CN113974652B - Muscle control accuracy determining method based on cortical muscle function network model - Google Patents

Abstract

The invention discloses a muscle control accuracy determining method based on a cortical muscle function network model. The invention firstly obtains four surface electromyographic signals and electroencephalogram signals of a person to be measured. The starting position and the ending position of the motion are determined as the motion electromyographic signals through an energy threshold method. Then, the original surface electromyographic signals and the electroencephalogram signals are subjected to multi-scale decomposition by using a wavelet analysis method, the noise of the signals is removed, and then the ICA independent component analysis method is used for removing the artifacts of the electroencephalogram signals. And finally, calculating the transfer entropy between different muscles and the electrodes by using the transfer entropy, and constructing an adjacency matrix. And (3) after binarizing the adjacency matrix, constructing a directional cortical muscle function network, and analyzing the states of the cortex and the muscles during movement by using an analysis method of the network. The invention embodies the information flow relation between the brain electrical signal and the electromyographic signal, integrates the cerebral cortex and the muscle into a network for deep analysis, and performs deeper analysis on the relation between the cortex and the muscle on the whole.

Description

Muscle control accuracy determining method based on cortical muscle function network model

Technical Field

The invention relates to an analysis method for controlling accurate force of arm muscles, in particular to a muscle control accuracy determining method based on a cortical muscle function network model.

Background

The control relationship between the cortex and the muscles of the human body and how to complete the accurate control task are always a difficult problem for a large number of students. In particular, for some patients with dyskinesia, accurate control of muscles cannot be achieved due to trauma or the like, and no evaluation method is available in this respect, so that specific rehabilitation therapy cannot be performed for this case.

Many scholars abroad currently conduct research based on human physical control: the university of albert-ludwigs neurological clinic Kristeva-Feige, germany, et al, found that cortical muscle coupling on the beta band could be used to indicate the state of the cortical muscle network when attention was focused on exercise tasks. Norlaili Mat Safri, department of scientific and technical research of the university of panda, japan, and the like, found that the coupling between cortex and muscle can be used as a degree of cognitive effort required to reflect the contraction of muscle by equidistant contraction of the anterior tibial muscle of the upper limb, and that there is no more than a domestic analysis study in this respect.

There is no determination method for the accuracy of the control of the muscle force, but it can be found by the related study: by calculating the coupling property of the cortex and the muscle, the relationship between the cortex and the muscle can be quantitatively analyzed when the muscle is accurately contracted. However, since nerve impulses are bi-directionally conducted, i.e., nerve signals are transmitted from the cortex to the motor units on the muscle to generate motor unit potentials that are fed back to the cortex, conventional CMC does not reflect this process. Meanwhile, the coupling analysis of the cortex and the muscles is that the muscles correspond to the relation between different brain areas, but in the activities of accurate force control and the like, the muscles and the cortex should be regarded as a whole, and CMC cannot perform global and systematic analysis on the cortex and the muscles under the condition.

Disclosure of Invention

Aiming at the defects of the prior art, the invention provides a novel muscle control accuracy determining method based on a cortical muscle function network model.

According to the invention, firstly, a person to be tested holds the grip dynamometer with the grip dynamometer of 70% of the maximum grip, and in the process, surface myoelectric signals of the flexor of the wrist on the radius side, the extensor of the wrist on the ulnar side, the brachial radial muscle and the flexor of the wrist on the ulnar side are collected by using the myoelectric collector to serve as myoelectric signals of the person to be tested, 59 channels of electroencephalogram signals are synchronously collected by using the 59 channels of electroencephalogram collector, and the starting position and the ending position of the action are determined to serve as action myoelectric signals in a system through an energy threshold method. Then, the original electromyographic signals and the electroencephalogram signals are subjected to multi-scale decomposition by using a wavelet analysis method, the noise of the signals is removed, and then the ICA independent component analysis method is used for removing the artifacts of the noise-reduced electroencephalogram signals. And finally, calculating the transfer entropy between the electromyographic signals and the electroencephalogram signals by using the transfer entropy, and calculating an adjacency matrix. And (3) after binarizing the adjacency matrix, constructing a directional cortex muscle function network, calculating the concentration degree of the person to be tested by using the average degree, the average cluster, the global efficiency, the local efficiency and the average value of the brain myoelectric coherence, and determining the muscle control accuracy according to the concentration degree.

In order to achieve the above, the method of the present invention mainly comprises the steps of:

Step 1: the experimental paradigm applied to determining the accuracy of muscle control force is designed and comprises the following specific steps:

step 11: firstly, measuring the maximum grip strength value of a person to be tested through a grip strength meter;

Step 12: let him hold the grip at 70% of the current maximum grip and hold for 3 seconds, repeat this action 5 times, each at half a minute interval for rest. In the process, surface myoelectric signals of the flexor carpi radialis, extensor carpi ulnar, brachioradial muscle and flexor carpi ulnaris are collected by a myoelectricity collector and used as myoelectric signals of a person to be tested; and synchronously acquiring the brain electrical signals through a 59-channel brain electrical acquisition instrument. The starting position and the ending position of the motion are determined as the motion electromyographic signals through an energy threshold method.

Step 2: wavelet decomposition and noise reduction are carried out on the electroencephalogram and the electromyographic signals, and the method specifically comprises the following steps:

2-1: selecting a db4 wavelet function as a decomposition function;

2-2: 4-layer decomposition is carried out on the electroencephalogram and the electromyographic signals by using a db4 function to obtain wavelet coefficients;

2-3: the wavelet coefficient is processed by a soft threshold method, the wavelet coefficient which is larger than or equal to the threshold value keeps the original value, the wavelet signal which is smaller than the threshold value is set to be zero, and the soft threshold method has the following formula:

Wherein ω _λ is the wavelet coefficient to be processed, ω is the wavelet coefficient to be processed decomposed from each layer, sgn (ω) is a step function, the function value is 1 when ω >0, the function value is 0 when ω <0, and λ is the set threshold value of 0.35.

Step 3: the method comprises the following specific steps of decomposing an electroencephalogram signal by using fastICA algorithm to remove artifacts:

3-1: carrying out averaging and whitening treatment on the electroencephalogram signals;

3-2: searching an initial unmixing matrix w (0) according to a negative entropy criterion;

3-3: iteration is carried out according to the initial unmixed matrix by a Newton iteration method, and the iteration formula is as follows:

w(n+1)＝E{X(w(n)^TX)³}-3w(n)；

Wherein w (n+1) is a unmixed matrix of an n-th iteration round, X is an input brain electrical signal, and E { X (w (n) ^TX)³ } is an expectation of X (w (n) ^TX)³);

3-4: normalizing w (n+1), and repeating 3-3 if the convergence condition is not satisfied, wherein the convergence condition is ||w (n+1) w (n) ^T|-1|<10^-6, and the normalization method is as follows:

Wherein ||w (n+1) | is the prime number.

3-5: And reconstructing the electroencephalogram signal based on the unmixed matrix, so as to achieve the effect of removing the artifacts.

And 4, respectively calculating symbol transfer entropy of the 4 paths of electromyographic signals and the 59 paths of the electroencephalogram signals by utilizing the electromyographic signals and the electroencephalogram signals obtained in the step 3, obtaining a coupling matrix between the electroencephalogram and the electroencephalogram, between the electromyographic signals and between the electromyographic signals, and constructing a brain-muscle function network, wherein the method comprises the following specific steps of:

step 41: the symbol transfer entropy between 4 paths of electromyographic signals and 59 paths of electroencephalogram signals is calculated as the coupling between signals, and the transfer entropy formula is as follows:

in the above expression, n is an index of discrete time, τ is a predicted time, and p is a probability distribution. The value of TE may reflect the coupling strength between the time period data, taking τ=1 in the calculation;

Step 42: forming an adjacency matrix according to the calculated coupling;

Step 43: binarizing the adjacent matrix to obtain a binarized matrix, wherein the binarization means that a value larger than a threshold value in the collar matrix is set to be 1, a value smaller than 1 is set to be 0, and the threshold value is according to a significance analysis formula: selecting, wherein l is the data length, and alpha is significance;

step 44: and constructing a brain muscle function network according to the obtained binarization matrix.

And 5, obtaining the concentration degree of the person to be tested through the coupling between the brain muscle function network and the obtained signals, wherein the method comprises the following steps of:

Step 51: calculating the average degree, average clustering coefficient, global efficiency and local efficiency in the brain muscle function network, wherein the concept and definition of the 4 parameters are as follows:

Node degree: the node degree refers to the number of edges associated with the node, and is also called association degree. In particular, for a directed graph, the degree of ingress of a node refers to the number of edges that enter the node; the degree of departure of a node refers to the number of edges from the node.

Clustering coefficients: clustering coefficients are coefficients used to describe the degree of clustering between vertices in a graph. Specifically, the degree of interconnection between adjacent points of one point.

Wherein V _i is the local cluster coefficient of a node, and N _i is the edge number of the neighbor node.

Global efficiency: the efficiency of a pair of nodes in the network is the multiplicative inverse of the shortest path distance between the nodes, and the average global efficiency of the graph is the average efficiency of all node pairs. The global variable represents the "fault tolerance" of the network system, i.e. how efficient a node is to communicate. The formula is as follows:

local efficiency: the efficiency of a pair of nodes in the graph is the multiplicative inverse of the shortest path distance between the nodes. The local efficiency of a node in the graph is the average global efficiency of the subgraph induced by the neighbors of that node, and the average local efficiency is the local efficiency of each node on average.

Step 52: calculating the average value of the coupling between the electroencephalogram and the electromyographic signals;

step 53: the concentration of the person to be tested is calculated based on the following formula:

in the above formula, grade is concentration degree, te is average coupling property between brain electricity and electromyographic signals, deg is average degree in brain muscle function network, clu is average clustering coefficient, gob is global efficiency, and efc is local efficiency. When the concentration is higher than 0.8, the force control precision of the person to be tested is high, and when the concentration is between 0.5 and 0.8, the force control precision of the person to be tested is generally high, and when the concentration is lower than 0.5, the force control precision of the person to be tested is lower.

Compared with the existing upper limb accurate force control analysis method, the method has the following characteristics:

The method can capture large-scale dynamic characteristics of signals, reduce the influence of signal noise, and simultaneously, the skin and muscle contraction are connected through the analysis of the coupling of the skin muscles, so that a deeper mechanism of human body movement can be better revealed, and the information flow between the brain and the muscle can be better obtained through the directionality of the transfer entropy. The directional network analysis is combined, so that information interaction control between different functional areas of the cortex and between muscles and the functional areas in accurate force control is more integrally perceived, and the analysis is not limited to different nodes or the analysis on the magnitude of the coupling of the cortex muscles on different muscles, so that the concentration of the force control of the human body is scientifically estimated.

Drawings

FIG. 1 is a flow chart of an experiment of the present invention;

FIG. 2 is a graph of the force trace fluctuation of the extensor ulnar surface electromyographic signals and partial electroencephalogram signal wavelet noise reduction under different concentration conditions;

FIG. 3 is a graph of the transfer entropy coupling of different electromyographic signals and electroencephalographic signals under different experimental conditions;

FIG. 4 is a graph comparing various parameters of a cortical muscle directional network;

Fig. 5 is a directed network visualization of cortical muscles.

Detailed Description

Embodiments of the present invention will be described in detail below with reference to the attached drawings: the embodiment is implemented on the premise of the technical scheme of the invention, and detailed implementation modes and specific operation processes are given.

As shown in fig. 1, the present embodiment includes the steps of:

Step 1, collecting surface myoelectric signals of a flexor carpi radialis, a extensor carpi ulnar, a brachial radial muscle and a flexor carpi ulnar by a myoelectric collector; and synchronously acquiring the brain electrical signals through a 59-channel brain electrical acquisition instrument. Determining a starting position and a termination position of the action as action electromyographic signals through an energy threshold method; the test operator was 10 healthy men and the maximum grip strength (MVC) of each person tested was measured without vigorous exercise one week before the test. The experiment is carried out by using a Trigno wireless myoelectricity acquisition instrument of America DELSYS to acquire surface myoelectricity signals of the flexor of the radius wrist, extensor ulnar wrist, brachial radial muscle and ulnar wrist when making fists under different control precision conditions. The sampling frequency is 1000Hz, the action starting moment is determined by an energy threshold method, 2000 subsequent sampling points of each electromyographic signal are taken, the EEG signal is recorded by using a G.Mobllab MP-2015 wireless EEG system, 59 scalp position EEG signals are recorded by using a sampling frequency of 59Hz, and the electrodes are placed in the system according to international standards 10-20. Care should be taken at the beginning and end of the recording to ensure that at least 95% of the derivative of the scalp electrode has an impedance below 20kΩ. The electrode positions are based on an international standard electrode pattern.

And (2) performing force control and concentration control tasks. During the experimental task, the subject sits in an electrically shielded, dimly lit room, and is required to maintain a constant equidistant force by holding the grip with its dominant hand completely un-relaxed. Visual feedback of force levels is provided through an analog display screen in front of the subject. The arm of the dominant hand is laid flat on the table. Prior to the experiment, the applied force for each subject was determined to be 7% and 70% of MVC. The average applied force for all subjects was 2.1kg and 20kg. A total of 6 different control accuracies were tested as defined below:

High precision condition (HP): this condition is defined as maintaining the force at 7% or 70% of the MVC force level, referred to as experiment 1 and experiment 4. This requires fine motor control to ensure that the grip is within 1% of the target force.

High precision level with arithmetic task condition (HPAT): this condition is identical to the HP condition, but instructs the subject to perform the mental arithmetic task (subtracting 7 from 400) at the same time, ensuring that the grip is within 3% error of the target force, referred to as experiment 2 and experiment 5.

Low Precision (LP) condition: the low accuracy condition is defined as maintaining the applied force within the target window, which is not a requirement for errors in the real-time grip, referred to as experiment 3 and experiment 6.

Each condition included 6 trials (including 3 runs of 7% mvc and 3 times 70% mvc) each lasting 3 minutes. The time between trials was about 5 minutes. To avoid sequence effects, experiments belonging to these three experimental conditions were pseudo-random. The experimenter gives an illustration of the test sequences belonging to different conditions. Before the experiment, several practical experiments are carried out on each object until the force and precision modes required by each condition are reached, and the force trace wave diagram of the extensor ulnar surface electromyographic signals and partial electroencephalogram signals under different concentration conditions is shown in figure 2.

And 2, carrying out wavelet decomposition on the obtained brain electricity and electromyographic signals and eliminating noise in the brain electricity and electromyographic signals.

And 3, performing ICA decomposition on the brain electricity to remove artifacts.

And 4, respectively obtaining symbol transfer entropy of 4 paths of electromyographic signals and 59 paths of electroencephalogram signals by utilizing the electromyographic signals and the electroencephalogram signals obtained in the step 3, obtaining a coupling matrix between the electroencephalogram signals and the electromyographic signals, and the electromyographic signals, and constructing a brain-muscle function network as shown in fig. 5, wherein the larger the node is, the better the connectivity of the node is, and the no circle exists if the node is not connected, and the specific construction comprises the following specific steps:

step 41: the symbol transfer entropy between 4 paths of electromyographic signals and 59 paths of electroencephalogram signals is calculated as the coupling between signals, and the transfer entropy formula is as follows:

in the above expression, n is an index of discrete time, τ is a predicted time, and p is a probability distribution. The value of TE may reflect the coupling strength between the time period data. In the calculation, take τ=1; see in particular fig. 3.

Step 42: forming an adjacency matrix according to the calculated coupling;

Step 43: binarizing the adjacent matrix to obtain a binarized matrix, wherein the binarization means that a value larger than a threshold value in the collar matrix is set to be 1, a value smaller than 1 is set to be 0, and the threshold value is according to a significance analysis formula: selecting, wherein l is the data length, and alpha is significance;

step 44: and constructing a brain muscle function network according to the obtained binarization matrix.

And 5, obtaining the concentration degree of the person to be tested through the coupling between the brain muscle function network and the obtained signals, wherein the method comprises the following steps of:

Step 51: the average degree, average clustering coefficient, global efficiency and local efficiency in the brain muscle function network are calculated, and the concept and definition of the 4 parameters are as shown in fig. 4 and 5.

Step 52: calculating the average value of the coupling between the electroencephalogram and the electromyographic signals;

step 53: calculating the concentration of the person to be tested based on the following formula:

In the above formula, grade is a concentration value, te is average coupling property between brain electricity and electromyographic signals, deg is average degree in a brain muscle function network, clu is average clustering coefficient, gob is global efficiency, and efc is local efficiency. When the value is higher than 0.8, the force control precision of the person to be tested is high, and when the value is between 0.5 and 0.8, the force control precision of the person to be tested is generally high, and when the value is lower than 0.5, the force control precision of the person to be tested is lower.

Claims (3)

1. The muscle control accuracy determining method based on the cortical muscle function network model is characterized by comprising the following steps of: the method comprises the following steps:

step 1: designing an experimental paradigm applied to determine the accuracy of muscle control force;

step 11: measuring the maximum grip strength value of a person to be tested through a grip strength meter;

Step 12: allowing the person to be tested to hold the grip at 70% of the current maximum grip for 3 seconds, repeating the action 5 times, each action being spaced half a minute apart for rest;

In the process, surface myoelectric signals of the flexor carpi radialis, extensor carpi ulnar, brachioradial muscle and flexor carpi ulnaris are acquired by a myoelectric acquisition instrument; synchronously acquiring brain electrical signals through a 59-channel brain electrical acquisition instrument; determining a starting position and a termination position of the action as action electromyographic signals through an energy threshold method;

Step 2: wavelet decomposition and noise reduction are carried out on the electroencephalogram and the electromyographic signals;

step3: using fastICA algorithm to decompose the brain electrical signal and remove the artifact;

Step 4: calculating the coupling between signals through the myoelectric signals subjected to noise reduction and the brain electric signals subjected to artifact removal, and constructing a brain muscle function network;

Step 41: calculating symbol transfer entropy between 4 paths of electromyographic signals and 59 paths of electroencephalogram signals as the coupling between signals;

Step 42: forming an adjacency matrix according to the calculated coupling;

step 43: performing binarization processing on the adjacent matrix to obtain a binarization matrix;

step 44: constructing a brain muscle function network according to the obtained binarization matrix;

And 5, obtaining the concentration degree of the person to be tested through the coupling between the brain muscle function network and the obtained signals, wherein the method comprises the following steps of:

step 51: calculating the average degree, average clustering coefficient, global efficiency and local efficiency in the brain muscle function network;

Step 52: calculating the average value of the coupling between the electroencephalogram and the electromyographic signals;

step 53: calculating concentration of a person to be tested, and determining muscle control accuracy according to the concentration;

The concentration is calculated as follows:

In the above formula, grade is a concentration value, te is average coupling property between brain electricity and electromyographic signals, deg is average degree in a brain muscle function network, clu is average clustering coefficient, gob is global efficiency, and efc is local efficiency.

2. The method for determining the accuracy of muscle control based on a network model of cortical muscle function according to claim 1, wherein: the step 2 specifically includes:

2-1: selecting a db4 wavelet function as a decomposition function;

2-2: 4-layer decomposition is carried out on the electroencephalogram and the electromyographic signals by using a db4 function to obtain wavelet coefficients;

2-3: processing wavelet coefficients by using a soft threshold method, wherein the wavelet coefficients larger than or equal to a threshold value keep the original values, and wavelet signals smaller than the threshold value are set to be zero;

2-4: and carrying out signal reconstruction by using the obtained wavelet coefficient, thereby obtaining the brain electricity and electromyographic signals after noise reduction.

3. The method for determining the accuracy of muscle control based on a network model of cortical muscle function according to claim 1, wherein: the step 3 specifically includes:

3-1: carrying out averaging and whitening treatment on the electroencephalogram signals;

3-2: searching an initial unmixing matrix w (0) according to a negative entropy criterion;

3-3: iteration is carried out according to the initial unmixed matrix by a Newton iteration method, and the iteration formula is as follows:

w(n+1)＝E{X(w(n)^TX)³}-3w(n)；

Wherein w (n+1) is a unmixed matrix of an n-th iteration round, X is an input brain electrical signal, and E { X (w (n) ^TX)³ } is an expectation of X (w (n) ^TX)³);

3-4: normalizing w (n+1), and repeating 3-3 if the convergence condition is not satisfied, wherein the convergence condition is ||w (n+1) w (n) ^T|-1|<10^-6, and the normalization is as follows:

the ||w (n+1) | is a prime number;

3-5: and reconstructing the electroencephalogram signal based on the unmixed matrix, so as to achieve the effect of removing the artifacts.