Multivariable brain and myoelectricity coupling analysis method and system based on structure sparse regularization

Abstract

The invention discloses a multivariable brain and myoelectricity coupling analysis method and system based on structure sparse regularization. The method comprises the steps that brain muscle signals are synchronously collected; performing data preprocessing on the collected brain muscle signals, and segmenting the preprocessed data into a plurality of different sample data segments; performing frequency domain analysis on the brain muscle signals in the sample data segments by using a fast Fourier algorithm to calculate respective autopower spectrum matrixes of the brain muscle signals and a cross spectrum matrix between the autopower spectrum matrixes; and based on the auto-power spectrum matrix and the cross-spectrum matrix, extracting cortical muscle coupling characteristics among the multi-lead brain muscle signals through a structure sparse typical coherence algorithm so as to reconstruct a brain topographic map related to the cortical muscle coupling characteristics. According to the method, under the conditions of low sample size and high noise level, cortical muscle coupling characteristics of healthy people and stroke patients can be recognized robustly, and cortical muscle coupling related brain topographic maps can be restored robustly.

Description

Technical Field

[0001] The present invention relates to the technical field of brain-myoelectric signal coupling analysis, and in particular to a multivariate brain-myoelectric coupling analysis method and system based on structured sparse regularization. Background Art

[0002] Rhythmic synchronization of neurons plays a vital role in various brain functions such as motor control, cognition and sensory processing. Effective estimation of the interaction of these neurons is an important area for studying the structural and functional remodeling of the brain in neurodegenerative diseases, and multimodal fusion analysis methods, combined with a variety of neurophysiological and imaging techniques, have become an advanced means of measuring neuronal connectivity. In recent years, the development of neurophysiological acquisition technology has made it possible to synchronously acquire electroencephalogram (EEG) and electromyogram (EMG), and functional corticomuscular coupling (FCMC) has been widely used to analyze the information exchange between the brain and muscles, which helps to understand the pathological mechanism of movement disorders after stroke.

[0003] Many multimodal fusion analysis methods for FCMC focus on the univariate coupling characteristics between individual sensors, often ignoring the spatiotemporal characteristics of central peripheral nerve information transmission. Several recent studies have proposed multivariate analysis methods based on canonical correlation analysis, but due to the high-dimensional characteristics of EMG data and the limitation of noise level, these multivariate methods based on spatial optimization often face the problem of overfitting in feature extraction.

[0004] In "Multiscale Canonical Coherence for Functional Corticomuscular Coupling Analysis", a multiscale canonical coherence algorithm was proposed, which aims to reduce the frequency band aliasing effect and avoid overfitting problems through a multiscale framework and multivariate method, thereby improving the performance of coupling feature extraction. However, the above research still has some shortcomings: First, principal component analysis (PCA) is required as a preprocessing step to achieve feature selection for each modality, but the optimal hyperparameters of PCA are difficult to determine. In addition, when processing high-dimensional, low signal-to-noise ratio EMG data, the algorithm is still prone to overfitting problems. Summary of the invention

[0005] The present invention aims to solve one of the technical problems in the related art at least to a certain extent.

[0006] To this end, the present invention proposes a multivariate EMG coupling analysis method based on structured sparse regularization to overcome the overfitting problem of existing FCMC feature extraction methods.

[0007] Another object of the present invention is to propose a multivariate brain-myoelectric coupling analysis system based on structured sparse regularization.

[0008] To achieve the above object, the present invention proposes a multivariate brain myoelectric coupling analysis method based on structured sparse regularization, comprising:

[0009] Synchronously collect brain-muscle signals;

[0010] Preprocessing the collected brain-muscle signals respectively, and dividing the preprocessed data into a number of different sample data segments;

[0011] The brain muscle signals in the sample data segment are analyzed in the frequency domain by using the fast Fourier algorithm to calculate the auto-power spectrum matrix of each brain muscle signal and the cross-spectrum matrix between them;

[0012] Based on the auto-power spectrum matrix and the cross-spectrum matrix, the corticomuscular coupling features between multi-lead brain-muscle signals are extracted through the structured sparse canonical coherence algorithm to reconstruct the brain topography related to the corticomuscular coupling features, including:

[0013] The cross-spectral matrix is ​​defined as:

[0014]

[0015] Where X∈C n×p The complex data matrix representing the EEG signal, Y∈C n×q represents the complex data matrix of the electromyographic signal, n represents the number of data segments, p represents the number of EEG leads, q represents the electromyographic lead data, and H represents the Hermitian transpose; the optimization problem of the typical coherence algorithm of structured sparsification is as follows:

[0016]

[0017] Among them, α∈R p×1 and β∈R q×1 are the real-valued projection vectors of EEG and EMG signals, respectively. φ is the auxiliary variable for solving the optimization problem. I is the identity matrix. λ1, λ2, γ1 and γ2 are the regularization hyperparameters of the optimization problem. L α and L β are the Laplace matrices of EEG signals and EMG signals, respectively, and are the difference between the angle matrix D and the adjacency matrix C;

[0018] To solve the optimization problem (2), the alternating optimization algorithm is used as follows: α and β are initialized as unit vectors, and φ is π / 4; the optimization parameters β and φ are fixed, and the original optimization problem is simplified to:

[0019]

[0020] Define the approximate point operator f as follows:

[0021]

[0022] For the unconstrained convex optimization problem (3), the gradient descent method is reformulated as a proximal regularized form:

[0023]

[0024] in, Yes X H The real part of Yexp(-iφ), P α is the 1-norm of the projection vector α, T α is the iteration step size of the projection vector α; the fast iterative threshold shrinkage algorithm (FISTA) is used to solve the LASSO problem (5);

[0025] When the optimization parameters α and φ are fixed, the original optimization problem is simplified to:

[0026]

[0027] At the same time, the iterative formula for optimizing the parameter β is:

[0028]

[0029] Among them, P β is the 1-norm of the projection vector β, T β is the iteration step size of the projection vector β;

[0030] When the optimization parameters α and β are fixed, the original optimization problem is simplified to:

[0031]

[0032] The Levenberg-Marquardt algorithm is used to find the optimal solution of the optimization parameter φ. After several iterations, the optimal solution of the original optimization problem (2) is obtained.

[0033] The multivariate brain myoelectric coupling analysis method based on structured sparse regularization in the embodiment of the present invention may also have the following additional technical features:

[0034] In one embodiment of the present invention, synchronously collecting brain muscle signals includes:

[0035] The EEG acquisition system and the wireless EMG acquisition system are used to synchronously acquire EEG signals and EMG signals, wherein the sampling frequency is 1000 Hz.

[0036] In one embodiment of the present invention, data preprocessing is performed on the collected brain muscle signals, including:

[0037] The EEG signal is referenced by common mean, and the EEG signal is zero-phase filtered by FIR filter to obtain the EEG signal after primary processing, and the 50Hz power frequency noise of the EEG signal after primary processing is removed by using EEGLAB standardized preprocessing framework, and the artifact interference of the EEG signal after power frequency noise removal is removed by independent component analysis method to obtain the preprocessed EEG signal;

[0038] The electromyographic signal is processed using a FIR filter of the same frequency band to obtain a preprocessed electromyographic signal.

[0039] In one embodiment of the present invention, the brain muscle signals in the sample data segment are analyzed in the frequency domain using the fast Fourier algorithm to calculate the auto-power spectrum matrix of each brain muscle signal and the cross-spectrum matrix therebetween, including:

[0040] The fast Fourier transform algorithm is used to calculate the EEG signal x∈R n×p and the electromyographic signal y∈R n×q The discrete Fourier spectrum X∈C n×p Y;

[0041] Obtain the EEG autopower spectrum matrix S XX =X H X, myoelectric autopower spectrum matrix S YY =Y H Y, brain and myoelectric cross spectrum matrix S XY =X H Y.

[0042] To achieve the above object, the present invention proposes a multivariate brain myoelectric coupling analysis system based on structured sparse regularization, comprising:

[0043] Brain-muscle signal acquisition module, used for synchronously acquiring brain-muscle signals;

[0044] A data preprocessing module is used to perform data preprocessing on the collected brain muscle signals and divide the preprocessed data into a number of different sample data segments;

[0045] A frequency domain analysis module is used to perform frequency domain analysis on the brain muscle signals in the sample data segment using a fast Fourier algorithm to calculate the auto-power spectrum matrix of each brain muscle signal and the cross-spectrum matrix between them;

[0046] The coherence analysis module is used to extract the cortical muscle coupling features between multi-lead brain muscle signals based on the auto-power spectrum matrix and the cross-spectral matrix through the structured sparse typical coherence algorithm to reconstruct the brain topography related to the cortical muscle coupling features; including:

[0047] The cross-spectral matrix is ​​defined as:

[0048]

[0049] Where X∈C n×p The complex data matrix representing the EEG signal, Y∈C n×q represents the complex data matrix of the electromyographic signal, n represents the number of data segments, p represents the number of EEG leads, q represents the electromyographic lead data, and H represents the Hermitian transpose; the optimization problem of the typical coherence algorithm of structured sparsification is as follows:

[0050]

[0051] Among them, α∈R p×1 and β∈R q×1 are the real-valued projection vectors of EEG and EMG signals, respectively. φ is the auxiliary variable for solving the optimization problem. I is the identity matrix. λ1, λ2, γ1 and γ2 are the regularization hyperparameters of the optimization problem. L α and L β are the Laplace matrices of EEG signals and EMG signals, respectively, and are the difference between the angle matrix D and the adjacency matrix C;

[0052] To solve the optimization problem (2), the alternating optimization algorithm is used as follows: α and β are initialized as unit vectors, and φ is π / 4; the optimization parameters β and φ are fixed, and the original optimization problem is simplified to:

[0053]

[0054] Define the approximate point operator f as follows:

[0055]

[0056] For the unconstrained convex optimization problem (3), the gradient descent method is reformulated as a proximal regularized form:

[0057]

[0058] in, Yes X H The real part of Yexp(-iφ), P α is the 1-norm of the projection vector α, T α is the iteration step size of the projection vector α; the fast iterative threshold shrinkage algorithm (FISTA) is used to solve the LASSO problem (5);

[0059] When the optimization parameters α and φ are fixed, the original optimization problem is simplified to:

[0060]

[0061] At the same time, the iterative formula for optimizing the parameter β is:

[0062]

[0063] Among them, P β is the 1-norm of the projection vector β, T β is the iteration step size of the projection vector β;

[0064] When the optimization parameters α and β are fixed, the original optimization problem is simplified to:

[0065]

[0066] The Levenberg-Marquardt algorithm is used to find the optimal solution of the optimization parameter φ. After several iterations, the optimal solution of the original optimization problem (2) is obtained.

[0067] The multivariate brain-myoelectric coupling analysis method and system based on structured sparse regularization in the embodiments of the present invention can robustly identify the corticomuscular coupling characteristics of healthy people and stroke patients under low sample size and high noise level, and robustly restore the brain topography related to corticomuscular coupling, and can serve as a potential biomarker for motor function recovery after stroke.

[0068] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0070] Figure 1 is a flowchart of a multivariate brain myoelectric coupling analysis method based on structured sparse regularization according to an embodiment of the present invention;

[0071] Figure 2 is an algorithm framework diagram of typical coherence of structure sparsification according to an embodiment of the present invention;

[0072] Figure 3 is a comparison diagram of feature extraction performance of a typical coherence algorithm for structure sparsification according to an embodiment of the present invention in a simulation data set;

[0073] Figure 4is a comparison diagram of feature extraction performance of a typical coherence algorithm for structural sparsification in a healthy person data set according to an embodiment of the present invention;

[0074] Figure 5 4 is a structural diagram of a multivariate brain-myoelectric coupling analysis system based on structured sparse regularization according to an embodiment of the present invention. DETAILED DESCRIPTION

[0075] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0076] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0077] The following describes a multivariate brain myoelectric coupling analysis method and system based on structured sparse regularization according to an embodiment of the present invention with reference to the accompanying drawings.

[0078] Figure 1 is a flow chart of a multivariate EMG coupling analysis method based on structured sparse regularization according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0079] S1, synchronous collection of brain-muscle signals.

[0080] Specifically, in the synchronous acquisition part of brain and myoelectric signals, the present invention can use the 64-channel Neuracle.W64 EEG acquisition system and the Neuracle.WM wireless EMG acquisition system to synchronously acquire EEG signals and EMG signals, and the sampling frequency is 1000Hz. The myoelectric equipment collects the activities of seven muscles, including the first dorsal interosseous, superficial flexor digitorum, extensor digitorum, radial flexor carpi, ulnar flexor carpi, ulnar extensor carpi, and brachioradialis, and the electrodes are placed on the muscle belly. During the experiment, the subjects were required to perform the grip control task with their right hands. Specifically, the subjects were required to control the force as close as possible to the preset target force, and the peak value of the preset target force was 20% of the maximum voluntary contraction. 3s after each set of tests started, the subjects performed a 6s static grip control task and finally rested for 3s.

[0081] It can be understood that by synchronously collecting EEG and EMG signals, the temporal consistency between the two is ensured, providing a reliable basis for subsequent coupling analysis.

[0082] It can be understood that the high sampling frequency and multi-channel setting ensure high resolution of the data, improve signal quality and reduce the influence of noise.

[0083] It is understandable that the design of the grip force control task of the present invention ensures the consistency and repeatability of the experimental conditions, making the results between different subjects comparable. The setting of the preset target force helps to standardize the experimental task, ensuring that each subject completes the task under similar conditions, thereby improving the effectiveness of data analysis.

[0084] In summary, the present invention achieves high-quality, synchronous brain and myoelectric signal acquisition by adopting advanced EEG and EMG acquisition systems and combining them with carefully designed experimental tasks. This technology not only improves data quality and experimental results, but also provides a solid foundation for in-depth research on brain-muscle coupling and the development of related applications.

[0085] S2, performing data preprocessing on the collected brain-muscle signals, and dividing the preprocessed data into a plurality of different sample data segments.

[0086] Specifically, in the preprocessing process of brain and myoelectric signals of the present invention, the EEG signal first takes a common average reference and uses a 4-60Hz FIR filter for zero-phase filtering. Subsequently, the EEGLAB standardized preprocessing framework (earlystage preprocessing, PREP) is used to remove 50Hz industrial frequency noise. For the EEG signal, the independent component analysis technology is used to remove the interference of artifacts such as electrooculography and electrocardiography to obtain the preprocessed EEG signal.

[0087] At the same time, the electromyographic signal is also subjected to FIR filtering in the same frequency band to obtain the preprocessed electromyographic signal.

[0088] The embodiment of the present invention ensures the purity and consistency of EEG signals and EMG signals in a wide frequency band through common average reference and frequency band filtering, providing high-quality basic data for subsequent processing. Each preprocessing step is designed to gradually optimize the signal quality, from removing unnecessary frequency components to eliminating power frequency noise of a specific frequency, and then to removing various artifacts, and finally obtaining a high-purity EEG signal. High-quality brain and myoelectric signals provide a solid foundation for advanced analysis such as frequency domain analysis, time-frequency analysis, and cortical muscle coupling feature extraction, which helps to conduct a deeper study of the relationship between cerebral cortical activity and muscle activity. It can be used to evaluate and optimize rehabilitation training programs to help patients recover motor function. It also provides valuable resources for the development of brain-computer interface (BCI) systems and promotes the advancement of human-computer interaction technology.

[0089] S3, using the fast Fourier algorithm to perform frequency domain analysis on the brain muscle signals in the sample data segment to calculate the auto-power spectrum matrix of each brain muscle signal and the cross spectrum matrix between them.

[0090] Specifically, the fast Fourier transform algorithm is used to calculate the EEG signal x∈R n×p and the electromyographic signal y∈R n×q The discrete Fourier spectrum X∈C n×p Y, and further obtain the EEG autopower spectrum matrix S XX =X H X, myoelectric autopower spectrum matrix S YY =Y H Y, brain and myoelectric cross spectrum matrix S XY =X H Y.

[0091] The FFT of the embodiment of the present invention converts the time domain signal into a frequency domain signal, reveals the component distribution of the signal at different frequencies, and improves the understanding of the time variation characteristics of the signal. The autopower spectrum matrix intuitively displays the energy distribution of each frequency component, reflects the intensity variation of the signal at different frequencies, and helps to identify key frequency bands. The cross-spectral matrix reveals the synchronization and coherence between EEG signals and EMG signals, and enhances the understanding of neural regulation mechanisms, especially in terms of motor control. High-quality frequency domain analysis provides a solid foundation for subsequent frequency domain analysis, time-frequency analysis, and cortical muscle coupling feature extraction, and enhances the depth and breadth of research.

[0092] S4, based on the auto-power spectrum matrix and the cross-spectrum matrix, and through the structured sparsification canonical coherence algorithm, the corticomuscular coupling features between multi-lead brain-muscle signals are extracted to reconstruct the brain topography related to the corticomuscular coupling features.

[0093] To avoid the overfitting problem of the optimization model, regularization techniques usually impose appropriate restrictions on the optimization variables to narrow the solution space of the optimal solution, thereby reducing the possibility of obtaining an overfitting solution. First, the cross-spectral block matrix obtained by the above calculation can be defined as:

[0094]

[0095] Where X∈C n×p The complex data matrix representing the EEG signal, Y∈C n×q represents the complex data matrix of the electromyographic signal, n represents the number of data segments, p represents the number of EEG leads, and q represents the electromyographic lead data. H represents the Hermitian transpose. Therefore, the optimization problem of the typical coherence algorithm for structured sparsification is as follows:

[0097]

[0098] Among them, α∈R p×1 and β∈R q×1are the real-valued projection vectors of EEG and EMG signals, respectively. φ is the auxiliary variable for solving the optimization problem. I is the identity matrix. λ1, λ2, γ1 and γ2 are the regularization hyperparameters of the optimization problem. L α and L β are the Laplace matrices of EEG signals and EMG signals respectively, which are the difference between the angle matrix D and the adjacency matrix C.

[0099] In order to solve the optimization problem (2), the alternating optimization algorithm is adopted as follows: First, α and β are initialized as unit vectors, and φ is π / 4; then, the optimization parameters β and φ are fixed, and the original optimization problem can be simplified to:

[0100]

[0101] Here, we define the approximate point operator f as follows:

[0102]

[0103] For the unconstrained convex optimization problem (3), the gradient descent method can be reformulated as the proximal regularization form

[0104]

[0105] in, Yes X H The real part of Yexp(-iφ), P α is the 1-norm of the projection vector α, T α is the iteration step size of the projection vector α. Therefore, the fast iterative threshold shrinkage algorithm (FISTA) can be used to solve the LASSO problem (5).

[0106] Similarly, when the optimization parameters α and φ are fixed, the original optimization problem can be simplified to:

[0107]

[0108] At the same time, the iterative formula for optimizing the parameter β is:

[0109]

[0110] Among them, P β is the 1-norm of the projection vector β, T β is the iteration step size of the projection vector β.

[0111] Furthermore, when the optimization parameters α and β are fixed, the original optimization problem can be simplified to:

[0112]

[0113] It is easy to prove that the optimization problem (8) can be simplified into the trigonometric function form of the optimization parameter φ, so the Levenberg-Marquardt algorithm can be used to solve the optimal solution of the optimization parameter φ. After several iterations, the optimal solution of the original optimization problem (2) can be obtained.

[0114] like Figure 2 As shown in the figure, this is the algorithm framework diagram of the typical coherence of structured sparsification. First, the EEG and EMG signals are obtained by synchronously collecting and preprocessing the EEG and EMG signals. Then, with maximizing the coherence as the optimization goal, the latent space representation of the EEG and EMG signals is calculated by solving the optimization problem, which is specifically reflected in the real projection vector α∈R p×1 and β∈R q×1 , and finally the brain and myoelectric fusion characteristics and the corresponding brain topology map are obtained.

[0115] like Figure 3 As shown, Figure 3 (A) Figure 3 (B) Figure 3 (C) Figure 3 (D) in the figure are comparison diagrams of feature extraction performance of the structured sparsified typical coherence algorithm in the present invention in the simulation data set. It can be observed that in the simulation data set, under different sample sizes and noise levels, the significant typical coherence calculated by the multivariate EMG coupling analysis method based on structured sparsified regularization proposed in the present invention is better than the previous multivariate method.

[0116] like Figure 4 As shown, Figure 4 (A) Figure 4 (B) in the figure are comparison diagrams of feature extraction performance of the structured sparsified typical coherence algorithm in the present invention in a healthy human dataset. It can be observed that in a healthy human dataset, the significant typical coherence calculated by the multivariate brain-myoelectric coupling analysis method based on structured sparsity regularization proposed in the present invention is significantly better than the previous multivariate method. At the same time, the method proposed in the present invention can more accurately extract the target brain areas related to corticospinal motor control by introducing sparsity and structured constraints, thereby generating a more centralized and interpretable brain topology map.

[0117] In summary, the beneficial effects of the embodiments of the present invention are:

[0118] 1) By introducing the structural sparsification regularization term, a bio-inspired optimization problem was constructed, combining feature selection with the model fitting process, effectively avoiding the overfitting problem that is prone to occur in traditional multivariate methods, and significantly improving the coupled feature detection and pattern restoration capabilities.

[0119] 2) The present invention proposes an alternating optimization algorithm to solve the above-mentioned optimization problem, taking into account the robustness and real-time performance of cortical muscle coupling feature extraction, and assisting in the design of a real-time neural feedback system.

[0120] According to the multivariate brain-myoelectric coupling analysis method based on structured sparse regularization in an embodiment of the present invention, the corticomuscular coupling characteristics of healthy people and stroke patients can be robustly identified under low sample size and high noise level, and the brain topography related to corticomuscular coupling can be robustly restored, thereby realizing accurate monitoring of the central peripheral neural circuit function of the subjects, and can serve as a potential biomarker for motor function recovery after stroke.

[0121] In order to implement the above embodiment, Figure 5 As shown, this embodiment also provides a multivariate brain myoelectric coupling analysis system 10 based on structured sparse regularization, including:

[0122] The brain-muscle signal acquisition module 100 is used to synchronously acquire brain-muscle signals;

[0123] The data preprocessing module 200 is used to perform data preprocessing on the collected brain muscle signals and divide the preprocessed data into a plurality of different sample data segments;

[0124] The frequency domain analysis module 300 is used to perform frequency domain analysis on the brain muscle signals in the sample data segment using a fast Fourier algorithm to calculate the auto-power spectrum matrix of each brain muscle signal and the cross-spectrum matrix between them;

[0125] The coherence analysis module 400 is used to extract the cortical muscle coupling features between the multi-lead brain muscle signals based on the auto-power spectrum matrix and the cross-spectral matrix and through the structured sparse typical coherence algorithm to reconstruct the brain topography related to the cortical muscle coupling features; including:

[0126] The cross-spectral matrix is ​​defined as:

[0127]

[0128] Where X∈C n×p The complex data matrix representing the EEG signal, Y∈C n×q represents the complex data matrix of the electromyographic signal, n represents the number of data segments, p represents the number of EEG leads, q represents the electromyographic lead data, and H represents the Hermitian transpose; the optimization problem of the typical coherence algorithm of structured sparsification is as follows:

[0129]

[0130] Among them, α∈R p×1 and β∈R q×1are the real-valued projection vectors of EEG and EMG signals, respectively. φ is the auxiliary variable for solving the optimization problem. I is the identity matrix. λ1, λ2, γ1 and γ2 are the regularization hyperparameters of the optimization problem. L α and L β are the Laplace matrices of EEG signals and EMG signals, respectively, and are the difference between the angle matrix D and the adjacency matrix C;

[0131] To solve the optimization problem (2), the alternating optimization algorithm is used as follows: α and β are initialized as unit vectors, and φ is π / 4; the optimization parameters β and φ are fixed, and the original optimization problem is simplified to:

[0132]

[0133] Define the approximate point operator f as follows:

[0134]

[0135] For the unconstrained convex optimization problem (3), the gradient descent method is reformulated as a proximal regularized form:

[0136]

[0137] in, Yes X H The real part of Yexp(-iφ), P α is the 1-norm of the projection vector α, T α is the iteration step size of the projection vector α; the fast iterative threshold shrinkage algorithm (FISTA) is used to solve the LASSO problem (5);

[0138] When the optimization parameters α and φ are fixed, the original optimization problem is simplified to:

[0139]

[0140] At the same time, the iterative formula for optimizing the parameter β is:

[0141]

[0142] Among them, P β is the 1-norm of the projection vector β, T β is the iteration step size of the projection vector β;

[0143] When the optimization parameters α and β are fixed, the original optimization problem is simplified to:

[0144]

[0145] The Levenberg-Marquardt algorithm is used to find the optimal solution of the optimization parameter φ. After several iterations, the optimal solution of the original optimization problem (2) is obtained.

[0146] Furthermore, the brain muscle signal acquisition module 100 is also used for:

[0147] The EEG acquisition system and the wireless EMG acquisition system are used to synchronously acquire EEG signals and EMG signals, wherein the sampling frequency is 1000 Hz.

[0148] Furthermore, the data preprocessing module 200 is also used for:

[0149] The EEG signal is referenced by common mean, and the EEG signal is zero-phase filtered by FIR filter to obtain the EEG signal after primary processing, and the 50Hz power frequency noise of the EEG signal after primary processing is removed by using EEGLAB standardized preprocessing framework, and the artifact interference of the EEG signal after power frequency noise removal is removed by independent component analysis method to obtain the preprocessed EEG signal;

[0150] The electromyographic signal is processed using a FIR filter of the same frequency band to obtain a preprocessed electromyographic signal.

[0151] Furthermore, the frequency domain analysis module 300 is also used for:

[0152] The fast Fourier transform algorithm is used to calculate the EEG signal x∈R n×p and the electromyographic signal y∈R n×q The discrete Fourier spectrum X∈C n×p Y;

[0153] Obtain the EEG autopower spectrum matrix S XX =X H X, myoelectric autopower spectrum matrix S YY =Y H Y, brain and myoelectric cross spectrum matrix S XY =X H Y.

[0154] According to the multivariate brain-myoelectric coupling analysis system based on structured sparse regularization according to the embodiment of the present invention, it is possible to robustly identify the corticomuscular coupling characteristics of healthy subjects and stroke patients and robustly restore the brain topography related to corticomuscular coupling at a low sample size and high noise level, thereby achieving accurate monitoring of the central peripheral neural circuit function of the subjects and serving as a potential biomarker for motor function recovery after stroke.

[0155] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0156] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

Claims

1. A multivariate brain myoelectric coupling analysis method based on structured sparse regularization, characterized in that: include: Synchronously collect brain-muscle signals; Preprocessing the collected brain-muscle signals respectively, and dividing the preprocessed data into a number of different sample data segments; The brain muscle signals in the sample data segment are analyzed in the frequency domain by using the fast Fourier algorithm to calculate the auto-power spectrum matrix of each brain muscle signal and the cross-spectrum matrix between them; Based on the auto-power spectrum matrix and the cross-spectrum matrix, the corticomuscular coupling features between multi-lead brain-muscle signals are extracted through the structured sparse canonical coherence algorithm to reconstruct the brain topography related to the corticomuscular coupling features; including: The cross-spectral matrix is ​​defined as: Where X∈C n×p The complex data matrix representing the EEG signal, Y∈C n×q represents the complex data matrix of the electromyographic signal, n represents the number of data segments, p represents the number of EEG leads, q represents the electromyographic lead data, and H represents the Hermitian transpose; the optimization problem of the typical coherence algorithm of structured sparsification is as follows: Among them, α∈R p×1 and β∈R q×1 are the real-valued projection vectors of EEG and EMG signals, respectively. φ is the auxiliary variable for solving the optimization problem. I is the identity matrix. λ1, λ2, γ1 and γ2 are the regularization hyperparameters of the optimization problem. L α and L β are the Laplace matrices of EEG signals and EMG signals, respectively, and are the difference between the angle matrix D and the adjacency matrix C; To solve the optimization problem (2), the alternating optimization algorithm is used as follows: α and β are initialized as unit vectors, and φ is π / 4; the optimization parameters β and φ are fixed, and the original optimization problem is simplified to: Define the approximate point operator f as follows: For the unconstrained convex optimization problem (3), the gradient descent method is reformulated as a proximal regularized form: in, Yes X H The real part of Yexp(-iφ), P α is the 1-norm of the projection vector α, T α is the iteration step size of the projection vector α; the fast iterative threshold shrinkage algorithm (FISTA) is used to solve the LASSO problem (5); When the optimization parameters α and φ are fixed, the original optimization problem is simplified to: At the same time, the iterative formula for optimizing the parameter β is: Among them, P β is the 1-norm of the projection vector β, T β is the iteration step size of the projection vector β; When the optimization parameters α and β are fixed, the original optimization problem is simplified to: The Levenberg-Marquardt algorithm is used to find the optimal solution of the optimization parameter φ. After several iterations, the optimal solution of the original optimization problem (2) is obtained.

2. The method according to claim 1, characterized in that The collected brain-muscle signals are preprocessed separately, including: The EEG signal is referenced by common mean, and the EEG signal is zero-phase filtered by FIR filter to obtain the EEG signal after primary processing, and the 50Hz power frequency noise of the EEG signal after primary processing is removed by using EEGLAB standardized preprocessing framework, and the artifact interference of the EEG signal after power frequency noise removal is removed by independent component analysis method to obtain the preprocessed EEG signal; The electromyographic signal is processed using a FIR filter of the same frequency band to obtain a preprocessed electromyographic signal.

3. The method according to claim 2, characterized in that The fast Fourier algorithm is used to perform frequency domain analysis on the brain muscle signals in the sample data segment to calculate the auto-power spectrum matrix of each brain muscle signal and the cross-spectrum matrix between them, including: The fast Fourier transform algorithm is used to calculate the EEG signal x∈R n×p and the electromyographic signal y∈R n×q The discrete Fourier spectrum X∈C n×p Y; Obtain the EEG autopower spectrum matrix S XX =X H X, myoelectric autopower spectrum matrix S YY =Y H Y, brain and myoelectric cross spectrum matrix S XY =X H Y.

4. A multivariate brain-myoelectric coupling analysis system based on structured sparse regularization, characterized in that: include: Brain-muscle signal acquisition module, used for synchronously acquiring brain-muscle signals; A data preprocessing module is used to perform data preprocessing on the collected brain muscle signals and divide the preprocessed data into a number of different sample data segments; A frequency domain analysis module is used to perform frequency domain analysis on the brain muscle signals in the sample data segment using a fast Fourier algorithm to calculate the auto-power spectrum matrix of each brain muscle signal and the cross-spectrum matrix between them; The coherence analysis module is used to extract the cortical muscle coupling features between multi-lead brain muscle signals based on the auto-power spectrum matrix and the cross-spectral matrix through the structured sparse typical coherence algorithm to reconstruct the brain topography related to the cortical muscle coupling features; including: The cross-spectral matrix is ​​defined as: Where X∈C n×p The complex data matrix representing the EEG signal, Y∈C n×q represents the complex data matrix of the electromyographic signal, n represents the number of data segments, p represents the number of EEG leads, q represents the electromyographic lead data, and H represents the Hermitian transpose; the optimization problem of the typical coherence algorithm of structured sparsification is as follows: Among them, α∈R p×1 and β∈R q×1 are the real-valued projection vectors of EEG and EMG signals, respectively. φ is the auxiliary variable for solving the optimization problem. I is the identity matrix. λ1, λ2, γ1 and γ2 are the regularization hyperparameters of the optimization problem. L α and L β are the Laplace matrices of EEG signals and EMG signals, respectively, and are the difference between the angle matrix D and the adjacency matrix C; To solve the optimization problem (2), the alternating optimization algorithm is used as follows: α and β are initialized as unit vectors, and φ is π / 4; the optimization parameters β and φ are fixed, and the original optimization problem is simplified to: Define the approximate point operator f as follows: For the unconstrained convex optimization problem (3), the gradient descent method is reformulated as a proximal regularized form: in, Yes X H The real part of Yexp(-iφ), P α is the 1-norm of the projection vector α, T α is the iteration step size of the projection vector α; the fast iterative threshold shrinkage algorithm (FISTA) is used to solve the LASSO problem (5); When the optimization parameters α and φ are fixed, the original optimization problem is simplified to: At the same time, the iterative formula for optimizing the parameter β is: Among them, P β is the 1-norm of the projection vector β, T β is the iteration step size of the projection vector β; When the optimization parameters α and β are fixed, the original optimization problem is simplified to: The Levenberg-Marquardt algorithm is used to find the optimal solution of the optimization parameter φ. After several iterations, the optimal solution of the original optimization problem (2) is obtained.

5. The system according to claim 4, characterized in that The data preprocessing module is also used to: The EEG signal is referenced by common mean, and the EEG signal is zero-phase filtered by FIR filter to obtain the EEG signal after primary processing, and the 50Hz power frequency noise of the EEG signal after primary processing is removed by using EEGLAB standardized preprocessing framework, and the artifact interference of the EEG signal after power frequency noise removal is removed by independent component analysis method to obtain the preprocessed EEG signal; The electromyographic signal is processed using a FIR filter of the same frequency band to obtain a preprocessed electromyographic signal.

6. The system according to claim 5, characterized in that The frequency domain analysis module is also used for: The fast Fourier transform algorithm is used to calculate the EEG signal x∈R n×p and the electromyographic signal y∈R n×q The discrete Fourier spectrum X∈C n×p Y; Obtain the EEG autopower spectrum matrix S XX =X H X, myoelectric autopower spectrum matrix S YY =Y H Y, brain and myoelectric cross spectrum matrix S XY =X H Y.

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