中风康复运动中脑肌电同步分析

发布时间：2018-06-21 19:52

本文选题：中风康复运动 + 功能评价　；参考：《燕山大学》2014年硕士论文

【摘要】：脑肌电同步特征分析已经成为运动神经科学领域一个新的研究热点。在人体运动的神经控制过程中，大脑感觉运动皮层与肌肉之间的控制反馈联系发挥着至关重要的作用，这种联系体现在脑电信号(Electroencephalogram，EEG)与肌电信号(Electromyography，EMG)间不同层次的同步现象。脑肌电同步特征分析可以反映大脑与肌肉之间的功能联系，从系统的层面理解运动控制过程及其运动障碍的病理机制，为揭示运动控制过程中神经网络的协同工作方式提供了理论基础，也为神经康复运动的功能状态评价提供新方法。本文首先介绍了脑电和肌电信号的产生及特点，分析了神经同步活动及分析方法的研究现状，对比分析了脑肌电耦合特征的多种同步分析方法及各自特点，确定了本文的研究内容：分别从一致性同步和广义同步角度，基于频域相干性及信息传递性分析研究康复运动中的脑肌电同步特征。针对脑电和肌电信号的非平稳性特点，运用基于时频谱估计的小波相干性分析方法研究脑肌电频域成分的线性相关特性，通过实测数据分析验证了小波相干分析能有效描述脑肌电相干性的时间变化特征。针对脑肌电间的非线性耦合特性，且运动中特定脑电节律与肌电信号功能相关的特点，引入基于信息熵的两通道关联特征分析方法，提出基于小波分解的改进信息传递指数指标，从非线性和方向性角度研究脑肌电信号间的信息流动特性。通过仿真及实测数据分析表明，信息传递指数能描述与运动控制相关的脑电功能频带(Beta和Gamma波)与对应肌电信号间的信息传递特性，可用于描述不同耦合模型间的信息传递关系。最后，针对所研究方法进行实验研究。通过设计目标力输出任务实验，同步采集了6名缺血性中风患者和6名健康被试的脑肌电信号。应用本文小波相干分析和信息传递特性分析方法，对中风患者在运动过程中脑肌电间不同层次的同步特征差异表现进行研究，并与健康人的脑肌电同步特征进行对比分析，通过代理数据的方法验证同步特征的显著水平，同时统计分析任务表现、脑肌电频谱能量等因素对脑肌电同步特征的影响，，为康复运动中的运动功能评价提供新方法。
[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.
【学位授予单位】：燕山大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN911.6

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