CN113974652A - Muscle control accuracy determination method based on cortical muscle function network model - Google Patents
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Abstract
The invention discloses a muscle control accuracy determination method based on a cortical muscle function network model. The method firstly obtains four surface electromyographic signals and electroencephalographic signals of a person to be tested. And determining the starting position and the ending position of the action as action electromyographic signals by an energy threshold method. Then, carrying out multi-scale decomposition on the original surface electromyogram signal and the electroencephalogram by using a wavelet analysis method, removing the noise of the signal, and removing the artifact of the electroencephalogram signal by using an ICA independent component analysis method. And finally, calculating the transfer entropy between different muscles and electrodes by using the transfer entropy, and constructing an adjacency matrix. And constructing an oriented cortex muscle function network after binarizing the adjacency matrix, and analyzing the states of cortex and muscle in motion by using an analysis method of the network. The invention embodies the information flow relation between the electroencephalogram signals and the electromyogram signals, integrates the cerebral cortex and the muscle into a network for deep analysis, and carries out deeper analysis on the relation between the cortex and the muscle on the whole.
Description
Technical Field
The invention relates to an analysis method for accurate force control of arm muscles, in particular to a muscle control accuracy determination method based on a cortical muscle function network model.
Background
The control relationship between the cortex and the muscle of the human body and how to complete the precise control task are always difficult problems for broad scholars. Especially for some patients with motor dysfunction, accurate muscle control cannot be achieved due to trauma and the like, and there is no assessment method for this aspect, so that specific rehabilitation treatment cannot be performed for this situation.
At present, many scholars abroad carry out research based on human body force control: the neurological clinic Kristeva-Feige, Albert, LoderVirges, Germany, et al found that cortical muscle coupling on the beta band could be used to indicate the status of the cortical muscle network when attention was focused on the motor task. Norlaili Mat Safri et al, the scientific and technical research department of Japan bear university, finds that cortex-to-muscle coupling can be used as a cognitive effort degree required for reflecting muscle contraction through equidistant contraction of tibialis anterior muscles of upper limbs, and domestic analytical research on this aspect is not common.
At present, no determination method for the control accuracy of muscle strength is available, but through related researches, it can be found that: the relation between the cortex and the muscle can be quantitatively analyzed when the muscle is accurately contracted by calculating the coupling of the cortex and the muscle. However, since the nerve impulses are conducted in two directions, i.e. the nerve signals are transmitted from the cortex to the motor units on the muscles to generate action unit potentials, and then fed back to the cortex, the ordinary CMC cannot reflect the process. Meanwhile, the cortex-muscle coupling analysis is performed on the relationship between different brain areas corresponding to muscles, but in activities such as accurate force control, the muscles and the cortex should be considered as a whole, and the 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 determination method based on a cortical muscle function network model.
Firstly, a person to be tested holds a grip dynamometer with 70% of the maximum grip strength, a myoelectric acquisition instrument is used for acquiring surface myoelectric signals of flexor carpi radialis, extensor carpi ulnaris, brachioradialis and flexor carpi ulnaris as myoelectric signals of the person to be tested in the process, a 59-channel electroencephalogram acquisition instrument is used for synchronously acquiring 59-channel electroencephalogram signals, and the starting position and the ending position of actions are determined in a system as action myoelectric signals through an energy threshold method. Then, carrying out multi-scale decomposition on the original electromyographic signals and the electroencephalogram by using a wavelet analysis method, removing the noise of the signals, and removing the artifacts of the electroencephalogram signals subjected to noise reduction by using an ICA independent component analysis method. And finally, calculating the transfer entropy between the electromyographic signals and the electroencephalographic signals by using the transfer entropy, and calculating an adjacency matrix. And constructing an oriented cortex muscle function network after binarization of the adjacency matrix, calculating the concentration of the person to be tested by using the average degree, the average clustering, the global efficiency, the local efficiency and the average value of the coherence of the brain and muscle electricity, and determining the muscle control accuracy according to the concentration.
In order to realize the above content, the method of the invention mainly comprises the following steps:
step 1: the design is applied to the experimental paradigm of confirming muscle control dynamics precision, includes following specific step:
step 11: firstly, measuring the maximum holding force value of a person to be tested by a holding force meter;
step 12: let him hold the grip at 70% of the current maximum grip and hold it for 3 seconds, repeating this action 5 times, each half a minute apart for rest. In the process, surface electromyographic signals of a flexor carpi radialis, an extensor carpi ulnaris, a brachioradialis and a flexor carpi ulnaris are collected by a myoelectricity collector to be used as electromyographic signals of a person to be tested; the EEG signals are synchronously collected by a 59-channel EEG collector. And determining the starting position and the ending position of the action as action electromyographic signals by an energy threshold method.
Step 2: the method for performing wavelet decomposition noise reduction on the electroencephalogram and electromyogram signals 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 electromyogram signals by using a db4 function to obtain wavelet coefficients;
2-3: the wavelet coefficient is processed by using a soft threshold method, the wavelet coefficient more than or equal to the threshold value keeps the original value, the wavelet signal less than the threshold value is set to be zero, and the soft threshold method has the following formula:
wherein, ω isλFor the wavelet coefficient, omega is the wavelet coefficient to be processed by decomposing each layer, sgn (omega) is step function, omega>0 time function value is 1, omega<The function value is 0 when 0, and the lambda is the set threshold value of 0.35.
And step 3: decomposing the electroencephalogram signal by using a fastICA algorithm to remove artifacts, and specifically comprising the following steps:
3-1: carrying out equalization and whitening processing 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 unmixing matrix by a Newton iteration method, and an iteration formula is as follows:
w(n+1)＝E{X(w(n)TX)3}-3w(n)；
wherein w (n +1) is the unmixing matrix of the nth iteration round, X is the input EEG signal, E { X (w (n))TX)3Is X (w (n)TX)3(iii) a desire;
3-4: w (n +1) is normalized, and if the convergence condition is not satisfied, 3-3 is repeated, wherein the convergence condition is | | w (n +1) w (n)T|-1|<10-6The normalization method is as follows:
where | | | w (n +1) | | is a two-class norm.
3-5: reconstructing the electroencephalogram signal based on the unmixing matrix, thereby achieving the effect of removing artifacts.
And 4, respectively solving sign transfer entropy of the 4 paths of electromyographic signals and the 59 paths of electroencephalographic signals by using the electromyographic and electroencephalographic signals obtained in the step 3, obtaining electroencephalographic and electroencephalographic, electroencephalographic and electromyographic, and coupling matrixes between electromyographic and electromyographic, and constructing a brain muscle function network, wherein the method comprises the following specific steps:
step 41: and (3) calculating the symbol transfer entropy between the 4 paths of electromyographic signals and the 59 paths of electroencephalographic signals as the coupling between the signals, wherein the transfer entropy formula is as follows:
in the above formula, n is an index of discrete time, τ is predicted time, and p is probability distribution. The value of TE may reflect the coupling strength between the data in this time period, and in the calculation, τ is taken to be 1;
step 42: forming an adjacency matrix according to the calculated coupling;
step 43: carrying out binarization processing on the adjacent matrix to obtain a binarization matrix, wherein binarization means setting a value larger than a threshold value in the concatenation matrix as 1 and setting a value smaller than 1 as 0, and the threshold value is analyzed according to significanceThe formula:
selecting, wherein l is data length and alpha is significance;
step 44: and constructing a brain muscle function network according to the obtained binary matrix.
And 5, obtaining the concentration degree of the person to be tested through the coupling of the brain muscle function network and the obtained signal, wherein the method comprises the following steps:
step 51: calculating the average degree, the average clustering coefficient, the global efficiency and the 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 entry of a node refers to the number of edges that enter the node; the out-degree of a node refers to the number of edges from the node.
Clustering coefficient: the clustering coefficient is a coefficient used to describe the degree of clustering between vertices in a graph. In particular the degree of interconnection between adjacent points of a point.
Wherein ViIs a local clustering coefficient, N, of a certain nodeiThe number of edges of its neighbor nodes.
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 the communication efficiency of a node is measured. 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 average local efficiency of each node.
Step 52: calculating the average value of the coupling between the electroencephalogram signal and the electromyogram signal;
step 53: the concentration degree of the person to be tested is calculated based on the following formula:
in the formula, Grade is concentration, Te is average coupling between electroencephalogram and electromyogram 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 concentration degree is higher than 0.8, the control precision of the human to be tested is very high, the control precision of the human to be tested is general when the concentration degree is between 0.5 and 0.8, and the control precision of the human to be tested is lower when the concentration degree is lower than 0.5.
Compared with the existing upper limb accurate force control analysis method, the invention has the following characteristics:
the method can capture large-scale dynamic characteristics of signals and reduce the influence of signal noise, and meanwhile, the cortex is connected with muscle contraction through the coupling analysis of the cortex muscles, so that a deeper mechanism of human motion can be better disclosed, and the tropism of the transfer entropy better obtains information flow between the brain and the muscles. The method is combined with the directed network analysis, so that more overall cognition can be achieved for information interaction control between different functional areas of the cortex and between muscles and the functional areas during accurate force control, and scientific evaluation can be performed on the force control concentration degree of the human body instead of being limited to analysis on the coupling degree of the cortex muscles on different nodes or different muscles.
Drawings
FIG. 1 is a flow chart of an experiment according to the present invention;
FIG. 2 is a wavelet de-noising of the surface electromyographic signals of extensor carpi ulnaris and partial electroencephalogram signals and a power channel fluctuation map under different concentration conditions;
FIG. 3 is a transfer entropy coupling diagram of different electromyographic signals and electroencephalographic signals under different experimental conditions;
FIG. 4 is a comparison graph of various parameters of a cortical muscle directional network;
fig. 5 is a visualization diagram of a cortical muscle directional network.
Detailed Description
The embodiments of the present invention will be described in detail below with reference to the accompanying drawings: the embodiment is implemented on the premise of the technical scheme of the invention, and a detailed implementation mode and a specific operation process are given.
As shown in fig. 1, the present embodiment includes the following steps:
step 1, collecting surface electromyographic signals of a flexor carpi radialis, an extensor carpi ulnaris, a brachioradialis and a flexor carpi ulnaris by a myoelectricity collector; the EEG signals are synchronously collected by a 59-channel EEG collector. Determining the initial position and the final position of the action as action electromyographic signals by an energy threshold method; the test operator was 10 healthy men, and the maximum grip strength (MVC) of each test person was measured without vigorous exercise for one week before the test. In the experiment, surface electromyographic signals of a flexo flexor carpi radialis, an extensor carpi ulnaris, a brachioradialis and a flexor carpi ulnaris are acquired under different control precision conditions by a Trigno wireless electromyography acquisition instrument of DELSYS USA. The sampling frequency is 1000Hz, the action starting time is determined by an energy threshold method, the subsequent 2000 sampling points of each path of electromyogram signals are taken, the EEG records the EEG signals by using a G.Mobllab MP-2015 wireless EEG system, 59 EEG signals of scalp positions are recorded at the sampling frequency of 59Hz, and the electrodes are placed in the system according to the international standard 10-20. Careful preparation is made at the beginning and end of the recording to ensure that at least 95% of the scalp electrode derivatives have an impedance below 20k omega. The electrode position is based on the international standard electrode map.
And (2) performing a power control task and a concentration control task. During the experimental task, the subject was seated in an electrically shielded, dimly lit room and was asked to maintain a constant isometric force by holding the grip with his dominant hand completely unrelaxed. Visual feedback of the force level is provided through a simulated 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 20 kg. 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 and is referred to as experiment 1 and experiment 4. This situation requires fine motor control to ensure that the grip is within 1% of the target force.
High level of precision with arithmetic task conditions (HPAT): this condition is identical to the HP condition, but the subjects were instructed to perform psycho-arithmetic tasks simultaneously (subtracting 7 from 400), ensuring that the grip strength was within 3% of the target strength error, referred to as experiment 2 and experiment 5.
Low Precision (LP) condition: the low precision condition is defined as maintaining the applied force within the target window, which is not required for the error of the real-time grip, and is referred to as experiment 3 and experiment 6.
Each condition included 6 trials (3 times with 7% MVC and 3 times with 70% MVC) each lasting 3 minutes. The time between the two tests was about 5 minutes. To avoid sequence effects, the experiments belonging to these three experimental conditions were pseudo-random. The experimenter gives an explanation of the test sequence pertaining to the different conditions. Before the experiment, each subject was actually tested several times until the force and precision pattern required for each situation was reached, and the electromyography of the extensor muscles of the ulnar wrist surface and a part of the electroencephalogram signal and the fluctuation map of the force channel under different concentration conditions were as shown in fig. 2.
And 2, performing wavelet decomposition on the obtained electroencephalogram and electromyogram signals and eliminating noise in the electroencephalogram and electromyogram signals.
And 3, carrying out ICA decomposition on the electroencephalogram to remove artifacts.
Step 4, respectively solving symbol transfer entropy of the 4 paths of electromyographic signals and the 59 paths of electroencephalographic signals by using the electromyographic signals and the electroencephalographic signals obtained in the step 3, obtaining a coupling matrix among electroencephalographic signals, electromyographic signals and 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 connectivity exists, and the specific construction comprises the following specific steps:
step 41: and (3) calculating the symbol transfer entropy between the 4 paths of electromyographic signals and the 59 paths of electroencephalographic signals as the coupling between the signals, wherein the transfer entropy formula is as follows:
in the above formula, n is an index of discrete time, τ is predicted time, and p is probability distribution. The value of TE may reflect the strength of the coupling between the time period data. In the calculation, τ is taken to be 1; see figure 3 in particular.
Step 42: forming an adjacency matrix according to the calculated coupling;
step 43: carrying out binarization processing on the adjacent matrix to obtain a binarization matrix, wherein binarization means that a value larger than a threshold value in the adjacent matrix is set as 1, a value smaller than 1 is set as 0, and the threshold value is determined according to a significance analysis formula:
selecting, wherein l is data length and alpha is significance;
step 44: and constructing a brain muscle function network according to the obtained binary matrix.
And 5, obtaining the concentration degree of the person to be tested through the coupling of the brain muscle function network and the obtained signal, wherein the method comprises the following steps:
step 51: and (3) calculating the average degree, the average clustering coefficient, the global efficiency and the local efficiency in the brain muscle function network, as shown in figures 4 and 5, wherein the concept and the definition of the 4 parameters are shown above.
Step 52: calculating the average value of the coupling between the electroencephalogram signal and the electromyogram signal;
step 53: the concentration degree of the person to be tested is calculated based on the following formula:
in the formula, Grade is concentration value, Te is average coupling between electroencephalogram and electromyogram 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 power control precision of the person to be tested is very high, the power control precision of the person to be tested is general when the value is between 0.5 and 0.8, and the power control precision of the person to be tested is lower when the value is lower than 0.5.
Claims (3)
1. A muscle control accuracy determination method based on a cortical muscle function network model is characterized by comprising the following steps: the method comprises the following steps:
step 1: designing an experimental paradigm applied to determining the accuracy of muscle control force;
step 11: measuring the maximum holding force value of a person to be tested by a holding force meter;
step 12: the person to be tested holds the spring-grip at 70% of the current maximum grip strength for 3 seconds, and the action is repeated for 5 times, wherein the interval of each action is half a minute for rest;
in the process, surface electromyographic signals of a flexor carpi radialis, an extensor carpi ulnaris, a brachioradialis and a flexor carpi ulnaris are collected through an electromyography collector; synchronously acquiring electroencephalogram signals through a 59-channel electroencephalogram acquisition instrument; determining the initial position and the final position of the action as action electromyographic signals by an energy threshold method;
step 2: performing wavelet decomposition noise reduction on the electroencephalogram and electromyogram signals;
and step 3: decomposing the electroencephalogram signal by using a fastICA algorithm to remove artifacts;
and 4, step 4: calculating the coupling between signals through the denoised electromyographic signals and the de-artifact electroencephalographic signals, and constructing a brain muscle function network;
step 41: calculating the symbol transfer entropy between the 4 paths of electromyographic signals and the 59 paths of electroencephalographic signals as the coupling between the signals;
step 42: forming an adjacency matrix according to the calculated coupling;
step 43: carrying out binarization processing on the adjacent matrix to obtain a binarization matrix;
step 44: constructing a brain muscle function network according to the obtained binary matrix;
and 5, obtaining the concentration degree of the person to be tested through the coupling of the brain muscle function network and the obtained signal, wherein the method comprises the following steps:
step 51: calculating the average degree, the average clustering coefficient, the global efficiency and the local efficiency in the brain muscle function network;
step 52: calculating the average value of the coupling between the electroencephalogram signal and the electromyogram signal;
step 53: calculating the concentration degree of the person to be tested, and determining the muscle control accuracy according to the concentration degree.
2. The method for determining the accuracy of muscle control based on the network model of cortical muscle function as claimed in claim 1, wherein: the step 2 specifically comprises:
2-1: selecting a db4 wavelet function as a decomposition function;
2-2: 4-layer decomposition is carried out on the electroencephalogram and electromyogram signals by using a db4 function to obtain wavelet coefficients;
2-3: processing the wavelet coefficient by using a soft threshold method, wherein the wavelet coefficient more than or equal to the threshold value keeps the original value, and the wavelet signal less than the threshold value is set to be zero;
2-4: and performing signal reconstruction by using the obtained wavelet coefficient so as to obtain the electroencephalogram and electromyogram signals subjected to noise reduction.
3. The method for determining the accuracy of muscle control based on the network model of cortical muscle function as claimed in claim 1, wherein: the step 3 specifically includes:
3-1: carrying out equalization and whitening processing 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 unmixing matrix by a Newton iteration method, and an iteration formula is as follows:
w(n+1)＝E{X(w(n)TX)3}-3w(n)；
wherein w (n +1) is the unmixing matrix of the nth iteration round, X is the input EEG signal, E { X (w (n))TX)3Is X (w (n)TX)3(iii) a desire;
3-4: w (n +1) is normalized, and if the convergence condition is not satisfied, 3-3 is repeated, wherein the convergence condition is | | w (n +1) w (n)T|-1|<10-6Normalized as follows:
| | w (n +1) | is a second-class norm;
3-5: reconstructing the electroencephalogram signal based on the unmixing matrix, thereby achieving the effect of removing artifacts.
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