Corticalmuscle Coupling And Fatigue Identification Of Upper Extremity Movement

The effector is part of the human body that can be moved.For exercises,we can control the effectors that are close to the body,such as the waist,neck and head,and remote end effectors that are far from the body,such as arms,hands and legs.For some stroke patients or the patients with motor function disorder,it is very difficult to control the effector normally.The human-machine interface based on Electromyography(EEG)/ Electromyography(EMG)can complete action to perform rehabilitation training according to the patient's intention.However,due to the complex features of non-linearity and individual variation of EEG/EMG signals,which sometimes is not effective for the patient and easy to produce fatigue.Therefore,it is necessary to explore the underlying mechanism of brain control using corticomuscular coupling analysis to provide theoretical basis for the patients' treatment.On the basis of this,the patient is dealt with the fatigue problems during the rehabilitation training.In summary,the study of corticomuscular coupling information transmission and fatigue detection during rehabilitation has important theoretical significance and application value.This article focuses on the corticomuscular coupling relationship which explores the functional interaction and information transfer characteristics between cerebral cortex and the related muscles during the hand movements from the physiological mechanism,and studies the fatigue identification method in time-frequency-space joint domain,which can provide help for rehabilitation and functional evaluation of the motor function disorder or stroke patients.The main work is as follows:First of all,in order to study the interaction between muscle tissue and cerebral cortex,and explore the multi-scale coupling characteristics of EEG/EMG signal and the functional relationship between the cortex and the intermuscular space-time,we combine the multiple empirical mode decomposition(MEMD)algorithm with transfer entropy(TE)to construct the MEMD-TE model,which is applied for the corticomuscular coupling analysis.The EEG and EMG signals collected synchronously are firstly preprocessed,and then the multivariate empirical mode decomposition algorithm is employed for the time-frequency scaling of the signals,and finally transfer entropy is calculated on different scales,to analyze the nonlinear coupling characteristics in different coupling directions at each scale.Secondly,in order to effectively analyze the time-frequency-space multidimensional features of surface EMG(s EMG)signals,a method of s EMG feature extraction based on tensor linear Laplace discriminant is proposed.The Morlet wavelet transform is firstly performed on s EMG signals to construct the fourth-order tensor data with time,space,frequency and task information,and then the tensor linear Laplace discriminant analysis method is adopted to obtain the projection matrix and the training set and the test set are projected into the projection matrix respectively to obtain distinguishing features,and finally a classifier is used to identify the grasping actions during normal and fatigue conditions.