CN113558639A - Motor intention brain muscle network analysis method based on glange causal relation and graph theory - Google Patents
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Abstract
The invention discloses a motor intention brain muscle network construction method based on a Glangel causal relationship and a graph theory. The method comprises the steps of firstly synchronously acquiring electroencephalogram and myoelectric signals under two different motion modes, preprocessing and dividing frequency bands of the signals, selecting partial electrodes related to a motion area for analysis, calculating power spectral densities of the electroencephalogram signals under the two different motion modes and different frequency bands, then constructing an autoregressive model to calculate the Glan causal relationship between the electroencephalogram signals or the myoelectric signals, analyzing the coupling strength between the electroencephalogram signals and the myoelectric signals under the two different motion modes, then constructing a brain muscle network according to the Glan causal relationship between the obtained signals, utilizing a graph theory method to calculate network characteristic parameters, and evaluating the rehabilitation effects of active training and passive training. The invention expands the evaluation method, is helpful for understanding the internal mechanism, constructs the brain muscle network, and can research the information flow between the brain and the muscle.
Description
Technical Field
The invention belongs to the technical field of pattern recognition and network analysis, relates to a comprehensive evaluation method for improving the rehabilitation effect of a stroke patient in active movement, and particularly relates to a brain muscle network analysis method for the stroke patient under different movement intentions based on the Glangen causal relationship and the graph theory.
Background
Stroke has now become one of the leading causes of death and disability in the world. In addition, about 75% of survivors have different degrees of dysfunction, and rehabilitation training for stroke patients plays an important role in recovering motor functions. Rehabilitation therapy in the early stages of stroke is important for the recovery of brain function and the degree of neural remodeling, and it can enhance cortical reorganization and promote relearning of the injured brain. Rehabilitation training includes active training and passive training, and the distinction between active and passive movements has been recognized since ancient times, but the nature of this distinction is still unclear. There is evidence that passive exercise is not sufficient to alter motor recovery, rehabilitation training with motor intent can improve rehabilitation, and patients must actively participate in and attempt exercise.
The motor intention refers to conscious cognition of preparation and movement of the upper limb of a patient, and with the development of brain-computer interface technology in recent years, brain muscle interface technology is used for recognizing the motor intention of the patient so as to promote the patient to participate in rehabilitation training and improve the rehabilitation effect. Passive motion is immediate but has little effect on promoting motor function and increasing motor power, which may result in decreased patient control. The active movement indicates that a certain motor function forms a loop in the central nervous system, so that the motor function can be better promoted, and the motor ability is improved.
At present, the evaluation on the effect of active training and exercise training is mainly to evaluate the active and passive rehabilitation training of patients through behavior and action data, and the effect of active training obtained through various behavior test scores is superior to that of passive training. When there is actual movement, the brain areas associated with the movement are connected and there is functional integration between the brain and the muscles. The coherent activity between the motor cortex and the muscles is believed to reflect the synchronous firing of the cortical spinal cord cells and can be estimated by analyzing the frequency domain coherence between the electroencephalogram and electromyogram signals, cortical muscle coupling provides a method of functional connectivity, revealing the mechanisms of cortical neuron functional connectivity and motor unit synchronicity. Although relevant research shows that the cortical muscle coupling strength of active movement is obviously higher than that of passive movement, the strength of the active movement and the passive movement is not quantitatively analyzed, the strength also influences the frequency range of the cortical muscle coupling, and few researches are carried out on the influence of the movement intention on the cortical muscle function integration and the introduction of a function connection network for analysis.
The existing network analysis is basically based on electroencephalogram signals, myoelectricity signals are not added, only human brains are analyzed, and the relation between relevant areas between cortex and muscles cannot be obtained. Most of the existing brain networks are undirected networks, and the directional information of network connection is lacked. The granger causal relationship may clarify the directional relationship between variables. Therefore, the invention introduces a method based on the grand causal relationship and graph theory, and simultaneously adds myoelectric signals, selects brain area electrodes related to motor functions and constructs a brain muscle network. The out-degree and in-degree, the clustering coefficient and the local efficiency of the nodes are extracted to be characteristic parameter analysis, the power spectral density of the electroencephalogram signal is analyzed, the mechanism of how the movement intention influences the cortical muscle function integration is revealed through indexes, and an internal basis is provided for the effectiveness of passive training.
Disclosure of Invention
In order to further improve the evaluation means of the training effect of the training mode on the stroke patient and the fuzzy of the motor intention influencing the cortical muscle function integration mechanism, the brain electrical signal, the myoelectrical signal and other easily obtained physiological signals are used for analysis. The invention changes the traditional method for evaluating the rehabilitation effect of active training and motor training on the cerebral apoplexy patient by using behavior and action indexes, overcomes the defect that the brain network can only observe the relation between brain areas, and creatively provides a motor intention brain muscle network analysis method based on the grand cause and effect relation and the graph theory.
The method comprises the steps of synchronously acquiring multichannel electroencephalogram signals and electromyogram signals of upper limb movement of a patient under two different movement modes through electroencephalogram and electromyogram acquisition equipment, processing the electroencephalogram signals and the electromyogram signals, carrying out power spectral density analysis on the electroencephalogram signals, calculating the causal relationship between time sequences of the electroencephalogram signals and the electromyogram signals by utilizing the Glanberg causal relationship, selecting electrodes of brain areas related to movement, constructing a directional and unweighted brain-muscle network, selecting a proper threshold value through a significance level method to obtain a sparse matrix, and extracting network characteristics such as the output and the input of nodes, a clustering coefficient, local efficiency and the like as indexes of cortical muscle function integration under movement intentions. By comparing the comprehensive indexes of the active movement mode and the passive movement mode, the rehabilitation effect of the patient can be evaluated for the movement modes, and how the movement intention influences the internal mechanism of the cortical muscle function integration can be further disclosed.
In order to achieve the above object, the method of the present invention mainly comprises the following steps:
And 2, preprocessing the obtained original data. The method comprises the following specific steps:
and 2-1, removing data with large artifacts, performing independent component analysis on the electroencephalogram signals, and performing empirical mode decomposition on the electromyogram signals. Filtering the EEG signal with a 4-order Butterworth filter to extract delta frequency band, theta frequency band,
Frequency band, beta frequency band and gamma frequency band.
2-2, performing down-sampling on the electromyographic signals to enable the electromyographic signals to have the same sampling frequency as the electroencephalographic signals.
And 3, calculating the power spectral density of the brain electrical signal preprocessed in the step 2 as the characteristics of the brain electrical signal.
And 4, calculating the causal relationship between the electroencephalogram signals and the electromyogram signals based on the Glanberg causal relationship, and constructing a brain muscle network adjacency matrix by taking the obtained causal relationship as an edge. And carrying out binarization on the adjacent matrix to obtain a binarization matrix. The method comprises the following specific steps:
and 4-1, constructing a constrained autoregressive model and an unconstrained autoregressive model between the electroencephalogram signal and the electromyogram signal.
And 4-2, respectively obtaining a causal expression of the electroencephalogram signal to the electromyogram signal and a causal expression of the electromyogram signal to the electroencephalogram signal according to the Glange causal relationship, and calculating the causal relationship of the electroencephalogram signal and the electromyogram signal.
And 4-3, constructing a brain muscle network adjacent matrix by using the obtained causal relationship as an edge, selecting a threshold value by using a significance method to carry out binarization processing on the adjacent matrix, setting the value larger than the threshold value as 0, and setting the value smaller than the threshold value as 1 to obtain a binarization matrix.
And 5, obtaining the topological structure of the brain muscle network by using the binarization matrix obtained in the step 4 according to a graph theory method, and calculating the out-degree and in-degree, the clustering coefficient and the local efficiency of the network nodes as the characteristics of the brain muscle network.
And 6, depicting a topographic map of the power spectral density of the electroencephalogram signals and a curve chart of the output, input, local efficiency and clustering coefficients of the brain muscle network under different frequency bands of the two motion modes through the electroencephalogram signal characteristics obtained in the step 3 and the brain muscle network characteristics obtained in the step 5.
And 7, analyzing the relation between the brain area and the muscle through the result obtained in the step 6, wherein the power spectral density characteristic of the electroencephalogram signal and the network characteristic parameters (in-degree, out-degree, local efficiency and clustering coefficient) of the brain muscle network can be used as indexes for influencing cortical muscle function integration by the movement intention, so that an internal basis is provided for the effectiveness of active training, and the method can be used for evaluating the rehabilitation effect of the active training and the passive training.
Compared with the prior method and technology, the invention has the beneficial effects that: in addition, only the quality of the rehabilitation effect can be obtained through the behavior action, and the internal mechanism of the difference of the two training modes cannot be known. The method directly extracts the human body surface bioelectric signals for analysis, expands an evaluation mode, fuses electroencephalogram signals and myoelectric signals based on a Glangel causal relationship and graph theory method, constructs a brain muscle network, can observe and analyze the relationship between brain areas and muscles, researches information flow between brains and muscles of patients, finds a potential mechanism of integration of motor intention on cortical muscle functions, and particularly has potential application value in rehabilitation training of stroke patients.
Drawings
FIG. 1 is a general flow diagram of the present invention;
FIG. 2 is a process diagram of an experiment;
FIG. 3 is a brain map of power spectral density of different frequency bands for two motion modes;
FIG. 4 is a diagram of directional adjacency matrices of different frequency bands in two motion modes;
FIG. 5 is a network diagram of brain muscles with different frequency bands in two exercise modes;
fig. 6 shows the network characteristic parameters of the brain muscle in different frequency bands in two motion modes.
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:
subjects included 11 men and 9 women, potential subjects who had not had intense exercise, had no physiological disease or had taken interfering medications one week prior to the experiment, all of whom had signed informed consent and were approved by the local ethics committee prior to the experiment. The first motion mode is active motion, the testee completes the gripping action of the grip dynamometer according to the instruction, and the second motion mode is passive motion, and corresponding actions are completed by means of mechanical equipment. The experimental flow is shown in fig. 2, and the specific steps are as follows:
step 1: subjects sat comfortably in front of a computer and they had a rest for 5 minutes before the start of the experiment to keep the body relaxed.
Step 2: in order to prevent the expecting the start of the experiment, the experimenter is required to wait for 10 seconds and look at the screen, according to the prompt of the screen, the corresponding gripping action is completed within 2 seconds, the holding time is kept for about 5 seconds, then the hands are opened for relaxation within 5 seconds, after each experiment, the experimenter takes a rest for 20 seconds, and the whole process is repeated for 10 times.
And 3, step 3: and repeating the steps with the aid of mechanical equipment to complete corresponding actions.
And 2, preprocessing the obtained original data. The method comprises the following specific steps:
and 2-1, removing data with large artifacts, and performing independent component analysis on the electroencephalogram signals, wherein the independent component analysis is performed by using an eeglab tool provided in matlab. And carrying out empirical mode decomposition on the electromyographic signals. Filtering the EEG signal with a 4-order Butterworth filter to extract delta frequency band, theta frequency band,
Frequency band, beta frequency band and gamma frequency band.
And 2-2, performing down-sampling on the electromyographic signals to enable the electromyographic signals to have the same sampling frequency as the electroencephalographic signals.
And 3, calculating the power spectral density of the brain electrical signal preprocessed in the step 2 by using a WeilChi algorithm as the characteristic of the brain electrical signal. The topographical map of the power spectral density is shown in fig. 3.
And 4, calculating the causal relationship between the electroencephalogram signals and the electromyogram signals based on the Glanberg causal relationship, and constructing a brain muscle network adjacency matrix by using the obtained causal relationship as an edge, wherein the adjacency matrix is shown in FIG. 4.
And carrying out binarization on the adjacent matrix to obtain a binarization matrix. The method comprises the following specific steps:
4-1, constructing a constrained autoregressive model and an unconstrained autoregressive model between the electroencephalogram signal and the electromyogram signal, wherein the model is constructed as follows:
var(ε1t)＝Σ1，var(η1t)＝Σ2 (5)
var(ε2t)＝∑3，var(η2t)＝∑4 (6)
wherein XtAnd YtIs a constrained autoregressive model, X, between electroencephalogram and electromyogram signalst1And Yt2Is a non-constrained autoregressive model between electroencephalogram signals and electromyogram signals, a1iAnd a1jIs the regression coefficient of the constrained autoregressive model, a2i、b2i、c2jAnd d2jIs the regression coefficient of the unconstrained autoregressive model, epsilon1tAnd η1t、ε2tAnd η2tIs a residual error, is white noise, sigma, which is mutually uncorrelated1Sum-sigma2Is respectively epsilon1tAnd η1tVariance of (E), sigma3Sum-sigma4Are respectively epsilon2tAnd η2tP and q are lag terms of the model.
And 4-2, respectively obtaining a causal expression of the electroencephalogram signal to the electromyogram signal and a causal expression of the electromyogram signal to the electroencephalogram signal according to the Glange causal relationship, and calculating the causal relationship of the electroencephalogram signal and the electromyogram signal. The expression is as follows:
FX→Yis a causal relationship from electroencephalogram signals to electromyographic signals, FY→XIs the causal relationship from myoelectric signals to electroencephalogram signals.
And 4-3, constructing a brain muscle network adjacent matrix by using the obtained causal relationship as an edge, selecting a threshold value by using a significance level method to carry out binarization processing on the adjacent matrix, setting the value larger than the threshold value as 0, and setting the value smaller than the threshold value as 1 to obtain a binarization matrix. The formula calculated by the significance level method is as follows:
where L is the length of the data,
is the degree of significance, will
Set to 0.95.
And 5, obtaining the topological structure of the brain muscle network by using the binarization matrix obtained in the step 4 according to a graph theory method, wherein a brain muscle network connection graph of active motion and passive motion is shown in figure 5. And calculating the out-degree and in-degree, the clustering coefficient and the local efficiency of the network nodes as the brain muscle network characteristics. The brain muscle network characteristics were calculated using the BCT toolkit provided in Matlab. The characteristics of the brain muscle network were calculated as follows:
(1) out-degree and in-degree of node i:
Di in＝∑iaji (11)
wherein Di outAnd Di inRespectively the out-degree and in-degree of the node i, aijAnd ajiAre all elements in the binary matrix, when nodes i and j have connection, aijWhen nodes j and i have a connection, aji＝1
(2) The local efficiency of the network is:
wherein N isiRepresenting a set of nodes, n, directly connected to node iiIs in the set NiTotal number of nodes of lijRepresenting the shortest path length between nodes i and j.
(3) The clustering coefficient of the network is:
wherein k isiIs the number of nodes in the neighborhood of node i, EiIs the number of actual connecting edges that exist between node i and the neighboring nodes.
And 6, depicting a topographic map of the power spectral density of the electroencephalogram signals and a curve chart of the output, input, local efficiency and clustering coefficients of the brain muscle network under different frequency bands of the two motion modes through the electroencephalogram signal characteristics obtained in the step 3 and the brain muscle network characteristics obtained in the step 5.
And 7, analyzing the relation between the brain area and the muscle through the result obtained in the step 6, wherein the power spectral density characteristic of the electroencephalogram signal and the network characteristic parameters (in-degree, out-degree, local efficiency and clustering coefficient) of the brain muscle network can be used as indexes for influencing cortical muscle function integration by the movement intention, so that an internal basis is provided for the effectiveness of active training, and the method can be used for evaluating the rehabilitation effect of the active training and the passive training.
From the existing research results, it can be seen from fig. 3 that the power spectral density of the electroencephalogram signal of each frequency band is higher than that of the passive motion under the active motion, which indicates that the brain is more active during the active motion, and the power spectral density is reduced with the increase of the frequency under both motion modes. From the brain muscle network diagram, as shown in fig. 5, it can be seen that the number of connecting edges of the brain muscle network diagram during passive exercise is significantly reduced compared to active exercise under different frequency bands, that is, the complexity of the network is significantly reduced, and the function shows a downward trend. By calculating the characteristic parameters of the brain muscle network in the two movement modes and drawing a curve chart as shown in fig. 6, the method can find that the output and the input of the brain muscle network node, the local efficiency and the clustering coefficient of the active movement in different frequency bands are improved, the brain muscle network with the active movement shows higher complexity, the information interaction between the brain and the muscle is enhanced, and the information transmission efficiency is higher compared with the passive movement. Indexes of power spectral density of brain electricity signals and network characteristic parameters (in-degree, out-degree, local efficiency and clustering coefficient) of a brain muscle network can be used as indexes of motor intention influencing cortical muscle function integration, and an internal basis is provided for effectiveness of active training.
The above embodiment is only one embodiment of the present invention, and is not intended to be limiting. It should be noted that, for those skilled in the art, modifications can be made to the invention without departing from the technical principle of the invention, and such modifications should be considered as the protection scope of the present application.
Claims (6)
1. The motor intention brain muscle network analysis method based on the Glangel causal relationship and the graph theory is characterized in that: the method comprises the following steps:
step 1, synchronously acquiring electroencephalogram signals of channels F3, FZ, F4, FC5, FC1, FC2, FC6, C3, C4, CP5, CP1, CP2 and CP6 and electromyogram signals of 4 channels in the process of grasping an upper limb by electroencephalogram and electromyogram acquisition equipment according to international 10-20 standards, wherein 4 electromyogram electrodes come from superficial flexors, extensors of fingers, flexors of ulnar wrist and extensors of radial wrist, and the acquired signals are used as original data;
step 2, preprocessing the obtained original data; the method comprises the following specific steps:
step 2-1, removing data with large artifacts, performing independent component analysis on the electroencephalogram signals, and performing empirical mode decomposition on the electromyogram signals; filtering the EEG signals by using a filter, and extracting EEG signals of a delta frequency band, a theta frequency band, an ∂ frequency band, a beta frequency band and a gamma frequency band;
2-2, performing down-sampling on the electromyographic signals to enable the electromyographic signals to have the same sampling frequency as the electroencephalographic signals;
step 3, calculating the power spectral density of the brain electrical signal preprocessed in the step 2 as the characteristics of the brain electrical signal;
step 4, calculating a causal relationship between the electroencephalogram signals and the electromyogram signals based on the Glankey causal relationship, and constructing a brain muscle network adjacency matrix by taking the obtained causal relationship as an edge; carrying out binarization on the adjacent matrix to obtain a binarization matrix; the method comprises the following specific steps:
step 4-1, constructing a constrained autoregressive model and a non-constrained autoregressive model between the electroencephalogram signal and the electromyogram signal;
4-2, respectively obtaining a causal expression of the electroencephalogram signal to the electromyogram signal and a causal expression of the electromyogram signal to the electroencephalogram signal according to the Glange causal relationship, and calculating the causal relationship of the electroencephalogram signal and the electromyogram signal;
step 4-3, constructing a brain muscle network adjacent matrix by using the obtained causal relationship as an edge, selecting a threshold value by using a significance method to carry out binarization processing on the adjacent matrix, setting the value larger than the threshold value as 0, and setting the value smaller than the threshold value as 1 to obtain a binarization matrix;
step 5, obtaining the topological structure of the brain muscle network by using the binarization matrix obtained in the step 4 according to a graph theory method, and calculating the out-degree and in-degree, the clustering coefficient and the local efficiency of network nodes as brain muscle network characteristics;
step 6, depicting a topographic map of the power spectral density of the electroencephalogram signals and a curve chart of the output, input, local efficiency and clustering coefficients of the brain muscle network under different frequency bands of two motion modes through the electroencephalogram signal characteristics obtained in the step 3 and the brain muscle network characteristics obtained in the step 5;
and 7, analyzing the relation between the brain area and the muscle according to the result obtained in the step 6, wherein the characteristics of the electroencephalogram signal and the network characteristics of the brain muscle network are used as indexes for influencing the integration of the cortical muscle function by the movement intention, and an internal basis is provided for the effectiveness of the active training.
2. The method for analyzing motor intention brain-muscle network based on Glandum causal relationship and graph theory according to claim 1, wherein in the step 1, according to the international 10-20 standard, the G.MOBllab MP-2015 wireless brain-electricity system and the Trigno TM wireless myoelectricity collector are adopted to synchronously record the signals of brain electricity and myoelectricity.
3. The method for analyzing motor intention brain muscle network based on Glangel causality and graph theory according to claim 1, wherein the myoelectric signals are subjected to empirical mode decomposition in the step 2-1, the EEG signals are filtered by a 4-order Butterworth filter, and EEG signals of delta frequency band, theta frequency band, ∂ frequency band, beta frequency band and gamma frequency band are extracted.
4. The method for analyzing motor intention brain muscle network based on Glangel causality and graph theory according to claim 1, wherein the step 3 adopts a Welch algorithm to obtain the power spectral density of the brain electrical signal.
5. The method for analyzing a motor intention brain muscle network based on the glargian causal relationship and graph theory according to claim 1, wherein the step 4 constructs a adjacency matrix of the brain muscle network based on the glargian causal relationship between the electroencephalogram signal and the electromyogram signal.
6. The method for analyzing motor intention brain muscle network based on Glangel's causal relationship and graph theory according to claim 1, wherein the topology of the brain muscle network is obtained in the step 5 through the graph theory method, and the brain muscle network characteristics are calculated by using BCT toolkit provided in Matlab.
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