法 18 3.3.2 研究儀器Hitachi OT R-40 系統 19 3.4 作業設計 21 3.4.1字母記憶作業(Letter Memory Task) 22 3.4.2音調監控作業(Tone Monitoring Task) 22 3.4.3空間回數作業(Spatial N-back Task) 24 3.4.4數字回數作業(N-back Task) 24 3.5 研究樣本 26 3.6 訊號處理流程 27 3.6.1資料前處理 27 3.6.2快速獨立成分分析 28 3.6.3 平均試驗間相關性(Mean inter-trial cross-correlation) 30 3.6.4 資料重建與資料標準化 31 3.6.5 通道圖與腦圖 32 第四章、單一作業結果 35 4.1 受試者行為測驗分數 35 4.2 訊號處理與演算法應用於血紅素濃度訊號 36 4.2.1 獨立成分選取 38 4.2.1 MITC數值選取 42 4.2.3 t檢定方式 46 4.3 受試者結果 49 4.3.1 字母記憶作業結果 49 4.3.2 音調監控作業結果 53 4.3.3 空間回數作業結果 57 4.3.4 數字回數作業結果 61 4.3.5 高表現組與低表現組比較 65 4.3.6 左右腦比較 68 4.4 章節總結 75 第五章、多作業比較結果 76 5.1 空間作業與非空間作業比較 76 5.2 音調監控作業與其他作業比較 79 5.3 字母記憶作業與數字回數作業比較 82 5.4 不同行為表現組別的跨作業比較 85 第六章、結論與未來工作 87 6.1 結論 87 6.2 未來工作 88 參考文獻 89 附錄 93 A. 通道在大腦空間分布表 93 B. FastICA應用在模擬訊號執行過程 98 B.1 演算法訊號模擬 98 B.1.1 中心化 99 B.1.2 主成分分析 100 B.1.3 白化 102 B.1.4 獨立成分分析 103 C. 單一受試者通道圖分布結果 106 C.1 字母記憶作業通道圖分布 106 C.2 音調監控作業通道圖分布 110 C.3 空間回數作業通道圖分布 114 C.4 數字回數作業通道圖分布 118

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