基於共同空間型樣之黎曼普氏分析法用於想像運動之腦波分類;Classification of Motor Imagery EEG Signals Using CSP-RPA

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题名:	基於共同空間型樣之黎曼普氏分析法用於想像運動之腦波分類;Classification of Motor Imagery EEG Signals Using CSP-RPA
作者:	邱信豪;Chiu, Hsin-Hao
贡献者:	電機工程學系
关键词:	腦電圖;想像運動;黎曼幾何;切線空間;普氏分析;領域自適應;Electroencephalographic;Motor Imagery;Riemannian geometry;tangent space;domain adaptation;transfer learning
日期:	2020-07-28
上传时间:	2020-09-02 18:26:49 (UTC+8)
出版者:	國立中央大學
摘要:	本篇論文以黎曼幾何空間為基礎，提出了一種解決腦波數據非平穩性的方法，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。腦波訊號普遍存在著不平穩(Non-stationary)的特性，即其數據分布隨著時間變化，而這個特性使得腦波在想像運動的分類上受到限制。在想像運動腦波錄製過程中，由於受測者不同或受測者錄製腦波的時間、環境不同，導致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。在傳統腦機介面中，雖然此情況可透過大量收集受測者的腦波資料以進行校準，然而這將導致系統必須花費過長的校準時間以保持原有的分類準確率。而腦機介面領域中的遷移學習方法，將可以藉由源域的資料應用於目標域的資料中，因此，可以減少大量錄製腦波資料所需要的時間。本篇主要研究內容如下：本論文提出CSP-RPA 方法，以黎曼幾何空間之切線空間作為基礎，結合共同空間型樣法(Common Spatial Pattern, CSP)將不同類別之資料事先盡可能區分開後以改良現有黎曼普氏分析架構，並且利用基於樹的特徵選擇(Tree-based Feature Selection)方式，以減少對於少量腦波數據的特徵維度過高所造成的過擬合(Over-fitting)情況，進而提升腦電訊號想像運動分類的效果，最後透過BCI 競賽所提供的腦電訊號數據集驗證其演算法之有效性，並利用t-SNE 視覺化以證明其特徵領域自適應之效果。;This thesis presents a transfer learning method based on Riemannian geometry for improving the classification accuracy of Electroencephalographic (EEG) signals. Non-stationarities are ubiquitous in EEG signals, which means the statistical characteristics of EEG signals alter from time to time. The nonstationarities of the EEG signals may be caused by different environmental factors (e.g. user’s fatigue level, the mental and physical state of user, the location of electrodes placement, etc.). Typically, classic Motor Imagery-based Brain- Computer Interface (MI-based BCI) requires a calibration session in each run, even for recorded subjects. During the calibration session, the subjects requested to perform various Motor Imagery (MI) tasks repeatedly, which will be timeconsuming and make user feel exhausted. As a consequence, we proposed a transfer learning method, namely Common Spatial Pattern Riemannian Procrustes Analysis (CSP-RPA), to shorten the calibration time while keeping MI-based BCI work optimally. CSP-RPA is based on the tangent space of Riemannian geometric spaces and combines the Common Spatial Pattern (CSP) method to modify the Riemannian Procrustes Analysis (RPA) architecture. To alleviate the overfitting in high-dimensional Riemannian manifold, the tree-based feature selection is adopted to reduce the dimensionality after mapping data from Riemannian manifold to tangent space. The framework was validated by the publicly available EEG dataset 2a of the BCI competition IV. In addition, we used t-SNE (t- Distributed Stochastic Neighbor Embedding) to visualize and prove the effectiveness of feature domain adaptation after CSP-RPA algorithm. To sum up, the experimental results indicate that CSP-RPA is superior to other methods, e.g., Re-center, Parallel Transport, RPA, under cross-sessions and cross-subjects conditions.
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