本文選題：中風(fēng)康復(fù)運動 + 功能評價�。� 參考：《燕山大學(xué)》2014年碩士論文

【摘要】：腦肌電同步特征分析已經(jīng)成為運動神經(jīng)科學(xué)領(lǐng)域一個新的研究熱點。在人體運動的神經(jīng)控制過程中，大腦感覺運動皮層與肌肉之間的控制反饋聯(lián)系發(fā)揮著至關(guān)重要的作用，這種聯(lián)系體現(xiàn)在腦電信號(Electroencephalogram，EEG)與肌電信號(Electromyography，EMG)間不同層次的同步現(xiàn)象。腦肌電同步特征分析可以反映大腦與肌肉之間的功能聯(lián)系，從系統(tǒng)的層面理解運動控制過程及其運動障礙的病理機(jī)制，為揭示運動控制過程中神經(jīng)網(wǎng)絡(luò)的協(xié)同工作方式提供了理論基礎(chǔ)，也為神經(jīng)康復(fù)運動的功能狀態(tài)評價提供新方法。 本文首先介紹了腦電和肌電信號的產(chǎn)生及特點，分析了神經(jīng)同步活動及分析方法的研究現(xiàn)狀，對比分析了腦肌電耦合特征的多種同步分析方法及各自特點，確定了本文的研究內(nèi)容：分別從一致性同步和廣義同步角度，基于頻域相干性及信息傳遞性分析研究康復(fù)運動中的腦肌電同步特征。 針對腦電和肌電信號的非平穩(wěn)性特點，運用基于時頻譜估計的小波相干性分析方法研究腦肌電頻域成分的線性相關(guān)特性，通過實測數(shù)據(jù)分析驗證了小波相干分析能有效描述腦肌電相干性的時間變化特征。 針對腦肌電間的非線性耦合特性，且運動中特定腦電節(jié)律與肌電信號功能相關(guān)的特點，引入基于信息熵的兩通道關(guān)聯(lián)特征分析方法，提出基于小波分解的改進(jìn)信息傳遞指數(shù)指標(biāo)，從非線性和方向性角度研究腦肌電信號間的信息流動特性。通過仿真及實測數(shù)據(jù)分析表明，信息傳遞指數(shù)能描述與運動控制相關(guān)的腦電功能頻帶(Beta和Gamma波)與對應(yīng)肌電信號間的信息傳遞特性，可用于描述不同耦合模型間的信息傳遞關(guān)系。 最后，針對所研究方法進(jìn)行實驗研究。通過設(shè)計目標(biāo)力輸出任務(wù)實驗，同步采集了6名缺血性中風(fēng)患者和6名健康被試的腦肌電信號。應(yīng)用本文小波相干分析和信息傳遞特性分析方法，對中風(fēng)患者在運動過程中腦肌電間不同層次的同步特征差異表現(xiàn)進(jìn)行研究，并與健康人的腦肌電同步特征進(jìn)行對比分析，通過代理數(shù)據(jù)的方法驗證同步特征的顯著水平，同時統(tǒng)計分析任務(wù)表現(xiàn)、腦肌電頻譜能量等因素對腦肌電同步特征的影響，，為康復(fù)運動中的運動功能評價提供新方法。
[Abstract]:The analysis of electromyography (EMG) synchronization characteristics has become a new research hotspot in motor neuroscience. The feedback connection between the sensorimotor cortex and the muscles plays an important role in the neural control of human motion, which is reflected in the synchronization between electroencephalogramma (EGG) and electromyography (EMG) at different levels. The characteristic analysis of electromyography can reflect the functional relationship between brain and muscle, and understand the process of motor control and the pathological mechanism of motor disorder from the system level. It provides a theoretical basis for revealing the cooperative working mode of neural networks in the process of motion control, and also provides a new method for evaluating the functional status of neural rehabilitation movements. This paper first introduces the generation and characteristics of EEG and EMG signals, analyzes the current research status of nerve synchronous activity and analysis methods, and compares and analyzes various synchronous analysis methods and their respective characteristics of EEG coupling characteristics. The research contents of this paper are as follows: from the angle of consistent synchronization and generalized synchronization, based on frequency domain coherence and information transitivity, the characteristics of EEG synchronization in rehabilitation movement are studied. In view of the non-stationary characteristics of EEG and EMG signals, the linear correlation characteristics of EEG frequency domain components are studied by wavelet coherence analysis based on time-spectrum estimation. It is proved that wavelet coherence analysis can effectively describe the temporal variation characteristics of EEG coherence through the analysis of measured data. According to the characteristics of nonlinear coupling between EEG and electromyography and the correlation between EEG rhythm and EMG function during exercise, a two-channel correlation feature analysis method based on information entropy is introduced. An improved index of information transfer index based on wavelet decomposition is proposed to study the information flow characteristics between EEG signals from the perspective of nonlinearity and directionality. The simulation and the analysis of measured data show that the information transfer index can describe the information transfer characteristics between the EEG functional bands (Beta and Gamma waves) and the corresponding EMG signals, which are related to motion control. It can be used to describe the information transfer relationship between different coupling models. Finally, the experimental study is carried out on the methods studied. The EEG signals of 6 ischemic stroke patients and 6 healthy subjects were collected synchronously by designing the target force output task experiment. In this paper, wavelet coherence analysis and information transfer characteristic analysis were used to study the different characteristics of EEG synchronization in stroke patients during exercise, and compared with the healthy subjects. The significant level of synchronous features was verified by proxy data, and the effects of task performance, EEG spectrum energy and other factors on EEG synchronization characteristics were analyzed, which provided a new method for the evaluation of motor function in rehabilitation exercise.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN911.6

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