Brain muscle function change coupling analysis method and system based on bidirectional delay compensation

Abstract

The invention discloses a brain muscle function change coupling analysis method based on bidirectional delay compensation, and the method comprises the steps: designing an experiment normal form, and carrying out the signal collection and signal preprocessing; determining a bidirectional cortical muscle delay estimation algorithm based on a sliding window and a transfer entropy TE, and quantifying conduction time of an EEG-EMG outgoing path and an EMG-EEG incoming path of cortical muscles of a tester; signal time alignment compensation is carried out, a transfer entropy with delay compensation and an average delay compensation transfer entropy are calculated, and a directional cortical muscle connection CMC network with directional delay compensation is constructed based on the transfer entropy with delay compensation and the average delay compensation transfer entropy; extracting evaluation features, and performing brain muscle function change coupling analysis based on the evaluation features; evaluation features are extracted based on dimensions of conduction delay, CMC coupling strength of hemisphere and task stages, hemisphere bias index, and delay-motion function behavioral index score correlation. The invention further discloses a corresponding system, electronic equipment and a computer readable storage medium.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of neuro-rehabilitation medicine and biomedical signal processing technology, and in particular to a brain-muscle function change coupling analysis method and system based on bidirectional delay compensation. BACKGROUND

[0002] There is a time delay (usually 20-30 ms) in the transmission of electroencephalogram (EEG) and electromyogram (EMG) signals. Traditional cortico-muscular coupling (CMC) analysis does not compensate for the delay, resulting in a time mismatch of the signals, which can mask the true functional coupling and reduce the sensitivity of the evaluation. In addition, existing delay estimation algorithms (such as the phase method and the optimization method) can only estimate the delay in a single direction (such as the outgoing direction), and cannot capture the delay changes in the afferent pathway, which is crucial for motor control.

[0003] To solve the above problems, researchers have tried to quantify the cortico-muscular functional connection through CMC analysis, but the existing technology still has two key bottlenecks: traditional CMC analysis mostly uses coherence methods, which can only capture linear interactions and cannot reflect the nonlinear neural interactions between the cortex and muscles (such as the complex regulatory relationships during the brain network reorganization process after stroke); neural signals during motor tasks are non-stationary (such as the differences in neural activity during the grip force rising period and the maintenance period), and traditional methods do not use dynamic analysis techniques such as sliding windows, so they cannot capture the CMC changes in different task stages

[0004] Therefore, there is an urgent need to design a CMC analysis method that can estimate the bidirectional delay of the cortex and muscles, compensate for the time mismatch of the signals, and capture dynamic nonlinear interactions, providing an objective and sensitive evaluation tool for brain-muscle function analysis and promoting its transition from "experience-driven" to "data-driven". SUMMARY

[0005] The purpose of the present application is to provide a method and system for analyzing the coupling of brain and muscle function changes based on bidirectional delay compensation, which is suitable for brain-computer interaction, motor function evaluation and other scenarios, and promotes the transformation from "experience-driven" to "data-driven". The method and system are based on the correlation mechanism of "cortical muscle bidirectional delay-function coupling-motor function", combined with transfer entropy (TE) and sliding window technology, to design a set of cortical muscle connection (CMC) analysis framework with bidirectional delay compensation. First, collect and preprocess the EEG and EMG signals, then propose a bidirectional brain-muscle conduction delay estimation algorithm, which globally optimizes the estimation of downlink and uplink conduction time by maximizing the significant transfer entropy unit across channels and time windows. Next, the estimated delay is compensated, and the high-sensitivity delay-compensated directional brain-muscle coupling strength is calculated. Finally, features are extracted from multiple dimensions such as conduction delay, coupling strength (hemisphere and task phase), hemisphere lateralization index, and delay-motor function behavior index score correlation, and the brain-muscle function changes are quantified through statistical analysis. Thus, the problems of subjectivity, low sensitivity, and inability to quantify cortical muscle bidirectional interaction in the prior art are solved.

[0006] The first aspect of the present application is to provide a method for analyzing the coupling of brain and muscle function changes based on bidirectional delay compensation, comprising:

[0007] S1, designing an experimental paradigm, based on which signal acquisition and signal preprocessing are performed;

[0008] S2, determining a bidirectional cortical muscle delay estimation algorithm based on sliding window and transfer entropy TE, and quantifying the conduction time of the EEG→EMG outgoing path and the EMG→EEG incoming path of the cortical muscle of the test subject based on the bidirectional cortical muscle delay estimation algorithm and the preprocessed signals;

[0009] S3, performing signal time alignment compensation based on the conduction time of the EEG→EMG outgoing path and the EMG→EEG incoming path, calculating the transfer entropy with delay compensation and the average delay compensation transfer entropy, and constructing a directional delay-compensated directional cortical muscle connection (CMC) network based on the transfer entropy with delay compensation and the average delay compensation transfer entropy, wherein the directional delay-compensated directional CMC network is used to visually display the strength distribution of the cortical muscle bidirectional functional connection;

[0010] S4, extracting evaluation features, and performing brain-muscle function change coupling analysis based on the evaluation features; wherein the evaluation features are extracted based on four dimensions of conduction delay, CMC coupling strength in hemispheres and task phases, hemisphere lateralization index and delay-motor function behavior index score correlation.

[0011] Preferably, S1 comprises:

[0012] S11, Collect EEG signals, EMG signals, and behavioral data from the test subjects;

[0013] S12, preprocess the EEG signals, EMG signals, and behavioral data to remove noise and artifacts and retain effective neural electrical signals.

[0014] Preferably, the preprocessing includes:

[0015] (1) Perform signal downsampling, including: downsampling the EEG signal from 2000Hz to 1000Hz, downsampling the EMG signal from 2000Hz to 1000Hz, and downsampling the grip strength signal in the behavioral data from 200Hz to 1000Hz;

[0016] (2) Noise filtering process, including: extracting the beta band signal using a 13-30Hz bandpass filter; removing power frequency noise using a 50Hz notch filter; wherein the beta band signal is the dominant frequency band of cortical muscle functional connectivity;

[0017] (3) Removal of artifacts, including: based on independent component analysis (ICA) combined with the ICLABEL plugin, identification and removal of electrooculography artifacts, electrocardiogram artifacts and electromyography interference artifacts in the EEG signal;

[0018] (4) Perform signal rereference and segmentation: Rereference the EEG signal as the average reference of all channels; divide the grip force tracking period into resting phase S0, ramp-up phase S1 and maintenance phase S2 according to the contour of the grip force signal, and synchronously segment the EEG signal and the EMG signal according to the segmentation of the grip force tracking period.

[0019] (5) Baseline correction: Using the phase signal of resting phase S0 as the baseline, the phase signals of ramp-up phase S1 and maintenance phase S2 are corrected.

[0020] Preferably, S2 includes:

[0021] S21, Set a sliding window, including: using an overlapping sliding window with a length of 500ms and a step size of 250ms to divide the EEG signal and EMG signal after the synchronous segmentation;

[0022] S22, perform phase space reconstruction, including: perform phase space reconstruction on the EEG signal and EMG signal in each sliding window to obtain equation (1) and equation (2): (1);

[0023] (2);

[0024] In the formula, This represents the time series of EEG signals. This represents the time series of electromyographic (EMG) signals. The associated time delay is determined by the correlation integral method. , All represent the embedding dimension, determined by the Cao criterion;

[0025] S23, Calculate the transfer entropy TE, including: calculating the transfer entropy in the outgoing direction EEG→EMG respectively. The transfer entropy with the input direction EMG→EEG ,in Characterizing the predictive ability of EEG signals to EMG signals. Transition entropy characterizes the predictive ability of electromyography (EMG) signals on electroencephalography (EEG) signals. and transfer entropy The calculation formulas are shown in (3) and (4) respectively:

[0026] (3);

[0027] (4);

[0028] In the formula, H(·) represents the conditional entropy, and ε represents the time interval. In this embodiment, it is set to 1. Those skilled in the art can make appropriate settings as needed, all of which are within the protection scope of this invention. They are respectively Embedding vectors of EEG and EMG signals at any given time; and Both are scalar random variables, representing two time series, EEG signals and EMG signals, respectively, at a specific future moment. The observed values ​​or state, Indicates from the current reference time Beginning, moving towards the future A moment after that time;

[0029] S24, Calculate the transfer entropy (DTE) with delay compensation, including: candidate delay based on the output direction. Time-align the signals to calculate the delay-compensated transfer entropy from EEG to EMG in the outgoing direction. And candidate delays based on the incoming direction Time-align the signals to calculate the delay-compensated transfer entropy from EMG to EEG in the incoming direction. The transfer entropy of the outgoing direction EEG→EMG with delay compensation and the transfer entropy of the input direction EMG→EEG with delay compensation The calculation formulas are shown in (5) and (6) respectively:

[0030] (5);

[0031] (6);

[0032] In the formula, For sliding window index, and These are the channel indices for EEG and EMG signals, respectively. For delay Embedded vector of EMG signal after electromyography (EMG) For delay Embedded vectors of EEG signals after EEG; It is a scalar random variable, representing the value at the th . Within a sliding analysis window, data from electromyography channels... The signal at time point The instantaneous amplitude or state; It is a scalar random variable, representing the value at the th . Within a sliding analysis window, data from the EEG channel... The signal at the time point The instantaneous amplitude or state;

[0033] S25, verifying the statistical significance of the delay-compensated transfer entropy DTE based on the alternative data method, including: generating the alternative data by performing 10,000 phase randomizations on the original EEG-EMG signal, and calculating the delay-compensated transfer entropy DTE value of the alternative data. The t-test was used to determine whether the delay-compensated transfer entropy (DTE) value of the original EEG-EMG signal was significantly higher than the delay-compensated DTE value of the surrogate data. The delay-compensated transfer entropy (DTE) value of the original EEG-EMG signal is significantly higher than that of the delay-compensated transfer entropy (DTE) value of the surrogate data. This indicates that there is a directed causal interaction between the channel pairs;

[0034] S26, Perform bidirectional delay optimization, including: designing optimization criteria to maximize the sum of significant DTEs across EEG-EMG channel pairs and sliding windows, and searching for the optimal delay in the outgoing direction after optimization. And optimized input direction optimal delay The optimized outgoing direction optimal delay And optimized input direction optimal delay The optimization formulas are shown in equations (7) and (8):

[0035] (7);

[0036] (8);

[0037] In the formula, and These are the channel indices for EEG and EMG signals, respectively. For sliding window index, This is the DTE change tracking function. The indicator function takes the value 1 when the delay-compensated transfer entropy (DTE) is significant, and 0 otherwise. , These are the estimated corticomuscular conduction times for the efferent pathway and the afferent pathway, respectively. It is a positive integer representing the summation symbol. The upper limit represents the total number of EEG signal channels in the system, channel index. Iterate from 1 to It covers all available EEG signal channels; It is a positive integer representing the summation symbol. The upper limit represents the total number of EMG signal channels in the system, channel index. Iterate from 1 to It covers all available electromyographic (EMG) signal channels; It is a positive integer representing the summation symbol. The upper limit represents the total number of sliding windows divided over the entire data time series; the sliding window index. Iterate from 1 to It covers all analysis time segments from the beginning to the end of the experiment.

[0038] Preferably, S3 includes:

[0039] S31, based on the optimized outgoing direction optimal delay And optimized input direction optimal delay The EEG and EMG signals were time-aligned.

[0040] S32, Substitute the EEG and EMG signals aligned over the specified time into formulas (9) and (10) to calculate the delay-compensated transfer entropy of the EEG→EMG transmission direction. And the transfer entropy of the input direction EMG→EEG with delay compensation after delay compensation. ;

[0041] (9);

[0042] (10);

[0043] In the formula, For delay The subsequent EMG embedding vector, For delay The EEG embedding vector is then used; ε represents the time interval. Indicates EMG signals at future moments The instantaneous amplitude or state; Indicates brainwave (EEG) signals at future moments The instantaneous amplitude or state; where: This is the current moment in the analysis, i.e., the reference point; and The optimal physiological conduction delay is estimated and obtained through a global search using the aforementioned optimization formulas (7) and (8). This composite time point represents the exact future moment when the electromyographic or electroencephalographic result signal of interest should appear after compensating for the estimated, pathway-specific physiological conduction time.

[0044] S33, Calculate the average delay-compensated transfer entropy, including: verifying the statistical significance of the delay-compensated transfer entropy in the same hemisphere and the same task phase based on the alternative data method to obtain all significant delay-compensated transfer entropies; averaging all significant delay-compensated transfer entropies to obtain the average delay-compensated transfer entropy. The average delay compensation transfer entropy As a quantitative index of CMC coupling strength, it is used to characterize the cortical-muscle functional coupling strength. The calculation formula is shown in Equation (11):

[0045] (11);

[0046] In the formula, These represent the selected EEG channel set, EMG channel set, and sliding window set, respectively, where N(·) is the cardinality of the sets. This represents the optimal delay for the corresponding direction;

[0047] S34, Construct a visualized, directionally delayed, compensated, directionally cortical-muscle connectivity (CMC) network, including: using EEG and EMG channels as nodes, and using the average delay to compensate for the transfer entropy. Using edge weights, a directional cortical muscle connectivity (CMC) network with directional delay compensation is constructed, wherein the EEG channel is the sensor motion cortical region.

[0048] Preferably, 18 channels in the sensor's motion cortex region are selected as the EEG channels. The 18 channels include FC1, FC3, FC5, C1, C3, C5, CP1, CP3, and CP5 channels in the left hemisphere and FC2, FC4, FC6, C2, C4, C6, CP2, CP4, and CP6 channels in the right hemisphere. According to the tester's target functional side, the 18 channels are classified into target hemisphere channels and control hemisphere channels.

[0049] Preferably, S4 includes:

[0050] S41, extracting cortical muscle conduction time features based on the conduction delay dimension, including: extracting the optimized outward direction optimal delay. And optimized input direction optimal delay As a characteristic of cortical-muscular conduction time, it reflects the conduction efficiency of the neural pathway;

[0051] S42, extract CMC coupling strength features based on the CMC coupling strength dimension of the hemisphere and task-specific phases, including: extracting the average delay compensation transfer entropy of the efferent and afferent directions of the target hemisphere and the control hemisphere in the resting phase S0, ramp-up phase S1 and maintenance phase S2 as the CMC coupling strength features, thereby reflecting the cortical muscle function interaction strength.

[0052] S43, based on the hemispheric lateralization index Dimensional extraction of the hemispherical lateralization index Features include: calculating the hemispheric lateralization index based on the average delay-compensated transfer entropy of the target side and the control side. This quantifies the degree of lateralization in motion control functions, specifically the hemispherical lateralization index. The calculation formula is shown in equation (12):

[0053] (12);

[0054] In the formula, The average delay compensation transfer entropy for the target hemisphere, To compensate for the average delay in the transfer entropy of the control hemisphere, The value range is [-1, 1], with positive values ​​indicating that the target hemisphere is dominant and negative values ​​indicating that the control hemisphere is dominant.

[0055] S44, extract behavioral and motor function scoring features based on the correlation dimension of the delayed-motor function behavioral index score, including: taking the RMSE of the grip strength tracking task as the behavioral feature of fine motor control ability assessment, and taking the behavioral and motor function scores as the motor function scoring features.

[0056] S45, Perform a coupling analysis of brain muscle function changes based on the assessment features.

[0057] A second aspect of the present invention provides a brain-muscle function change coupling analysis system based on bidirectional delay compensation, for implementing the method of the first aspect, comprising:

[0058] The data acquisition module (101) is used to design experimental paradigms and perform signal acquisition and signal preprocessing based on the experimental paradigms.

[0059] The conduction time optimization module (102) is used to determine the bidirectional cortical muscle delay estimation algorithm based on the sliding window and the transfer entropy TE, and to quantify the conduction time of the EEG→EMG efferent pathway and the EMG→EEG infferent pathway of the cortical muscle of the test personnel based on the bidirectional cortical muscle delay estimation algorithm and the preprocessed signal.

[0060] The network construction and coupling strength delay compensation module (103) is used to perform signal time alignment compensation based on the transmission time of the EEG→EMG outgoing path and the EMG→EEG incoming path, calculate the delay-compensated transfer entropy and the average delay-compensated transfer entropy, and construct a directional delay-compensated directional cortico-muscle connection CMC network based on the delay-compensated transfer entropy and the average delay-compensated transfer entropy. The directional delay-compensated directional cortico-muscle connection CMC network is used to visually display the strength distribution of bidirectional functional connections of cortico-muscle.

[0061] The brain muscle function change coupling analysis module (104) is used to extract assessment features and perform brain muscle function change coupling analysis based on the assessment features; wherein, the assessment features are based on conduction delay, CMC coupling strength in hemispheric and task-specific stages, and hemispheric lateralization index. The data was extracted from four dimensions: the correlation between delayed-motion function behavioral index scores and other data.

[0062] A third aspect of the present invention provides an electronic device including a processor and a memory, the memory storing a plurality of instructions, the processor being configured to read the instructions and execute the method as described in the first aspect.

[0063] A fourth aspect of the present invention provides a computer-readable storage medium storing a plurality of instructions which can be read by a processor and executed as described in the first aspect.

[0064] The beneficial effects of the method and system of the present invention are as follows:

[0065] 1. It achieves precise quantification and bidirectional separation of cortical-muscle conduction time, overcoming the limitations of unidirectional assessment.

[0066] Traditional methods are often limited to assessing unidirectional conduction from the brain to the muscle (EEG→EMG). This invention introduces a bidirectional cortical-muscle delay estimation algorithm, enabling precise quantification of the conduction time in both the efferent (EEG→EMG) and afferent (EMG→EEG) pathways. This is the first methodological achievement that separates and independently measures the ascending and descending signal conduction velocities in the cortical-muscle circuit, providing crucial data for in-depth research into the neural mechanisms of motor control and sensory feedback, and overcoming the limitations of traditional unidirectional assessment.

[0067] 2. Construct a high-fidelity orientational functional connectivity network to significantly improve the realism and intuitiveness of brain-muscle coupling analysis.

[0068] This invention innovatively introduces a delay compensation mechanism to solve the calculation error of coupling strength caused by asynchronous signal transmission time. By calculating the transfer entropy with delay compensation and constructing a CMC network with directional delay compensation, the strength of bidirectional functional connections between the brain and muscles can be more realistically reflected. This network displays the spatial distribution of coupling strength in an intuitive and visual way, enabling researchers to clearly identify the core brain-muscle interaction pathways, greatly improving the accuracy and interpretability of the analysis results.

[0069] 3. Provides a multi-dimensional and comprehensive assessment system to achieve panoramic correlation analysis from neurophysiology to behavioral performance.

[0070] The system extracts comprehensive assessment features through four dimensions: conduction delay, hemispheric / stage-specific CMC strength, hemispheric lateralization index, and correlation between delay and behavioral scores. This multi-dimensional framework avoids the one-sidedness of a single indicator and can simultaneously reveal: temporal characteristics (conduction speed), spatial characteristics (division of labor and lateralization between the left and right hemispheres), state characteristics (coupling dynamics in different task stages), and functional significance (correlation between neural conduction efficiency and motor behavior performance), thereby achieving a panoramic and in-depth interpretation of changes in brain-muscle function coupling.

[0071] 4. To provide standardized, localizable, objective, and quantitative tools for motor function assessment and neurorehabilitation research.

[0072] This invention integrates experimental paradigm design, signal acquisition, preprocessing, core algorithm calculation, and comprehensive analysis into a complete system. This modular and streamlined design highly standardizes the entire evaluation process, reduces human error, and ensures the repeatability and comparability of results. By precisely locating abnormal pathways with conduction delays (whether it's an efferent or afferent problem) and weakened coupling brain regions, this invention can provide strong data support for the accurate assessment of motor dysfunction, objective monitoring of rehabilitation efficacy, and the development of personalized rehabilitation plans in clinical practice.

[0073] 5. By using bidirectional delay compensation technology, based on accurate estimation and compensation of bidirectional transmission time of EEG and EMG, a more reliable coupling strength can be calculated, reducing the delay in the brain-muscle pathway, and accurately quantifying the delay of bidirectional information flow between the brain and muscle to calculate a more reliable coupling strength. Attached Figure Description

[0074] To more clearly illustrate the technical solutions in the specific embodiments or related technologies of the present invention, the drawings used in the description of the specific embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0075] Figure 1 This is a flowchart of a brain muscle function change coupling analysis method based on bidirectional delay compensation provided in an embodiment of the present invention; Figure 2 This is a flowchart of step S1 of the brain muscle function change coupling analysis method based on bidirectional delay compensation provided in an embodiment of the present invention; Figure 3 This is a flowchart of step S2 of the brain muscle function change coupling analysis method based on bidirectional delay compensation provided in an embodiment of the present invention; Figure 4 This is a flowchart of step S3 of the brain muscle function change coupling analysis method based on bidirectional delay compensation provided in an embodiment of the present invention; Figure 5 This is a flowchart of step S4 of the brain muscle function change coupling analysis method based on bidirectional delay compensation provided in an embodiment of the present invention; Figure 6 This is a flowchart and system architecture diagram of the steps S1 of the brain muscle function change coupling analysis method based on bidirectional delay compensation provided in an embodiment of the present invention. Figure 7 This is a structural diagram of an electronic device provided according to an embodiment of the present invention. Detailed Implementation

[0076] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0077] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0078] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0079] The following embodiments are intended to solve the following core technical problems:

[0080] (1) Achieve synergistic optimization of multiple performance indicators: Design a control method that can not only ensure high-precision trajectory tracking, but also strictly constrain and manage multiple performance indicators such as overshoot and convergence time, thereby comprehensively improving the efficiency and quality of rehabilitation training.

[0081] (2) Give the performance boundary dynamic adaptive capability: solve the safety problems that may be caused by the fixed performance boundary under sudden circumstances, and design a soft performance boundary that can be dynamically relaxed or tightened according to the real time of the system, so as to ensure safety while taking into account the optimal performance.

[0082] (3) Establish the relationship between interference and constraints and intelligently adjust the boundary: The concept of "safety boundary" is clearly proposed and a linkage mechanism between it and the performance boundary is constructed. When the system detects that the tracking error exceeds the safety tolerance due to interference, it can automatically trigger the adjustment strategy of temporarily relaxing the performance boundary, thereby resolving the conflict and ensuring the safe and stable operation of the system.

[0083] (4) Efficient integration of intelligent approximation and constraint learning: By utilizing the Actor-Critic framework of reinforcement learning and the approximation capability of neural networks, the lumped disturbances in the system are efficiently estimated and compensated; and the preset performance constraints are integrated into the learning framework to significantly reduce the policy search space, accelerate the convergence speed of the control policy, and improve learning efficiency.

[0084] The technical terms used in this embodiment and their meanings are as follows:

[0085] (1) Lumped disturbance: refers to the sum of all uncertainties acting on the controlled object. In the context of this patent, it is a unified concept, specifically including the unmodeled dynamics of the system, parameter changes, and external disturbances to the system.

[0086] (2) Soft preset performance: a dynamic performance constraint mechanism. It uses a time-varying, state-dependent performance boundary function to define the transient and steady-state behavior of the system tracking error (such as convergence speed, overshoot and steady-state error band).

[0087] (3) Reinforcement learning framework based on evaluator-executor: an adaptive optimization structure that uses two neural networks to learn in parallel.

[0088] (4) Command filtering technique: In the backstepping design framework, this is a numerical technique used to generate the virtual control law and its derivative. It takes the virtual control law calculated in the previous design step as the command input to a filter (usually a first- or second-order linear filter), and the output of the filter is the smoothed virtual control command. At the same time, its analytical derivative can be directly obtained. The main purpose of this technique is to fundamentally avoid the "analytic differential explosion" problem in the backstepping method, greatly simplify the controller structure, and introduce amplitude and rate limits by setting filter parameters.

[0089] (5) Filtering error compensation signal: A dynamic signal specifically designed to compensate for the phase lag and amplitude attenuation introduced by the dynamic characteristics of the command filter.

[0090] Example 1

[0091] like Figure 1 As shown, this embodiment provides a method for coupled analysis of brain muscle function changes based on bidirectional delay compensation, including:

[0092] S1. Design an experimental paradigm, and perform signal acquisition and signal preprocessing based on the experimental paradigm;

[0093] like Figure 2 As shown, in a preferred embodiment, S1 includes:

[0094] S11, Collect EEG signals, EMG signals, and behavioral data from the test subjects;

[0095] In this embodiment, the EEG signal acquisition uses a 64-channel Neuroscan SynAmps system, following the international 10-10 electrode system, with electrode impedance controlled below 10kΩ; the EMG signal acquisition uses a Trigno WirelessEMG system, with the test subject's EMG electrodes attached to the corresponding muscle area on the target side; behavioral data specifically refers to grip strength signals, which are acquired using a Vernier Hand Dynamometer at a sampling rate of 200Hz, used to divide task stages and calculate RMSE (Root Mean Square Error).

[0096] S12, preprocess the EEG signals, EMG signals and behavioral data to remove noise and artifacts and retain effective neural electrical signals;

[0097] In this embodiment, the preprocessing method includes:

[0098] (1) Perform signal downsampling, including: downsampling the EEG signal from 2000Hz to 1000Hz, downsampling the EMG signal from 2000Hz to 1000Hz, and downsampling the grip strength signal in the behavioral data from 200Hz to 1000Hz;

[0099] (2) Noise filtering process, including: extracting the beta band signal using a 13-30Hz bandpass filter; removing power frequency noise using a 50Hz notch filter; wherein the beta band signal is the dominant frequency band of cortical muscle functional connectivity;

[0100] (3) Removal of artifacts, including: based on independent component analysis (ICA) combined with the ICLABEL plugin, identification and removal of electrooculography artifacts, electrocardiogram artifacts and electromyography interference artifacts in the EEG signal;

[0101] (4) Perform signal rereference and segmentation: Rereference the EEG signal as the average reference of all channels; divide the grip force tracking period into resting phase S0, ramp-up phase S1 and maintenance phase S2 according to the contour of the grip force signal, and synchronously segment the EEG signal and the EMG signal according to the segmentation of the grip force tracking period.

[0102] (5) Baseline correction: Using the phase signal of resting phase S0 as the baseline, the phase signals of ramp-up phase S1 and maintenance phase S2 are corrected.

[0103] S2, a bidirectional cortical muscle delay estimation algorithm is determined based on the sliding window and the transfer entropy TE, and the conduction time of the EEG→EMG efferent pathway and the EMG→EEG afferent pathway of the cortical muscle of the test personnel is quantified based on the bidirectional cortical muscle delay estimation algorithm and the preprocessed signal.

[0104] like Figure 3 As shown, in a preferred embodiment, S2 includes:

[0105] S21, Set a sliding window, including: using an overlapping sliding window with a length of 500ms and a step size of 250ms to divide the EEG signal and EMG signal after the synchronous segmentation;

[0106] S22, perform phase space reconstruction, including: perform phase space reconstruction on the EEG signal and EMG signal in each sliding window to obtain equation (1) and equation (2):

[0107] (1);

[0108] (2);

[0109] In the formula, This represents the time series of EEG signals. This represents the time series of electromyographic (EMG) signals, where τ represents the associated time delay, determined by the correlation integral method. , All represent the embedding dimension, determined by the Cao criterion;

[0110] S23, Calculate the transfer entropy TE, including: calculating the transfer entropy in the outgoing direction EEG→EMG respectively. The transfer entropy with the input direction EMG→EEG ,in Characterizing the predictive ability of EEG signals to EMG signals. Transition entropy characterizes the predictive ability of electromyography (EMG) signals on electroencephalography (EEG) signals. and transfer entropy The calculation formulas are shown in (3) and (4) respectively:

[0111] (3);

[0112] (4);

[0113] In the formula, H(·) represents the conditional entropy, and ε represents the time interval. In this embodiment, it is set to 1. Those skilled in the art can make appropriate settings as needed, all of which are within the protection scope of this invention. They are respectively Embedding vectors of EEG and EMG signals at any given time; and Both are scalar random variables, representing two time series, EEG signals and EMG signals, respectively, at a specific future moment. The observed values ​​or state, Indicates from the current reference time Beginning, moving towards the future A moment after that time;

[0114] S24, Calculate the transfer entropy (DTE) with delay compensation, including: candidate delay based on the output direction. Time-align the signals to calculate the delay-compensated transfer entropy from EEG to EMG in the outgoing direction. And candidate delays based on the incoming direction Time-align the signals to calculate the delay-compensated transfer entropy from EMG to EEG in the incoming direction. The transfer entropy of the outgoing direction EEG→EMG with delay compensation and the transfer entropy of the input direction EMG→EEG with delay compensation The calculation formulas are shown in (5) and (6) respectively:

[0115] (5);

[0116] (6);

[0117] In the formula, For sliding window index, and These are the channel indices for EEG and EMG signals, respectively. For delay Embedded vector of EMG signal after electromyography (EMG) For delay Embedded vectors of EEG signals after EEG; It is a scalar random variable, representing the value at the th . Within a sliding analysis window, data from electromyography channels... The signal at time point The instantaneous amplitude or state; It is a scalar random variable, representing the value at the th . Within a sliding analysis window, data from the EEG channel... The signal at the time point The instantaneous amplitude or state;

[0118] S25, verifying the statistical significance of the delay-compensated transfer entropy DTE based on the alternative data method, including: generating the alternative data by performing 10,000 phase randomizations on the original EEG-EMG signal, and calculating the delay-compensated transfer entropy DTE value of the alternative data. The t-test (p<0.05) was used to determine whether the delay-compensated transfer entropy (DTE) value of the original EEG-EMG signal was significantly higher than that of the delay-compensated transfer entropy (DTE) value of the surrogate data. The delay-compensated transfer entropy (DTE) value of the original EEG-EMG signal is significantly higher than that of the delay-compensated transfer entropy (DTE) value of the surrogate data. This indicates that there is a directed causal interaction between the channel pairs;

[0119] S26, Perform bidirectional delay optimization, including: designing optimization criteria to maximize the sum of significant DTEs across EEG-EMG channel pairs and sliding windows, and searching for the optimal delay in the outgoing direction after optimization. And optimized input direction optimal delay The optimized outgoing direction optimal delay And optimized input direction optimal delay The optimization formulas are shown in equations (7) and (8):

[0120] (7);

[0121] (8);

[0122] In the formula, and These are the channel indices for EEG and EMG signals, respectively. For sliding window index, This is the DTE change tracking function. The indicator function takes the value 1 when the delay-compensated transfer entropy (DTE) is significant, and 0 otherwise. , These are the estimated corticomuscular conduction times for the efferent pathway and the afferent pathway, respectively. It is a positive integer representing the summation symbol. The upper limit represents the total number of EEG signal channels in the system, channel index. Iterate from 1 to It covers all available EEG signal channels; It is a positive integer representing the summation symbol. The upper limit represents the total number of EMG signal channels in the system, channel index. Iterate from 1 to It covers all available electromyographic (EMG) signal channels; It is a positive integer representing the summation symbol. The upper limit represents the total number of sliding windows divided over the entire data time series; the sliding window index. Iterate from 1 to It covers all analysis time segments from the beginning to the end of the experiment.

[0123] S3. Based on the conduction time of the EEG→EMG outgoing path and the EMG→EEG incoming path, signal time alignment compensation is performed. The delay-compensated transfer entropy and the average delay-compensated transfer entropy are calculated. Based on the delay-compensated transfer entropy and the average delay-compensated transfer entropy, a directional delay-compensated directional corticomuscular connection CMC network is constructed. The directional delay-compensated directional corticomuscular connection CMC network is used to visually demonstrate the strength distribution of bidirectional functional connections between the cortex and muscles.

[0124] like Figure 4 As shown, in a preferred embodiment, S3 includes:

[0125] S31, based on the optimized outgoing direction optimal delay And optimized input direction optimal delay The EEG and EMG signals were time-aligned.

[0126] S32, Substitute the EEG and EMG signals aligned over the specified time into formulas (9) and (10) to calculate the delay-compensated transfer entropy of the EEG→EMG transmission direction. And the transfer entropy of the input direction EMG→EEG with delay compensation after delay compensation. ;

[0127] (9);

[0128] (10);

[0129] In the formula, For delay The subsequent EMG embedding vector, For delay The EEG embedding vector is ε; ε represents the time interval, which is set to 1 in this embodiment. Those skilled in the art can make appropriate settings as needed, all of which are within the protection scope of this invention. Indicates EMG signals at future moments The instantaneous amplitude or state; Indicates brainwave (EEG) signals at future moments The instantaneous amplitude or state; where: This is the current moment in the analysis, i.e., the reference point; and The optimal physiological conduction delay is estimated and obtained through a global search using the aforementioned optimization formulas (7) and (8). This composite time point represents the exact future moment when the electromyographic or electroencephalographic result signal of interest should appear after compensating for the estimated, pathway-specific physiological conduction time.

[0130] S33, Calculate the average delay-compensated transfer entropy, including: verifying the statistical significance of the delay-compensated transfer entropy in the same hemisphere and the same task phase based on the alternative data method to obtain all significant delay-compensated transfer entropies; averaging all significant delay-compensated transfer entropies to obtain the average delay-compensated transfer entropy. The average delay compensation transfer entropy As a quantitative index of CMC coupling strength, it is used to characterize the cortical-muscle functional coupling strength. The calculation formula is shown in Equation (11):

[0131] (11);

[0132] In the formula, These represent the selected EEG channel set, EMG channel set, and sliding window set, respectively, where N(·) is the cardinality of the sets. This represents the optimal delay for the corresponding direction;

[0133] S34, Construct a visualized, directionally delayed, compensated, directionally cortical-muscle connectivity (CMC) network, including: using EEG and EMG channels as nodes, and using the average delay to compensate for the transfer entropy. Using edge weights, a directional cortical muscle connectivity (CMC) network with directional delay compensation is constructed, wherein the EEG channel is the sensor motion cortical region.

[0134] In a preferred embodiment, 18 channels in the sensor's motion cortex region are selected as the EEG channels. The 18 channels include FC1, FC3, FC5, C1, C3, C5, CP1, CP3, and CP5 channels in the left hemisphere and FC2, FC4, FC6, C2, C4, C6, CP2, CP4, and CP6 channels in the right hemisphere.

[0135] As a preferred embodiment, the 18 channels are classified into target hemisphere channels and control hemisphere channels according to the target functional side of the tester, and are used only for the calculation of cortical muscle delay and CMC coupling strength.

[0136] S4. Extract assessment features and perform brain-muscle function change coupling analysis based on the assessment features; wherein, the assessment features are based on conduction delay, CMC coupling strength in hemispheric and task-specific stages, and hemispheric lateralization index. The data was extracted from four dimensions: the correlation between delayed-motion function behavioral index scores and other data.

[0137] like Figure 5 As shown, in a preferred embodiment, S4 includes:

[0138] S41, extracting cortical muscle conduction time features based on the conduction delay dimension, including: extracting the optimized outward direction optimal delay. And optimized input direction optimal delay As a characteristic of cortical-muscular conduction time, it reflects the conduction efficiency of the neural pathway;

[0139] S42, extract CMC coupling strength features based on the CMC coupling strength dimension of the hemisphere and task-specific phases, including: extracting the average delay compensation transfer entropy of the efferent and afferent directions of the target hemisphere and the control hemisphere in the resting phase S0, ramp-up phase S1 and maintenance phase S2 as the CMC coupling strength features, thereby reflecting the cortical muscle function interaction strength.

[0140] S43, based on the hemispheric lateralization index Dimensional extraction of the hemispherical lateralization index Features include: calculating the hemispheric lateralization index based on the average delay-compensated transfer entropy of the target side and the control side. This quantifies the degree of lateralization in motion control functions, specifically the hemispherical lateralization index. The calculation formula is shown in equation (12):

[0141] (12);

[0142] In the formula, The average delay compensation transfer entropy for the target hemisphere, To compensate for the average delay in the transfer entropy of the control hemisphere, The value range is [-1, 1], with positive values ​​indicating that the target hemisphere is dominant and negative values ​​indicating that the control hemisphere is dominant.

[0143] S44, extract behavioral and motor function scoring features based on the correlation dimension of the delayed-motor function behavioral index score, including: taking the RMSE of the grip strength tracking task as the behavioral feature of fine motor control ability assessment, and taking the behavioral and motor function scores as the motor function scoring features.

[0144] S45, Perform a coupling analysis of brain muscle function changes based on the assessment features.

[0145] Example 2

[0146] like Figure 6 As shown, this embodiment provides a brain-muscle function change coupling analysis system based on bidirectional delay compensation, used to implement the method of Embodiment 1, including:

[0147] Data acquisition module 101 is used to design experimental paradigms and perform signal acquisition and signal preprocessing based on the experimental paradigms;

[0148] The conduction time optimization module 102 is used to determine the bidirectional cortical muscle delay estimation algorithm based on the sliding window and the transfer entropy TE, and to quantify the conduction time of the EEG→EMG efferent pathway and the EMG→EEG infferent pathway of the cortical muscle of the test personnel based on the bidirectional cortical muscle delay estimation algorithm and the preprocessed signal.

[0149] The network construction and coupling strength delay compensation module 103 is used to perform signal time alignment compensation based on the transmission time of the EEG→EMG outgoing path and the EMG→EEG incoming path, calculate the delay-compensated transfer entropy and the average delay-compensated transfer entropy, and construct a directional delay-compensated directional corticomuscular connection CMC network based on the delay-compensated transfer entropy and the average delay-compensated transfer entropy. The directional delay-compensated directional corticomuscular connection CMC network is used to visually display the strength distribution of bidirectional functional connections between corticomuscular muscles.

[0150] The brain-muscle function change coupling analysis module 104 is used to extract assessment features and perform brain-muscle function change coupling analysis based on the assessment features; wherein, the assessment features are based on conduction delay, CMC coupling strength in hemispheric and task-specific stages, and hemispheric lateralization index. The data was extracted from four dimensions: the correlation between delayed-motion function behavioral index scores and other data.

[0151] Application scenarios:

[0152] 1. Motor function status assessment scenario: It is suitable for quantitative assessment of the cortical muscle functional connectivity status of healthy people and people with different motor functions. By analyzing characteristics such as efferent / apergent pathway conduction time, target side and control side DTE, lateralization index (LI), etc., it can accurately determine the cortical muscle interaction efficiency and motor control function level of the assessed object.

[0153] 2. Neurological function monitoring scenario: It can be integrated into a neurological function monitoring system to track the dynamic changes in brain-muscle coupling of the monitored subject in different activity states (such as resting and exercise training) in real time, providing data support for the early identification of cortical muscle dysfunction;

[0154] 3. Human-computer interaction system calibration scenario: In the research and application of human-computer interaction devices based on EEG-EMG signals (such as prosthetic control and motion assistive devices), this method can be used to accurately estimate brain-muscle conduction delay, optimize brain-muscle coupling signal processing algorithms, and improve the response accuracy and collaborative stability of human-computer interaction.

[0155] The present invention also provides a memory that stores multiple instructions for implementing the method as described in Embodiment 1.

[0156] like Figure 7 As shown, the present invention also provides an electronic device, including a processor 301 and a memory 302 connected to the processor 301. The memory 302 stores a plurality of instructions, which can be loaded and executed by the processor to enable the processor to perform methods as described in Embodiments 2 and 3.

[0157] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for coupled analysis of brain muscle function changes based on bidirectional delay compensation, characterized in that, include: S1. Design an experimental paradigm, and perform signal acquisition and signal preprocessing based on the experimental paradigm; S2, a bidirectional cortical muscle delay estimation algorithm is determined based on the sliding window and the transfer entropy TE, and the conduction time of the EEG→EMG efferent pathway and the EMG→EEG afferent pathway of the cortical muscle of the test personnel is quantified based on the bidirectional cortical muscle delay estimation algorithm and the preprocessed signal. S3. Based on the conduction time of the EEG→EMG outgoing path and the EMG→EEG incoming path, signal time alignment compensation is performed. The transfer entropy with delay compensation and the average delay compensation transfer entropy are calculated. Based on the transfer entropy with delay compensation and the average delay compensation transfer entropy, a directional delay compensation directional corticomuscular connection CMC network is constructed. The directional delay compensation directional corticomuscular connection CMC network is used to visually display the strength distribution of bidirectional functional connections between corticomuscular muscles. S4. Extract assessment features and perform brain-muscle function change coupling analysis based on the assessment features; wherein, the assessment features are based on conduction delay, CMC coupling strength in hemispheric and task-specific stages, and hemispheric lateralization index. The data was extracted from four dimensions: the correlation between delayed-motion function behavioral index scores and other data.

2. The method for coupled analysis of brain-muscle function changes based on bidirectional delay compensation according to claim 1, characterized in that, S1 includes: S11, Collect EEG signals, EMG signals, and behavioral data from the test subjects; S12, preprocess the EEG signals, EMG signals, and behavioral data to remove noise and artifacts and retain effective neural electrical signals.

3. The method for coupled analysis of brain-muscle function changes based on bidirectional delay compensation according to claim 2, characterized in that, The preprocessing includes: (1) Perform signal downsampling, including: downsampling the EEG signal from 2000Hz to 1000Hz, downsampling the EMG signal from 2000Hz to 1000Hz, and downsampling the grip strength signal in the behavioral data from 200Hz to 1000Hz; (2) Noise filtering process, including: extracting the beta band signal using a 13-30Hz bandpass filter; removing power frequency noise using a 50Hz notch filter; wherein the beta band signal is the dominant frequency band of cortical muscle functional connectivity; (3) Removal of artifacts, including: based on independent component analysis (ICA) combined with the ICLABEL plugin, identification and removal of electrooculography artifacts, electrocardiogram artifacts and electromyography interference artifacts in the EEG signal; (4) Perform signal rereference and segmentation: Rereference the EEG signal as the average reference of all channels; divide the grip force tracking period into resting phase S0, ramp-up phase S1 and maintenance phase S2 according to the contour of the grip force signal, and synchronously segment the EEG signal and the EMG signal according to the segmentation of the grip force tracking period. (5) Baseline correction: Using the phase signal of resting phase S0 as the baseline, the phase signals of ramp-up phase S1 and maintenance phase S2 are corrected.

4. The method for coupled analysis of brain-muscle function changes based on bidirectional delay compensation according to claim 3, characterized in that, S2 includes: S21, Set a sliding window, including: using an overlapping sliding window to divide the EEG signal and EMG signal after the synchronous segmentation; S22, perform phase space reconstruction, including: perform phase space reconstruction on the EEG signal and EMG signal in each sliding window to obtain equation (1) and equation (2): （1）； （2）； In the formula, This represents the time series of EEG signals. This represents the time series of electromyographic (EMG) signals. The associated time delay is determined by the correlation integral method. , All represent the embedding dimension, determined by the Cao criterion; S23, Calculate the transfer entropy TE, including: calculating the transfer entropy in the outgoing direction EEG→EMG respectively. The transfer entropy with the input direction EMG→EEG ,in Characterizing the predictive ability of EEG signals to EMG signals. Transition entropy characterizes the predictive ability of electromyography (EMG) signals on electroencephalography (EEG) signals. and transfer entropy The calculation formulas are shown in (3) and (4) respectively: （3）； （4）； In the formula, H(·) is the conditional entropy. Indicates time interval, They are respectively Embedding vectors of EEG and EMG signals at any given time; and Both are scalar random variables, representing two time series, EEG signals and EMG signals, respectively, at a specific future moment. The observed values ​​or state, Indicates from the current reference time Beginning, moving towards the future A moment after that time; S24, Calculate the transfer entropy (DTE) with delay compensation, including: candidate delay based on the output direction. Time-align the signals to calculate the delay-compensated transfer entropy from EEG to EMG in the outgoing direction. And candidate delays based on the incoming direction Time-align the signals to calculate the delay-compensated transfer entropy from EMG to EEG in the incoming direction. The transfer entropy of the outgoing direction EEG→EMG with delay compensation and the transfer entropy of the input direction EMG→EEG with delay compensation The calculation formulas are shown in (5) and (6) respectively: (5)； (6)； In the formula, For sliding window index, and These are the channel indices for EEG and EMG signals, respectively. For delay Embedded vector of EMG signal after electromyography (EMG) For delay Embedded vectors of EEG signals after EEG; It is a scalar random variable, representing the value at the th . Within a sliding analysis window, data from electromyography channels... The signal at time point The instantaneous amplitude or state; It is a scalar random variable, representing the value at the th . Within a sliding analysis window, data from the EEG channel... The signal at the time point The instantaneous amplitude or state; S25, verifying the statistical significance of the delay-compensated transfer entropy DTE based on the substitute data method includes: generating the substitute data by performing phase randomization on the original EEG-EMG signal, and calculating the delay-compensated transfer entropy DTE value of the substitute data. The t-test was used to determine whether the delay-compensated transfer entropy (DTE) value of the original EEG-EMG signal was significantly higher than the delay-compensated DTE value of the surrogate data. The delay-compensated transfer entropy (DTE) value of the original EEG-EMG signal is significantly higher than that of the delay-compensated transfer entropy (DTE) value of the surrogate data. This indicates that there is a directed causal interaction between the channel pairs; S26, Perform bidirectional delay optimization, including: designing optimization criteria to maximize the sum of significant DTEs across EEG-EMG channel pairs and sliding windows, and searching for the optimal delay in the outgoing direction after optimization. And optimized input direction optimal delay The optimized outgoing direction optimal delay And optimized input direction optimal delay The optimization formulas are shown in equations (7) and (8): (7)； (8)； In the formula, and These are the channel indices for EEG and EMG signals, respectively. For sliding window index, This is the DTE change tracking function. The indicator function takes the value 1 when the delay-compensated transfer entropy (DTE) is significant, and 0 otherwise. , These are the estimated corticomuscular conduction times for the efferent pathway and the afferent pathway, respectively. It is a positive integer representing the summation symbol. The upper limit represents the total number of EEG signal channels in the system, channel index. Iterate from 1 to It covers all available EEG signal channels; It is a positive integer representing the summation symbol. The upper limit represents the total number of EMG signal channels in the system, channel index. Iterate from 1 to It covers all available electromyographic (EMG) signal channels; It is a positive integer representing the summation symbol. The upper limit represents the total number of sliding windows divided over the entire data time series; the sliding window index. Iterate from 1 to It covers all analysis time segments from the beginning to the end of the experiment.

5. The method for coupled analysis of brain-muscle function changes based on bidirectional delay compensation according to claim 4, characterized in that, S3 includes: S31, based on the optimized outgoing direction optimal delay And optimized input direction optimal delay The EEG and EMG signals were time-aligned. S32, Substitute the EEG and EMG signals aligned over the specified time into formulas (9) and (10) to calculate the delay-compensated transfer entropy of the EEG→EMG transmission direction. And the transfer entropy of the input direction EMG→EEG with delay compensation after delay compensation. ; (9)； (10)； In the formula, For delay The subsequent EMG embedding vector, For delay The resulting EEG embedding vector; Indicates a time interval; Indicates EMG signals at future moments The instantaneous amplitude or state; Indicates brainwave (EEG) signals at future moments The instantaneous amplitude or state; where: This is the current moment in the analysis, i.e., the reference point; and The optimal physiological conduction delay is estimated and obtained through a global search using the aforementioned optimization formulas (7) and (8). This composite time point represents the exact future moment when the electromyographic or electroencephalographic result signal of interest should appear after compensating for the estimated, pathway-specific physiological conduction time. S33, Calculate the average delay-compensated transfer entropy, including: verifying the statistical significance of the delay-compensated transfer entropy in the same hemisphere and the same task phase based on the alternative data method to obtain all significant delay-compensated transfer entropies; averaging all significant delay-compensated transfer entropies to obtain the average delay-compensated transfer entropy. The average delay compensation transfer entropy As a quantitative index of CMC coupling strength, it is used to characterize the cortical-muscle functional coupling strength. The calculation formula is shown in Equation (11): (11)； In the formula, These represent the selected EEG channel set, EMG channel set, and sliding window set, respectively, where N(·) is the cardinality of the sets. This represents the optimal delay for the corresponding direction; S34, Construct a visualized, directionally delayed, compensated, directionally cortical-muscle connectivity (CMC) network, including: using EEG and EMG channels as nodes, and using the average delay to compensate for the transfer entropy. Using edge weights, a directional cortical muscle connectivity (CMC) network with directional delay compensation is constructed, wherein the EEG channel is the sensor motion cortical region.

6. The method for coupled analysis of brain-muscle function changes based on bidirectional delay compensation according to claim 5, characterized in that, Eighteen channels from the sensor's motion cortex region were selected as the EEG channels. These 18 channels include FC1, FC3, FC5, C1, C3, C5, CP1, CP3, and CP5 channels from the left hemisphere and FC2, FC4, FC6, C2, C4, C6, CP2, CP4, and CP6 channels from the right hemisphere. Based on the tester's target functional side, the 18 channels were categorized into target hemisphere channels and control hemisphere channels.

7. The method for coupled analysis of brain-muscle function changes based on bidirectional delay compensation according to claim 6, characterized in that, S4 includes: S41, extracting cortical muscle conduction time features based on the conduction delay dimension, including: extracting the optimized outward direction optimal delay. And optimized input direction optimal delay As a characteristic of cortical-muscular conduction time, it reflects the conduction efficiency of the neural pathway; S42, extract CMC coupling strength features based on the CMC coupling strength dimension of the hemisphere and task-specific phases, including: extracting the average delay compensation transfer entropy of the efferent and afferent directions of the target hemisphere and the control hemisphere in the resting phase S0, ramp-up phase S1 and maintenance phase S2 as the CMC coupling strength features, thereby reflecting the cortical muscle function interaction strength. S43, based on the hemispheric lateralization index Dimensional extraction of the hemispherical lateralization index Features include: calculating the hemispheric lateralization index based on the average delay-compensated transfer entropy of the target side and the control side. This quantifies the degree of lateralization in motion control functions, specifically the hemispherical lateralization index. The calculation formula is shown in equation (12): (12)； In the formula, The average delay compensation transfer entropy for the target hemisphere, To compensate for the average delay in the transfer entropy of the control hemisphere, The value range is [-1, 1], with positive values ​​indicating that the target hemisphere is dominant and negative values ​​indicating that the control hemisphere is dominant. S44, extract behavioral and motor function scoring features based on the correlation dimension of the delayed-motor function behavioral index score, including: taking the RMSE of the grip strength tracking task as the behavioral feature of fine motor control ability assessment, and taking the behavioral and motor function scores as the motor function scoring features. S45, Perform a coupling analysis of brain muscle function changes based on the assessment features.

8. A brain-muscle function change coupling analysis system based on bidirectional delay compensation, used to implement the method according to any one of claims 1-7, characterized in that, include: The data acquisition module (101) is used to design experimental paradigms and perform signal acquisition and signal preprocessing based on the experimental paradigms. The conduction time optimization module (102) is used to determine the bidirectional cortical muscle delay estimation algorithm based on the sliding window and the transfer entropy TE, and to quantify the conduction time of the EEG→EMG efferent pathway and the EMG→EEG infferent pathway of the cortical muscle of the test personnel based on the bidirectional cortical muscle delay estimation algorithm and the preprocessed signal. The network construction and coupling strength delay compensation module (103) is used to perform signal time alignment compensation based on the transmission time of the EEG→EMG outgoing path and the EMG→EEG incoming path, calculate the delay-compensated transfer entropy and the average delay-compensated transfer entropy, and construct a directional delay-compensated directional cortico-muscle connection CMC network based on the delay-compensated transfer entropy and the average delay-compensated transfer entropy. The directional delay-compensated directional cortico-muscle connection CMC network is used to visually display the strength distribution of bidirectional functional connections of cortico-muscle. The brain muscle function change coupling analysis module (104) is used to extract assessment features and perform brain muscle function change coupling analysis based on the assessment features; wherein, the assessment features are based on conduction delay, CMC coupling strength in hemispheric and task-specific stages, and hemispheric lateralization index. The data was extracted from four dimensions: the correlation between delayed-motion function behavioral index scores and other data.

9. An electronic device, characterized in that, It includes a processor and a memory, the memory storing multiple instructions, and the processor being used to read the instructions and execute the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a plurality of instructions, which can be read by a processor and executed according to any one of claims 1-7.

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