基於黎曼幾何之改良型共同空間型樣法用於想像運動之腦波分類

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姓名

柯霽恩(Ji-En Ke) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

基於黎曼幾何之改良型共同空間型樣法用於想像運動之腦波分類
(Classification of Motor Imagery EEG Signals using Improved CSP based on Riemannian Geometry)

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至系統瀏覽論文 (2024-8-19以後開放)

摘要(中)

本論文主要研製一基於黎曼幾何空間之想像運動的特徵提取演算法，在想像運動的腦電訊號分類中，共同空間型樣法是一個常被用來提取腦電訊號特徵的演算法，透過共同空間濾波器進行資料重組，去除事件不相關雜訊的影響，強化事件相關的腦波特徵，藉以極大化不同訊號群組之間的差異性。相較於腦電訊號的協方差矩陣位在傳統歐式空間；黎曼幾何空間更能夠表達腦電訊號在空間中距離分布。因此本論文以黎曼幾何空間作為基礎，改良現有共同空間型樣法的演算法架構，並利用黎曼幾何空間和切線空間的轉換，提升腦電訊號特徵提取的效果，最後透過BCI競賽和自錄的腦電訊號驗證其分類的準確度有明顯的提升。

摘要(英)

This thesis, based on Riemannian geometric space, focuses on the design and implementation of a classification algorithm for motor imagery Electroencephalography(EEG). When classifing imaginary brain electrical signals, the common spatial pattern method is often used to extract the feature of EEG signals. The common spatial filter performs data reorganization to remove the effects of event-unrelated noise and enhance the EEG feature associated with the event. Thereby it maximizes the difference between different signal groups. Note that the distance distribution of the covariance matrix of the EEG signal located in Riemannian geometry space, that is more distinguishable than that in the traditional Euclidean space. Therefore, based on Riemannian geometric space, this thesis uses the transformation of Riemannian geometric space and tangent space combined with the existing common spatial pattern method to improve the EEG feature extraction effect. Finally, BCI competition and the self-recorded EEG signals are used to verify that the classified accuracy of the proposed method is significantly effective.

關鍵字(中)

★ 腦電圖
★ 想像運動
★ 黎曼幾何
★ 切線空間
★ 共同空間型樣法
★ 線性區別分析

關鍵字(英)

★ electroencephalography
★ motor imagery
★ Riemannian geometry
★ tangent space
★ common spatial pattern
★ linear discriminant analysis

論文目次

摘要 I
Abstract II
致謝 III
目錄 IV
圖目錄 VII
表目錄 X
第一章緒論 1
1-1 前言 1
1-2 研究動機與目的 2
1-3 文獻回顧 3
1-4 內容大綱 5
第二章腦電訊號 6
2-1 大腦活動區 6
2-2 腦波種類簡介 7
第三章演算法原理與分析 8
3-1 黎曼幾何 8
3-1-1 前言 8
3-1-2 對稱正定矩陣之定義與特性 9
3-1-3 黎曼幾何距離 11
3-1-4 指數/對數投影 13
3-1-5 SPD矩陣的平均值 16
3-2最短黎曼距離CSP設計與實現 18
3-2-1 共同空間型樣法 19
3-2-2 最短距離到黎曼均值 23
3-2-3 最短黎曼距離CSP 24
3-3 線性區別分析 26
3-3-1組內分散量 27
3-3-2 組間分散量 29
3-3-3 最佳化投影矩陣 30
第四章實驗與討論 31
4-1 腦波資料 31
4-1-1 BCI 競賽 IV IIa 31
4-1-2 自行錄製之腦波資料 32
4-2 CSP 之濾波器參數n值比較 33
4-3 切線空間之比較 44
4-4 實驗結果 48
第五章結論與未來展望 54
參考文獻 55

參考文獻

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指導教授

徐國鎧

審核日期

2019-8-21

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