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研究生: 陳致仰
論文名稱: 改良式對角化主要成份分析法應用於兩類別想像動作腦電波的分類
Modified Diagonal Principal Component Analysis Applied to Two-Class Motor Imagery EEG Classification
指導教授: 葉榮木
Yeh, Zong-Mu
蔡俊明
Tsai, Chun-Ming
學位類別: 碩士
Master
系所名稱: 機電工程學系
Department of Mechatronic Engineering
論文出版年: 2007
畢業學年度: 95
語文別: 中文
論文頁數: 59
中文關鍵詞: 大腦人機介面對角化主要成份分析法腦電波時-頻-空域分析
論文種類: 學術論文
相關次數: 點閱:150下載:3
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  • 本論文提出一個有效的方法,對受測者在意圖抬起左手小指頭與意圖吐舌頭時的腦電波做辨識。腦電波辨識是否成功的關鍵,在於特徵擷取與分類兩個議題。過去文獻將重點放在分類演算法的改良上,然而找出更簡單而重要的特徵,也可以獲得高辨識率。對角化主要成份分析法(DiaPCA)可以從腦電波高維度的時-頻-空域資料矩陣中找出主要的成份。被挑選出來的主要成份可構成一個較低維度的特徵矩陣,但仍保有兩種想像動作的腦電波之間主要的特徵差異。因此,藉由計算特徵矩陣間的歐氏距離就可分類腦電波。這個方法比起其他分類演算法,如支持向量機(SVM),不但較為簡單,而且不會降低辨識率。本論文提出利用「改良式對角化主成份分析法」對腦電波擷取特徵並辨識,結果顯示,腦電波辨識的準確率大幅提升了10.07%。

    目錄 摘要 I Abstract II 目錄 III 圖目錄 V 表目錄 VII 第一章 緒論 1 1.1 研究動機 1 1.2 腦電波 2 1.3 大腦人機介面的控制源 7 1.4 研究目的 10 1.5 研究流程 11 1.6 論文架構 12 第二章 文獻探討 13 2.1 μ波大腦人機介面的文獻 13 2.2 特徵擷取的趨勢 15 2.3 針對同一腦電波資料集做辨識的文獻 17 2.4 文獻回顧整理 22 第三章 特徵擷取與分類 23 3.1 混合時間、頻率與空間域特徵的初始資料矩陣 23 3.2 以對角化主要成份分析法(DiaPCA)擷取主要特徵 26 3.3 以改良式最近鄰居分類法分類腦電波 29 第四章 腦電波資料集 35 4.1 資料集I 35 4.2 資料集II 36 4.3 資料前處理 42 4.4 資料後處理 44 第五章 實驗結果 47 5.1 PCA與DiaPCA的比較 47 5.2 DiaPCA最佳的參數值組合 48 5.3 改良式DiaPCA的分析 49 5.4 改良式DiaPCA與其他方法的比較 52 第六章 結論與未來工作 58 參考文獻 59

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