基於共空間模式與黎曼流形之即時腦波多分類

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姓名

董凱仁(Kai-Jen Tung) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

基於共空間模式與黎曼流形之即時腦波多分類
(Common Spatial Patterns and Riemannian Manifold based Real-Time Classification of Multiclass EEG)

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至系統瀏覽論文 (2027-7-19以後開放)

摘要(中)

本論文基於共空間模式(Common Spatial Patterns, CSP)與黎曼流形(Riemannian Manifold)切線空間映射(Tangent Space Mapping, TSM)，用於腦電訊號(Electroencephalography, EEG)多分類任務上。藉由濾波器組(Filter Bank)與共變異數矩陣(Covariance Matrix)計算出各個通道間的頻譜功率，再用共空間模式與黎曼流形的切線空間映射分別提取特徵，最後透過基於支持向量機(Support Vector Machine, SVM)的新型分類器分類。資料集使用BCI competition IV 2a想像運動(Motor Imagery, MI)四分類準確率達到78.55%，BCI competition III 3a想像運動四分類準確率達到83.33%，自行錄製之想像運動四分類準確率達到57.44%，自行錄製之實際運動四分類準確率達到81.25%。

摘要(英)

This paper is based on common spatial patterns (CSP) and Riemannian Manifold tangent space mapping (TSM) for Electroencephalography (EEG) of multiclass classification tasks. The spectral power between each channel is calculated by the filter bank and the covariance matrix, and then the features are extracted by the CSP and TSM respectively, and finally the new classifier based on Support Vector Machine (SVM) is used to classify. The datasets used BCI competition IV 2a motor imagery (MI) four-classes accuracy rate achieved 78.55%, BCI competition III 3a MI four-classes accuracy rate achieved 83.33%, self-recorded MI four-classes accuracy rate achieved 57.44%, self-recorded motor movement four-classes accuracy rate achieved 81.25%.

關鍵字(中)

★ 腦電圖
★ 腦機介面
★ 想像運動
★ 多分類
★ 共空間模式
★ 黎曼流形切線空間

關鍵字(英)

★ EEG
★ brain-computer interface
★ motor imagery
★ multiclass class-ification
★ common spatial patterns
★ Riemann tangent space

論文目次

摘要 I
Abstract II
致謝 III
目錄 IV
圖目錄 VI
表目錄 IX
第1章緒論 1
1-1前言 1
1-2研究動機與目的 1
1-3文獻回顧 2
1-4內容大綱 5
第2章腦電訊號 6
2-1腦波訊號頻帶介紹 6
2-2大腦功能區介紹 6
2-3 EEG資料庫 8
2-3-1 BCI競賽IV 2a資料集[22] 8
2-3-2 BCI競賽III 3a資料集[23] 9
2-3-3 自行錄製之腦波資料集 11
2-4 自行錄製腦波軟硬體介紹 12
第3章演算法原理與分析 14
3-1演算法架構 14
3-2資料前處理 15
3-3濾波器組頻帶選擇 17
3-4特徵提取 19
3-4-1黎曼流形切線空間映射 19
3-4-2 共空間模式 24
3-5分類 26
第4章實驗結果與討論 29
4-1本論文提出之DFBTSM-CSP架構演進過程實驗結果 29
4-2頻帶篩選演算法效果分析 33
4-3本論文提出之架構與其他方法準確率比較 34
第5章即時控制系統設計與應用 47
5-1腦波即時控制系統介紹 47
5-2錄製腦波詳細流程 48
5-3即時分類流程及Demo影片 50
5-4硬體耗時比較 51
第6章結論與未來展望 52
6-1結論 52
6-2未來展望 52
參考文獻 53

參考文獻

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指導教授

徐國鎧(Kuo-Kai Shyu)

審核日期

2022-7-28

推文