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姓名	黃時霖(Shih-Lin Huang)  查詢紙本館藏	畢業系所	物理學系
論文名稱	基於最小範數法的腦電磁訊號源定位 (Source localization of brain electromagnetic signals based on minimum norm method)
檔案	[Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   [檢視]  [下載] 本電子論文使用權限為同意立即開放。 已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。 請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。
摘要(中)	我們提出了一個在最小範數法的架構內，從腦磁/電圖估算腦可能的活動區域的方法。這個方法將整個大腦以格點覆蓋，每個格點和少量格點組合求最小範數解，利用少量格點的組合之中源的解會大於多數非源格點解的性質，經由統計每個格點在多次不同的組合中解大於每個組合半數格點之解的次數，篩選出可能是源的格點，再對篩選出的格點進行源分布迭代(SIMN)定位源的位置。數值模擬顯示本方法大幅提升源定位正確率，用人臉辨識腦磁圖數據對本方法進行測試，結果和fMRI的結果一致。
摘要(英)	A method in the framework of minimum norm estimate(MNE) is proposed to identify the possible active regions of the brain from MEG/EEG data. The whole brain is covered with grid points. Each grid point is teamed up with a small number of grid points to calculate the MNE of these points. In a group of few grid points, the MNE of source grid point is in general greater than that of non-source grid points. By considering the ensemble of each grid point teaming up with few grid points of a fixed number, the statistic of the number of times that each grid point’s MNE is greater than half of the grid points’ MNE in each combination enables us to identify the possible source grid points. SIMN is applied to these grid points to determine the positions of sources. Numerical simulations show that the accuracy of source localization is greatly improved. This method is applied to the facial recognition MEG data and the results are consistent with the results of fMRI.
關鍵字(中)	★ 最小範數估計 ★ 腦磁圖儀 ★ 腦電圖儀 ★ 逆問題 ★ 奇異值分解 ★ 源分布迭代最小範數法	關鍵字(英)	★ MNE ★ MEG ★ EEG ★ inverse problem ★ SVD ★ source iteration of minimum norm, SIMN
論文目次	摘 要 I Abstract II 第一章 緒論 1 第二章 基於最小範數法的腦電磁訊號源定位 3 2-1 　最小範數估計的問題及發展 3 2-2 　leadfield的正交規範性(orthonormality) 4 2-3 　相應格點的雜訊調控 5 2-4 　格點篩選的依據 6 2-5 　論文方法的演算步驟 11 第三章 　數值模擬驗證 12 3-1 　數值模擬環境的建置 12 3-2 　篩選格點的實用性 12 3-3 　源定位的正確率 20 第四章 　真實數據應用 33 4-1 　數據背景及前處理 33 4-2 　源定位的計算 35 4-3 　統計檢定 37 第五章 結論 39 參考文獻 40
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指導教授	王敏生 黎璧賢(Min-Sheng Wang Pik-Yin Lai)	審核日期	2020-7-24
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