【摘要】：運(yùn)動想象(Motor Imagery,MI)訓(xùn)練是一種新型康復(fù)訓(xùn)練方法。本文借助腦機(jī)交互系統(tǒng),通過神經(jīng)反饋的方式,對其增強(qiáng)MI康復(fù)訓(xùn)練效果進(jìn)行探索。本文首先提出一種MI康復(fù)訓(xùn)練腦機(jī)交互系統(tǒng)框架,再就MI腦電信號(Electroencephal ogra-m,EEG)的眼電偽跡(Ocular Artifact,OA)去除算法、特征提取算法以及分類算法的編程實現(xiàn)進(jìn)行研究,并構(gòu)建相應(yīng)功能模塊,組成在線MI康復(fù)訓(xùn)練腦機(jī)交互系統(tǒng),并就有無神經(jīng)反饋的情況下,MI訓(xùn)練的效果作對比研究,對所研發(fā)系統(tǒng)的有效性進(jìn)行驗證。本文的主要研究內(nèi)容可分為以下5個方面:(1)本文介紹了系統(tǒng)的基本概念、系統(tǒng)的組成以及國內(nèi)外的研究現(xiàn)狀,并分析目前該類系統(tǒng)研究中的關(guān)鍵技術(shù)難題。同時,了解人腦的結(jié)構(gòu)與EEG產(chǎn)生的機(jī)理以及MI過程中EEG具有的事件相關(guān)去同步/同步(Event-Related Desynchr-onization/Synchronization,ERD/ERS)現(xiàn)象,以此作研究的理論支撐。(2)提出系統(tǒng)的總體架構(gòu)以及各模塊應(yīng)具備的功能,并設(shè)計EEG采集方案,介紹采集所需的實驗設(shè)備和實驗對象,并提出實驗中需要注意的要點(diǎn),最后記錄實驗中具體采集情況。(3)提出一種自動去除OA的方法:首先將水平和垂直眼電(ElectroOculogram,EOG)信號按一定比例混疊成一導(dǎo)新的信號,與EEG一起通過改進(jìn)獨(dú)立分量分析(Improved Independent Component Analysis,IICA)算法獲取各導(dǎo)信號的獨(dú)立分量,再利用相關(guān)系數(shù)自動識別并去除混疊信號獨(dú)立分量,最后通過ICA逆變換獲取純凈EEG。(4)EEG的特征提取與分類研究分二個方面展開:先由小波變換獲取EEG的小波能量,再計算相對小波能量作為特征;再構(gòu)建Logistic分類器對特征進(jìn)行分類。(5)完成EEG在線分析處理功能,與神經(jīng)反饋功能,實現(xiàn)系統(tǒng)整體構(gòu)建。最終,該系統(tǒng)既能分析已保存的EEG,又能在線實時處理EEG,并將處理結(jié)果轉(zhuǎn)換成控制信號,完成虛擬人體模型的控制,反饋用戶MI狀態(tài)。在線實驗結(jié)果表明該系統(tǒng)能輔助受試者更有效地進(jìn)行MI,從而提升康復(fù)訓(xùn)練效果。
[Abstract]:Motor imagination (Motor Imagery,MI) training is a new method of rehabilitation training. In this paper, with the help of brain-computer interaction system, through the way of neural feedback, we explore how to enhance the effect of MI rehabilitation training. In this paper, we first propose a framework of brain-computer interaction system for MI rehabilitation training, and then study the MI EEG signal (Electroencephal ogra-m,EEG) eye artifact (Ocular Artifact,OA) removal algorithm, feature extraction algorithm and the programming implementation of classification algorithm. The corresponding functional modules are constructed to form a brain-computer interactive system for online MI rehabilitation training. The effects of MI training are compared with or without neural feedback, and the validity of the developed system is verified. The main contents of this paper can be divided into the following five aspects: (1) this paper introduces the basic concept of the system, the composition of the system and the research status at home and abroad, and analyzes the key technical problems in the current research of this kind of system. At the same time, we understand the structure of the human brain and the mechanism of EEG generation and the event-related desynchronization / synchronization (Event-Related Desynchr-onization/Synchronization,ERD/ERS) phenomenon of EEG in the process of MI. (2) the overall architecture of the system and the functions of each module are proposed, and the EEG acquisition scheme is designed, the experimental equipment and objects required for the collection are introduced, and the main points needing attention in the experiment are put forward, and the main points for attention in the experiment are put forward, and the main points to be paid attention to in the experiment are put forward. Finally, the specific data collected in the experiment are recorded. (3) an automatic method of removing OA is proposed: firstly, the horizontal and vertical ElectroOculogram,EOG signals are mixed into a new signal in a certain proportion. Together with EEG, an improved Independent component Analysis (Improved Independent Component Analysis,IICA) algorithm is used to obtain the independent components of each derived signal, and then the correlation coefficient is used to automatically identify and remove the independent components of the aliasing signals. Finally, the feature extraction and classification of pure EEG. (4) EEG obtained by inverse ICA transform are divided into two aspects: firstly, the wavelet energy of EEG is obtained by wavelet transform, and then the relative wavelet energy is calculated as the feature; Then the Logistic classifier is constructed to classify the features. (5) the function of on-line analysis and processing of EEG and the function of neural feedback are completed to realize the overall construction of the system. Finally, the system can not only analyze the saved EEG, but also process the EEG, in real time on line and convert the processing results into control signals. The virtual human model can be controlled and the user's MI status can be fed back. The results of on-line experiments show that the system can help the subjects to carry out MI, more effectively and improve the effect of rehabilitation training.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R318.0;TN911.7

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