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%。
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