中風病患之下肢運動想像腦波分析

腦機介面(Brain-Computer Interface, BCI)在近年來的研究中已經不只如過往是用以使運動功能嚴重受損的病患(如：中風患者、漸凍症)能夠利用其腦波(EEG)直接與外界溝通或控制外界的裝置，而是以BCI研究領域中的運動想像這個主題作為復健的一環。為了能夠有效發展基於運動想像的BCI復健系統，本文基於下肢運動想像而設計了一套實驗流程，會分別擷取受測者放鬆狀態、左腳運動想像以及右腳運動想像時的腦波。並將受測者分為中風、年輕健康、老年健康三個組別，分析單側腳與放鬆狀態時的腦波辨識、運動想像時的腦拓撲圖，並以統計的方式加以探討年紀差異、中風患者的健側、患側差異，與中風患者與健康者之間的差異性。進行腦波辨識時用到的特徵抽取方法為頻帶功率(Band Power, BP)、共同空間模式(Common Spatial Pattern, CSP)，而分類器則使用K個最近鄰居法(K-Nearest Neighbor, K-NN )以及支持向量機(Support Vector Machine, SVM)。實驗結果顯示使用特徵抽取CSP搭配分類器SVM時，年輕健康組在單側腳與放鬆狀態時的分類率可達89.94%(左)、88.94%(右)，而老年健康組則為91.88%(左)、88.06%(右)，中風組則為87.19%(患側)、85.56%(健側)。進行腦拓撲圖及統計結果的探討則可發現，年輕健康組與老年健健康組不管是在右腳運動想像以及左腳運動想像皆可發現有許多的顯著差異，說明年紀對下肢運動想像的影響。中風組健側腳與老年健康組的比較可以發現，在8-12Hz時有較多的顯著差異，且在0~2秒時無顯著差異。而中風組患側腳與老年健康組的比較可以發現，在16-20Hz時有較多的顯著差異，且有顯著差異的多數頻帶顯示在FZ、CZ、CPZ這三個電極，另外時間區間則是在0~2秒時皆無顯著差異。綜合上述結果在未來將有助於開發一套中風患者基於下肢運動想像的BCI復健系統。

關鍵字

中風；復健；腦機介面；運動想像

並列摘要

BCIs (Brain-Computer Interface) used to help people who suffered for severe motor–disabilities (such as stroke or ALS patients) to interact with external world or control devices. In recent years, many BCIs for stroke rehabilitation with motor imagery have been report. To develop a BCI system for rehabilitation, this thesis designs an experimental procedure based on lower limb motor imagery. The experiment consists of three states, including resting state, left foot motor imagery and right foot motor imagery. Participants are divided into three groups, stroke, healthy young and healthy elderly, respectively. This thesis analyzes the classification rate of unilateral foot motor imagery and resting state, brain topography, and use results of statistics to find EEG difference between young and elderly, sound and affected side, stroke patients and healthy elderly. Feature extraction methods include Band Power (BP), Common Spatial Pattern (CSP). This thesis employs K-nearest neighbor (K-NN) and Support Vector Machine (SVM) as classifier. The results show that CSP feature extraction method combined with SVM classifier can obtain the best classification rate between unilateral foot motor imagery and resting state. In the group of healthy young, classification rates are 89.94% (left vs. resting) and 88.94% (right vs. resting). In the group of healthy elderly, classification rates are 91.88% (left vs. resting) and 88.06% (right vs. resting). In the group of stroke, classification rates are 87.19% (affected side vs. resting) and 85.56% (sound side vs. resting). According to brain topography and the results of statistics, many significant differences between healthy young and healthy elderly in left foot motor imagery and right foot motor imagery are found. It indicates that lower limb motor imagery has age effect. Compare the stroke sound side with healthy elderly, 8-12 Hz have more significant differences, many electrode sites show significant differences in the frequency band of 8-12 Hz. There are no significant differences in period of 0~2 s. Compare the stroke affected side with healthy elderly, 16-20 Hz have more significant differences, and lots of bands show significant differences in electrodes FZ、CZ、CPZ. There are no significant differences in period of 0~2 s. Summarizing all the results, development of a low limb motor imagery-based BCI rehabilitation system for stroke patients is possible in the future.

並列關鍵字

Stroke ； rehabilitation ； BCI ； Motor Imagery.

參考文獻

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[31] 黃俊偉, 非同步腦機介面方法比較及即時腦控開關之發展,中原大學機械工程研究所碩士學位論文, 2013.

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中風病患之下肢運動想像腦波分析

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