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姓名	陳俞豪(Yu-Hao Chen)  查詢紙本館藏	畢業系所	電機工程學系
論文名稱	基於領域自適應法結合多階層歐式空間與黎曼流形對齊之想像運動腦波分類 (Classification of Motor Imagery EEG using Multi-Stage Euclidean Space and Riemannian Manifold Alignment based on Domain Adaptation)
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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]   至系統瀏覽論文 (2028-7-23以後開放)
摘要(中)	腦電訊號（Electroencephalography, EEG）存在著不平穩(Non-stationary)的特性，由於受試者不同或相同受試者但錄製腦波的時間、環境不同，使腦電訊號之間存在巨大差異，造成想像運動分類準確率不佳，最終難以訓練出通用性高的分類模型。若是能夠運用遷移學習法，利用事先錄製的 EEG 訊號遷移至當前的分類任務，不僅可以減少大量錄製腦波資料所需要的時間，還能使分類模型有足夠的訓練集資料提升分類準確度。 為此本論文提出基於領域自適應方法將大量帶有標籤(Label)的腦波從 源域遷移至少量標籤的目標域中，透過歐式空間與黎曼流行兩邊各自資料 對齊的方式，完成多分類遷移學習之想像運動，提高跨時段(Cross-sessions)及跨受試者(Cross-subjects)在想像運動上的分類性能。首先計算電極通道與各個類別黎曼均值之間的黎曼距離進行腦波電極通道選擇，通過濾波器組(Filter Bank)並基於互訊息之頻帶選擇法選擇最佳頻帶，再結合歐式空間的資料中心對齊法(Euclidean Space Data Alignment, EA)使源域與目標域資料圍繞同一中心，降低源域和目標域之間的差異性，接著由共空間模式(Common Spatial Patterns, CSP)提取一部分特徵，同時經由共變異數矩陣中心對齊(Covariance Matrix Centroid Alignment, CA)後從黎曼流形切線空間映射(Tangent Space Mapping, TSM)方法提取另一部分特徵，結合兩種特徵後透過領域可遷移性評估(Domain Transferability Estimation, DTE)選擇腦波資料，最終透過基於支持向量機(Support Vector Machine, SVM)的分類模型訓練以及分類。由 BCI 競賽資料集驗證，實驗結果顯示基於領域自適應的想像運動分類準確率與近幾年提出的架構相比準確度最高。
摘要(英)	Electroencephalography (EEG) has non-stationary characteristics, leading to a large difference in EEG classification accuracy due to different subjects or the same subject′s brainwaves at different times and environments. Finally, it is difficult to train a robust classification model. If the transfer learning method can be used to transfer pre-recorded EEG signals to the current classification task, it can make the classification model have enough training set data to improve the classification accuracy. Therefore, this paper proposes a domain adaptive method to transfer a large number of brainwaves with labels from the source domain to the target domain with a small number of labels. To improve the classification performance of Crosssessions and Cross-subjects in motor imagery, the Riemannian distance between the electrode channel and the Riemannian mean of each category was calculated to screen the brainwave electrode channel, and the optimal frequency band was screened by Filter Bank and based on the mutual information band screening method. Combined with Euclidean Space Data Alignment (EA), the data of the source domain and target domain revolve around the same center, reducing the difference between source domain and target domain. Some features were extracted from Common Spatial Patterns (CSP). The other features are extracted from Tangent Space Mapping (TSM) method of Riemannian manifold after being aligned through Covariance Matrix Centroid Alignment (CA) at the same time. Combined with two features, brainwave data are screened through Domain Transferability Estimation (DTE). Finally, the classification model is trained and classified based on Support Vector Machine (SVM). The results showed that the accuracy of classification was obviously better than other algorithms through BCI competition EEG dataset.
關鍵字(中)	★ 腦電圖 ★ 腦機介面 ★ 想像運動 ★ 遷移學習 ★ 領域自適應 ★ 資料對齊	關鍵字(英)	★ EEG ★ brain-computer interface ★ motor imagery ★ transfer learning ★ domain adaptation ★ data alignment,
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 6 第二章 腦電訊號 7 2-1腦機介面 7 2-1大腦活動區 8 2-2腦波頻帶 10 2-3想像運動 10 2-4領域自適應於腦機介面發展 11 第三章 演算法原理與分析 12 3-1演算法架構 12 3-2資料前處理 14 3-3電極通道選擇 16 3-3-1 黎曼幾何概念 16 3-3-2 黎曼幾何距離 17 3-3-3 黎曼指數投影和對數投影 20 3-3-4 黎曼均值 21 3-3-5 黎曼距離電極通道選擇演算法 23 3-4 濾波器組頻帶選擇 25 3-4-1 黎曼切線空間投影 26 3-4-2基於互訊息之濾波器組頻帶選擇法 27 3-5 資料對齊法 29 3-5-1 歐式空間資料對齊 29 3-5-2 共變異數矩陣中心對齊 31 3-6 特徵提取 32 3-6-1 共空間模式 32 3-6-2 特徵結合 36 3-7 腦波資料試驗選擇 36 3-8分類 38 第四章 實驗結果與討論 40 4-1 EEG資料集 40 4-1-1 BCI Competition IV 2a資料集 40 4-1-2 BCI Competition III 3a資料集 41 4-3比較方法與實驗結果 44 4-3-1跨時段遷移 47 4-3-2跨受試者遷移 58 4-4電極通道選擇演算法效果分析 64 4-5頻帶選擇演算法效果分析 68 第五章 結論與未來展望 73 5-1結論 73 5-2未來展望 74 參考文獻 75
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指導教授	徐國鎧(Kuo-Kai Shyu)	審核日期	2023-7-20
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