請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79916完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳志宏(Jyh-Horng Chen) | |
| dc.contributor.author | Yi-Wen Lin | en |
| dc.contributor.author | 林怡彣 | zh_TW |
| dc.date.accessioned | 2022-11-23T09:16:35Z | - |
| dc.date.available | 2022-02-16 | |
| dc.date.available | 2022-11-23T09:16:35Z | - |
| dc.date.copyright | 2022-02-16 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-02-11 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79916 | - |
| dc.description.abstract | "近年來，多模態成像技術常被應用在大腦功能研究中，尤其是神經血管耦合相關的研究領域，經常整合不同影像的結果並探討其機制，而同時取得神經與血管訊號對此耦合現象之研究是至關重要的。非侵入性的功能性磁振造影 (functional Magnetic Resonance Imaging, fMRI) 與腦電圖 (electroencephalography, EEG) 之同步掃描技術，雖然可同時紀錄神經電訊號和來自神經活動所誘發之血管訊號，但卻會對腦電圖產生嚴重干擾：(1) 梯度偽跡與 (2) 心衝擊波偽跡。因此，如何將干擾訊號去除是該同步技術的核心問題之一，也因其訊號處理之技術門檻，使得EEG-fMRI同步技術尚未被廣泛應用。 迄今為止，針對腦電圖之偽跡雖然已提出多種校正方式，但對於何種方法能在校正後得到最佳數據質量，仍未有共識。而市面上雖有針對腦電圖端的處理工具，但在心衝擊波偽跡校正上，除了參數設定較為複雜，且採半自動式抓取心跳複合波，以達到去除心跳干擾的目的，對於掃描時間較長的資料，此步驟的分析將費心耗時。因此，本研究欲改進心衝擊波偽跡校正方式，發展自動檢測心跳複合波演算法，同時也整合多項去除梯度偽跡之方法，加入fMRI頭動參數與主成分分析 (Principal Components Analysis, PCA) 提升偽跡去除效果，並且簡化校正所需參數設定。在MATLAB (MathWorks, Inc., MA, USA) 系統環境下，開發簡易操作之使用者介面，以此建立一個高效的全自動偽跡校正系統 (Automatic Artifact Correction System, AACS)。 與過去研究常使用的商用分析軟體Brain Vision Analyzer 2.0 (Brainproducts Gilching, Germany)、EEGlab的FMRIB工具箱 (Delorme and Makeig, 2004) 相比，本系統成功使MR (Magnetic Resonance) 梯度偽跡的基頻功率多衰減了4.269 %，並且全自動心跳偵測率提升至95 %以上。而心衝擊波偽跡的部分，偽跡殘留量多減少了1.442%，與心跳的相關性也由0.092降至0.073。同時，為了確保偽跡去除乾淨，神經電訊號也有成功被保留下來。我們利用Stroop任務誘發腦電活動，計算事件相關電位 (Event-Related Potential, ERP) 的訊號雜訊比 (Signal-to-Noise Ratio, SNR)，作為評估神經功能的替代指標。以掃描室外所收集到，不受環境干擾之腦電圖作為標準，與分別以Analyzer、FMRIB、AACS三種校正系統進行校正後的EEG做比較。結果發現本研究開發之AACS系統使校正後ERP的SNR，從原先的6.639、10.344提升至11.722，與在掃描室外收集到數據所得的12.378相比，差距大幅減少，成功提升了SNR。 最後，我們也嘗試以三個系統分別校正後的ERP成分，製作空間拓樸圖，觀察健康老化是否導致任務執行差異。結果顯示透過AACS系統去除偽跡的腦電圖，在空間拓樸圖的分布範圍更接近於掃描室外的結果。同時，對比年輕組於任務執行期間的枕葉活化表現，可發現老年組的激活腦區更多轉移至額、頂葉。此結果是以神經電訊號技術，更直接地再現過去研究中利用功能性磁振造影，所觀察到的大腦老化模型，衰老後前移 (Posterior-Anterior Shift in Aging, PASA)。" | zh_TW |
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| dc.description.tableofcontents | "致謝 I 中文摘要 II Abstract IV 目錄 VII 圖目錄 X 表目錄 XII 第一章 緒論 1 1.1 研究動機 1 1.2 論文架構 3 第二章 研究背景 5 2.1 EEG-fMRI同步技術 5 2.1.1 神經血管耦合的重要性 5 2.1.2 頭皮電場訊號來源 7 2.1.2.1 局部場電位 (Local Field Potentials) 7 2.1.2.2 腦神經網絡的振盪 8 2.2 明顯汙染掃描同時所收取腦電圖之偽跡介紹 9 2.2.1 MR梯度偽跡 (MR gradient artifacts (GA)) 10 2.2.2 心衝擊波偽跡 (ballistocardiogram (BCG) artifacts) 11 2.3 常用偽跡校正方式 15 2.3.1 平均偽跡減法 (Average Artifact Subtraction, AAS) 15 2.3.2 最佳基準選擇 (Optimal Basis Selection, OBS) 16 2.3.3 獨立成分分析 (Independent Component Analysis, ICA) 17 2.4 去除偽跡的挑戰 19 2.4.1 去除梯度偽跡的挑戰 19 2.4.2 去除心衝擊波偽跡的挑戰 20 第三章 實驗設計與方法 22 3.1 受試者條件 22 3.2 數據採集 22 3.3 任務刺激與流程 23 3.4 與掃描同步紀錄之腦電圖的前處理 25 3.5 全自動偽跡校正方法 26 3.5.1 MR梯度偽跡校正 27 3.5.2 自動偵測心跳之演算法 30 3.5.3 心衝擊波偽跡校正 33 第四章 驗證偽跡校正成果與實現全自動系統 35 4.1 量化評估AACS系統校正偽跡的成效 35 4.1.1 去除MR梯度偽跡 35 4.1.2 自動心跳峰值偵測 40 4.1.3 去除心衝擊波偽跡 47 4.2 系統介面與使用介紹 58 第五章 系統應用於神經功能評估 61 5.1 Stroop任務所誘發之特定ERP成分介紹 61 5.2 計算事件相關電位之預處理 62 5.3 比較掃描室內、外ERP的SNR與功率拓譜圖 63 5.4 任務刺激後的腦電活動於不同年齡組之變化 67 第六章 結論 77 6.1 偽跡校正成效 77 6.2 AACS系統優勢與限制 79 6.3 神經功能評估結果 81 6.4 未來研究方向 82 參考文獻 84" | |
| dc.language.iso | zh-TW | |
| dc.subject | 事件誘發電位 | zh_TW |
| dc.subject | EEG-fMRI | zh_TW |
| dc.subject | MR梯度偽跡 | zh_TW |
| dc.subject | 心衝擊波偽跡 | zh_TW |
| dc.subject | 腦波偽跡校正 | zh_TW |
| dc.subject | Stroop任務 | zh_TW |
| dc.subject | EEG-fMRI | en |
| dc.subject | Event-Related Potential (ERP) | en |
| dc.subject | Stroop task | en |
| dc.subject | EEG artifact correction | en |
| dc.subject | ballistocardiogram (BCG) artifacts | en |
| dc.subject | MR gradient artifacts (GA) | en |
| dc.title | AACS：EEG-fMRI全自動腦電訊號偽跡校正系統 | zh_TW |
| dc.title | AACS : Development of an Automatic EEG Artifact Correction System for EEG-fMRI | en |
| dc.date.schoolyear | 110-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 吳昌衛(Ching-Ping Shao),林遠彬(Yung-Cheng Chuang),郭立威,吳育德 | |
| dc.subject.keyword | EEG-fMRI,MR梯度偽跡,心衝擊波偽跡,腦波偽跡校正,Stroop任務,事件誘發電位, | zh_TW |
| dc.subject.keyword | EEG-fMRI,MR gradient artifacts (GA),ballistocardiogram (BCG) artifacts,EEG artifact correction,Stroop task,Event-Related Potential (ERP), | en |
| dc.relation.page | 92 | |
| dc.identifier.doi | 10.6342/NTU202200226 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2022-02-13 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 生醫電子與資訊學研究所 | zh_TW |
| 顯示於系所單位： | 生醫電子與資訊學研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-2701202210564900.pdf | 4.37 MB | Adobe PDF | 檢視/開啟 |
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