Phase amplitude coupling analysis method and device based on electroencephalogram and magnetoencephalogram, storage medium and terminal

Abstract

The invention provides a phase amplitude coupling analysis method and device based on an electroencephalogram and a magnetoencephalogram, a storage medium and a terminal, and the method comprises the steps: obtaining a multi-band signal corresponding to a to-be-analyzed brain signal; phase information of a first frequency interval signal and amplitude information of a second frequency interval signal in the multi-band signal are extracted as preset information; all preset information is extracted according to a time point sequence by using the sliding time windows to obtain phase information and amplitude information corresponding to the multiple time windows to serve as window results, and a covariance matrix corresponding to each time window is obtained based on the window results; based on a preset phase amplitude coupling algorithm, extracting phase amplitude coupling characteristics of all the covariance matrixes, and obtaining phase amplitude coupling characteristics corresponding to the time point; and inputting the phase amplitude coupling features corresponding to all the time points into a coupling feature standard database for comparison. The precision of the brain analysis result can be effectively improved.

Description

Technical Field

[0001] The present invention belongs to the field of electroencephalogram (EEG) technology, and relates to a phase-amplitude coupling analysis method based on an electroencephalogram (EEG) and a magnetoencephalogram (MEG), and in particular to a phase-amplitude coupling analysis method and device based on an EEG and a MEG, a storage medium, and a terminal. Background Art

[0002] Brain neural activity is the basis of cognition, emotion, and behavior, and its research is of great significance for revealing brain functions. Traditional electroencephalogram (EEG) and magnetoencephalogram (MEG) signal analysis methods mainly focus on the power spectrum characteristics of a single frequency or the extraction of event-related activities.

[0003] In the existing technology, EEG and MEG signal analysis methods are mainly concentrated in the following technical paths: First, power spectrum analysis, through Fourier transform or wavelet transform, extracts the power characteristics of a single frequency, such as alpha waves (8-12Hz), beta waves (13-30Hz), gamma waves (above 30Hz), etc., which are used to study the power changes in different brain states. Second, event-related potentials and event-related fields, use time-locked methods to extract EEG / MEG responses caused by specific stimuli (such as vision, hearing, and movement). Third, functional connectivity analysis, through coherence analysis, mutual information and other methods, evaluates the functional connectivity between different brain regions.

[0004] However, although the above methods have played an important role in brain signal analysis, they still have some limitations. On the one hand, the interaction between multi-band signals is ignored. Traditional power spectrum analysis only focuses on a single frequency component and cannot reveal how the low-frequency phase modulates the high-frequency amplitude. For example, before an epileptic seizure, low-frequency delta waves (1-4 Hz) may abnormally enhance the amplitude of gamma waves (30-100 Hz), leading to abnormal activity in the lesion area, and traditional analysis methods are difficult to capture this dynamic change. On the other hand, the exploration of synergy between brain regions is insufficient. Existing methods mainly focus on signal changes in single channels or local brain regions. The brain is a highly complex network. Information exchange and dynamic synergy across brain regions (such as cortical-cortical interaction or cortical-thalamic loops) are difficult to effectively characterize using traditional analysis methods.

[0005] Therefore, based on the above reasons, the existing technology for extracting features of brain signals lacks the mining of signal interactions and dynamic coordination, resulting in low accuracy of brain signal analysis results. Summary of the Invention

[0006] The purpose of the present invention is to provide a phase-amplitude coupling analysis method and device based on electroencephalography and magnetoencephalography, a storage medium and a terminal, which are used to solve the technical problem of poor feature extraction of brain signals in the prior art.

[0007] In a first aspect, the present invention provides a phase-amplitude coupling analysis method based on electroencephalography and magnetoencephalography, comprising:

[0008] Acquiring a multi-band signal corresponding to the brain signal to be analyzed, and extracting phase information of a first frequency interval signal and amplitude information of a second frequency interval signal from the multi-band signal as preset information;

[0009] Extracting all the preset information in time order using a sliding time window to obtain phase information and amplitude information corresponding to multiple time windows as window results, and obtaining a covariance matrix corresponding to each time window based on the window results;

[0010] Extracting phase-amplitude coupling features of all the covariance matrices based on a preset phase-amplitude coupling algorithm, and acquiring a phase-amplitude coupling feature corresponding to each time point based on the number of times each time point is counted by the sliding time window;

[0011] Inputting the phase-amplitude coupling features corresponding to all the time points into a coupling feature standard database for comparison to obtain a brain analysis result corresponding to the brain supplement signal to be analyzed;

[0012] The first frequency is lower than a preset frequency, and the second frequency is higher than a preset frequency.

[0013] In one embodiment of the present invention, extracting the preset information in time order using a sliding time window to obtain phase information and amplitude information corresponding to multiple time windows as window results includes:

[0014] Arrange all the preset information in time order to obtain the window size parameter and step size parameter of the sliding time window;

[0015] According to the window size parameter and the step size parameter, sequentially slide over all time points until the last time point to obtain all time windows;

[0016] Get the preset information within all time windows as the window result.

[0017] In one embodiment of the present invention, obtaining the phase-amplitude coupling feature corresponding to the preset time point based on the number of times the preset time point is counted by the sliding time window includes:

[0018] Filtering a time window containing a preset time point from all the time windows to serve as a target time window set corresponding to the preset time point;

[0019] Obtaining an average value of the phase-amplitude coupling characteristics corresponding to all time windows in the target time window set as the phase-amplitude coupling characteristic corresponding to the preset time point;

[0020] The preset time point is any time point.

[0021] In one embodiment of the present invention, a method for obtaining the covariance matrix corresponding to any time window includes a Gaussian mixture model.

[0022] In one embodiment of the present invention, the preset phase-amplitude coupling algorithm is a Fisher-Rao distance algorithm.

[0023] In one embodiment of the present invention,

[0024] Obtaining multi-band signals corresponding to the brain signals to be analyzed includes:

[0025] The 1 to 120 Hz signal in the brain supplement signal to be analyzed is obtained by wavelet transform as a corresponding multi-band signal, wherein the multi-band signal includes signals of delta band, theta band, alpha band, beta band and gamma band.

[0026] In one embodiment of the present invention,

[0027] The first frequency interval signal includes signals in the delta frequency band, theta frequency band, alpha frequency band and beta frequency band;

[0028] The second frequency interval signal includes a gamma frequency band signal.

[0029] In a second aspect, the present invention further provides a phase-amplitude coupling analysis device based on electroencephalography and magnetoencephalography, characterized in that it comprises:

[0030] a data extraction module, configured to obtain a multi-band signal corresponding to the brain signal to be analyzed, and extract phase information of a first frequency interval signal and amplitude information of a second frequency interval signal from the multi-band signal as preset information;

[0031] A sliding acquisition module is used to extract all the preset information in time order using a sliding time window to obtain phase information and amplitude information corresponding to multiple time windows as window results, and obtain a covariance matrix corresponding to each time window based on the window results;

[0032] A coupling algorithm module, configured to extract the phase-amplitude coupling features of all the covariance matrices based on a preset phase-amplitude coupling algorithm, and obtain the phase-amplitude coupling features corresponding to each time point based on the number of times each time point is counted by the sliding time window;

[0033] A result acquisition module is used to input the phase amplitude coupling features corresponding to all the time points into a coupling feature standard database for comparison to obtain a brain analysis result corresponding to the brain supplement signal to be analyzed;

[0034] The first frequency is lower than a preset frequency, and the second frequency is higher than a preset frequency. In a third aspect, the present invention further provides a storage medium storing a computer program, which, when executed by a processor, implements the phase-amplitude coupling analysis method based on electroencephalography and magnetoencephalography as described above.

[0035] In a fourth aspect, the present invention further provides a terminal, comprising a processor and a memory, wherein the memory is communicatively connected to the processor;

[0036] The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the terminal performs the phase-amplitude coupling analysis method based on electroencephalogram and magnetoencephalogram as described above.

[0037] As described above, the phase-amplitude coupling analysis method and device, storage medium, and terminal based on electroencephalography and magnetoencephalography according to the present invention have the following beneficial effects:

[0038] The present invention uses the phase information and amplitude information of multi-band signals as the data basis, and uses a sliding time window to capture the changing trend of phase amplitude to avoid information loss due to short-term signal fluctuations. The window result obtains the corresponding covariance matrix for phase-amplitude coupling feature extraction, eliminates the edge effect caused by window overlap according to the number of coverage, ensures the smoothness of the time series, and finally obtains the brain analysis results through the phase-amplitude coupling feature. The method of the present invention fully considers the interaction of signals in different frequency bands, and mines the information exchange and dynamic coordination across brain regions through sliding time windows and phase-amplitude coupling. Therefore, compared with the existing technology, the brain analysis results obtained using the method of the present invention are more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A flow chart of a phase-amplitude coupling analysis method based on electroencephalogram (EEG) and magnetoencephalogram (MEG) according to an embodiment of the present invention is shown.

[0040] Figure 2 A flow chart of extracting preset information in time sequence using a sliding time window to obtain phase information and amplitude information corresponding to multiple time windows as window results is shown in an embodiment of the present invention.

[0041] Figure 3A schematic diagram of a process for obtaining a phase-amplitude coupling feature corresponding to a preset time point based on the number of times the preset time point is counted by a sliding time window according to an embodiment of the present invention is shown.

[0042] Figure 4 A schematic structural diagram of a phase-amplitude coupling analysis device based on electroencephalography and magnetoencephalography according to an embodiment of the present invention is shown.

[0043] Figure 5 A schematic structural diagram of a terminal according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0044] The following describes the embodiments of the present invention through specific examples. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. The details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other unless they conflict.

[0045] The principles and implementation methods of the phase-amplitude coupling analysis method and device based on electroencephalogram and magnetoencephalogram, storage medium and terminal of this embodiment will be explained in detail below, so that those skilled in the art can understand the phase-amplitude coupling analysis method, device, storage medium and terminal based on electroencephalogram and magnetoencephalogram of this embodiment without creative work.

[0046] In order to solve the above technical problems existing in the prior art, an embodiment of the present invention provides a phase-amplitude coupling analysis method based on electroencephalogram (EEG) and magnetoencephalogram (MEG).

[0047] Figure 1 FIG2 shows a flow chart of a phase-amplitude coupling analysis method based on electroencephalogram and magnetoencephalogram according to an embodiment of the present invention, with reference to FIG2. Figure 1 As shown, the phase-amplitude coupling analysis method based on electroencephalogram (EEG) and magnetoencephalogram (MEG) according to an embodiment of the present invention mainly includes steps S100 to S400.

[0048] Step S100: Acquire a multi-band signal corresponding to the brain signal to be analyzed, and extract phase information of a first frequency interval signal and amplitude information of a second frequency interval signal in the multi-band signal as preset information.

[0049] Specifically, the brain supplement signal to be analyzed includes electroencephalogram data and magnetoencephalogram data, and the brain supplement signal is decomposed to obtain signals of different frequency bands as a multi-band signal. In an embodiment of the present invention, the multi-band signal is divided into two target frequency intervals: a first frequency interval and a second frequency interval. The first frequency interval signal is a signal in the first frequency interval, which is used to characterize low-frequency signals; the second frequency interval signal is a signal in the second frequency interval, which is used to characterize high-frequency signals. The phase information of the first frequency interval signal is extracted, and the amplitude information of the second frequency interval signal is extracted as preset information. Among them, the first frequency is less than the preset frequency, and the second frequency is greater than the preset frequency.

[0050] Optionally, obtaining a multi-band signal corresponding to the brain signal to be analyzed includes:

[0051] The 1 to 120 Hz signal in the brain supplement signal to be analyzed is obtained through wavelet transform as the corresponding multi-band signal, which includes the delta band, theta band, alpha band, beta band and gamma band signals. Specifically, the wavelet transform output formula is as follows:

[0052]

[0053] Among them, W f (a,b) is the wavelet transform of function f(t) at scale a and position b; a is the scale parameter; b is the translation parameter; ψ(t) is the wavelet function; ψ * (t) is the complex conjugate of the wavelet function.

[0054] Optionally, the first frequency interval signal includes signals of a delta frequency band, a theta frequency band, an alpha frequency band, and a beta frequency band; and the second frequency interval signal includes signals of a gamma frequency band.

[0055] Optionally, data preprocessing of the brain signals to be analyzed is also included. Specifically, the data preprocessing includes:

[0056] First, remove noise. That is, use a notch filter to filter the raw data at 50Hz and 100Hz to remove noise. The output formula of the zero-phase notch filter used is as follows:

[0057] Y1(Z)=H(Z)X(Z)

[0058] Where Y1(Z) is the output of the forward filter, H(Z) is the transfer parameter of the filter, and X(Z) is the Z-transform of the signal.

[0059]

[0060] where U(Z) is the Z transform of the flipped signal.

[0061] Y2(Z)=U(Z)H(Z)

[0062] Where Y2(Z) is the output of the reverse filtering.

[0063]

[0064] where Y(Z) is the output of the final zero-phase filter.

[0065]

[0066] Among them, H eq (Z) is the equivalent transfer function.

[0067] According to the above formula, the forward filtering, signal flipping, reverse filtering and reverse output of the signal are completed to realize the zero-phase filtering processing of the input signal.

[0068] Second, filtering. Specifically, a Butterworth filter is used to filter the signal from 1 to 120 Hz. The output formula of the Butterworth filter is as follows:

[0069]

[0070] Where H(s) is the transfer function, s is the complex frequency variable in Laplace transform, ω c is the cutoff frequency (rad / s), and N is the filter order.

[0071] Third, independent component analysis. The embodiment of the present invention uses independent component analysis to process data to remove eye movement and muscle movement artifacts. Independent component analysis is a computational method used to separate statistically independent subcomponents from multivariate signals. Its goal is to find an unmixing matrix W so that after linearly transforming the mixed signal X through W, independent components S can be obtained. The output formula is as follows:

[0072] X=AS

[0073] Where X is the observed mixed signal, A is the mixing matrix, and S is the independent component.

[0074] U=WX=WAS

[0075] Among them, U is the independent component estimate output by the algorithm, and W is the unmixing matrix.

[0076] Fourth, remove bad segments: remove data segments that still contain interference in the signal.

[0077] Fifth, extract epoch: extract the corresponding epoch according to the trigger of the experimental design.

[0078] Optionally, the method used to extract the phase information of the first frequency interval signal and the amplitude information of the second frequency interval signal in the multi-band signal is Hilbert transform. Specifically, the output formula of the Hilbert transform is as follows:

[0079]

[0080] in, represents the Hilbert transform of the function f(t); f(t) is the original real-valued function; τ is the integration variable.

[0081] The amplitude is calculated as follows:

[0082]

[0083] Wherein, F(t) is a complex signal.

[0084] The phase is calculated as follows:

[0085]

[0086] Among them, tan -1 Represents the inverse tangent function, which is used to calculate the argument of a complex number.

[0087] Step S200: Using a sliding time window to extract all preset information in time point order to obtain phase information and amplitude information corresponding to multiple time windows as window results, and obtaining a covariance matrix corresponding to each time window based on the window results.

[0088] Specifically, a sliding time window is set and slid sequentially along the time axis. Each time it stops, a new time window is acquired. Different time windows overlap. Phase and amplitude information within each time window is obtained as the corresponding window result. This can capture the changing trends of phase and amplitude at different times and avoid information loss caused by short-term signal fluctuations. Based on the window result, the corresponding covariance matrix is ​​obtained for subsequent phase-amplitude coupling feature extraction.

[0089] Optionally, extracting the preset information in time order using a sliding time window to obtain phase information and amplitude information corresponding to multiple time windows as window results includes:

[0090] Step S201: Arrange all preset information in order of time points, and obtain the window size parameter and step size parameter of the sliding time window.

[0091] The preset information is the phase information of the first frequency interval signal and the amplitude information of the second frequency interval signal in the extracted multi-band signal. All the preset information is arranged in the order of time points to form a time series matrix, as follows:

[0092] D=[d1,d2,...,d T ]

[0093] Where di represents the phase and amplitude information at the i-th time point, and T is the total number of time points. This embodiment also requires obtaining the window size parameter W and step size parameter S of the sliding time window. The window size parameter is the length of each time window. For example, the sliding time window captures 10 time points as a time window at a time. The step size parameter is the movement step of the sliding time window. For example, the sliding time window moves two time points after each time window is captured.

[0094] Step S202: According to the window size parameter and the step size parameter, slide sequentially over all time points until the last time point to obtain all time windows.

[0095] Starting from the first time point, a time window of size W is captured each time and slides S time points until the phase and amplitude information of all time points are covered. That is, the last time point of the last time window is dT.

[0096] Step S203: Obtain preset information within all time windows as the window result.

[0097] The method of obtaining the window result in the above embodiment can retain the dynamic information in the time dimension, avoid the limitation of analysis based on only a single time point, and make the subsequent phase amplitude coupling feature extraction more time-varying.

[0098] Optionally, the covariance matrix corresponding to any time window is obtained by using a Gaussian mixture model. Specifically, the Gaussian mixture model output formula is as follows:

[0099]

[0100] Where p(x|θ) is the probability density function of the observed data x given the model parameters θ; π k is the mixing coefficient of the kth Gaussian distribution, satisfying 0≤π k ≤1 and The mean is μ k , the variance is The probability density function of the Gaussian distribution.

[0101] Step S300: extracting the phase-amplitude coupling features of all covariance matrices based on a preset phase-amplitude coupling algorithm, and obtaining the phase-amplitude coupling features corresponding to each time point based on the number of times each time point is counted by the sliding time window.

[0102] A preset phase-amplitude coupling algorithm is used to extract phase-amplitude coupling features from the covariance matrix corresponding to the time window to obtain all phase-amplitude coupling features. Since each time point may be covered by multiple time windows, a weighted average is required based on the number of coverages to eliminate edge effects caused by window overlap and ensure time series smoothness.

[0103] Optionally, obtaining the phase-amplitude coupling feature corresponding to the preset time point based on the number of times the preset time point is counted by the sliding time window includes:

[0104] Step 301: Filtering time windows containing a preset time point from all time windows to serve as a target time window set corresponding to the preset time point;

[0105] Step 302: Obtain an average value of the phase-amplitude coupling characteristics corresponding to all time windows in the target time window set as the phase-amplitude coupling characteristic corresponding to the preset time point;

[0106] The preset time point is any time point, and the time window containing the preset time point is also the time window that has counted (or covered) the preset time point.

[0107] Specifically, refer to the following formula:

[0108]

[0109] Where PACWi is the phase-amplitude coupling feature corresponding to time window i, the numerator is the sum of the phase-amplitude coupling feature values ​​for all windows at that time point, and the denominator is the total number of times that time point is covered by the window. For example, if the window size parameter is 41 and the step size parameter is 2, and the calculation value is obtained for every other time point by sliding sequentially, the number of calculations corresponding to all time points in the time series matrix will be 1, 1, 2, 2, 3, 3, 4, 4, 5, 5... 19, 19, 20, 20, 21, 20, 21, 20, ..., 19, 19, 18, 18, ..., 1, 1. The window result corresponding to the first time window (containing 41 time points) is a1, the window result for the second time window is a2, and so on, resulting in all window results such as a3, a4, a5, a6, and a7. Since time windows overlap, each time point may be counted by multiple time windows (that is, covered). The first time point is counted by the first time window (that is, the time window set corresponding to the first time point is the first time window), the second time point is counted by the second time window, the third time point is counted by the first time window and the second time window, the fourth time point is counted by the first time window and the second time window, and the fifth time point is counted by the first time window, the second time window, and the third time window (that is, the time window set corresponding to the fifth time point is the first time window, the second time window, and the third time window). The purpose of the above smoothing processing is to eliminate the interference of overlapping sliding time windows, so that the calculated value of each time point is less affected by the window. Therefore, the phase-amplitude coupling feature corresponding to the third time point is (a1+a2) / 2, and the phase-amplitude coupling feature corresponding to the fifth time point is (a1+a2+a3) / 3.

[0110] Optionally, the preset phase-amplitude coupling algorithm is a Fisher-Rao distance algorithm. Specifically, the Fisher-Rao distance is a concept in information geometry used to measure the difference between two probability distributions, and its output format is based on the Fisher information matrix, as follows:

[0111]

[0112] Where I is the Fisher information matrix, i rs is an element in the matrix, θ r and θ s is an element in the parameter vector, E represents the expected value, and p is the probability density function.

[0113] The Fisher-Rao metric is defined as follows:

[0114] ds=∑∑i rs dθ r dθs

[0115] This formula measures the change in the probability density function p(x,θ) when the parameter θ is replaced by θ+δθ.

[0116] The geodesic distance induced by the Fisher-Rao metric is called the Rao distance, which provides a measure of the difference between two probability distributions. Specifically, the Fisher-Rao distance between two probability distributions is defined as:

[0117]

[0118] Here, θ1 and θ2 are parameter vectors of two different probability distributions.

[0119] Step S400: Inputting the phase-amplitude coupling features corresponding to all time points into a coupling feature standard database for comparison to obtain brain analysis results corresponding to the brain supplement signal to be analyzed.

[0120] In this embodiment, the coupling feature standard database stores multiple pieces of standard data, each of which includes phase-amplitude features corresponding to multiple time points and the corresponding brain analysis results. Specifically, a brain signal dataset is collected, and the phase-amplitude coupling features corresponding to all corresponding time points are obtained using the methods of the embodiments of the present invention as the features to be calibrated. These features to be calibrated are then labeled to obtain the corresponding brain analysis results, thereby constructing the coupling feature standard database.

[0121] Optionally, the phase-amplitude coupling analysis method based on electroencephalogram (EEG) and magnetoencephalogram (MEG) according to the embodiment of the present invention further includes step S500:

[0122] Step S500: Plotting based on the phase-amplitude coupling characteristics corresponding to all time points to obtain a coupling characteristic diagram within the time domain.

[0123] Specifically, the phase-amplitude coupling characteristics corresponding to each time point characterize the corresponding coupling strength, and then these time points and their corresponding phase-amplitude coupling characteristics are plotted in the time domain. The types of coupling feature maps include brain region coupling topology maps and time evolution curve maps. The brain region coupling topology map presents the delta-gamma and other frequency band coupling strengths of each brain region in the form of a heat map, etc., and marks abnormal super-threshold areas; the time evolution curve map can show the trend of the modulation intensity of the coupling of each frequency band over time, and annotate the characteristic fluctuations related to the disease. According to the drawing results, the specific situation of the coupling can be more intuitively displayed, so as to obtain the brain supplement analysis results. The embodiment of the present invention obtains a coupling feature map in the time domain by dynamically analyzing the interaction characteristics between multi-band signals, providing an efficient tool for the quantification of neural activity, which is suitable for various scenarios such as cognitive research, brain-computer interface development, and auxiliary assessment of neurological diseases.

[0124] The protection scope of the phase-amplitude coupling analysis method based on electroencephalography and magnetoencephalography in the embodiment of the present invention is not limited to the execution order of the steps listed in this embodiment. All solutions implemented by adding, reducing, or replacing steps in the prior art based on the principles of the present invention are included in the protection scope of the present invention.

[0125] The phase-amplitude coupling analysis method based on electroencephalography and magnetoencephalography in an embodiment of the present invention uses the phase information and amplitude information of multi-band signals as the data basis, and uses a sliding time window and phase-amplitude coupling feature extraction to obtain brain analysis results. It fully considers the interaction of signals in different frequency bands, and through sliding time windows and phase-amplitude coupling, it mines information exchange and dynamic coordination across brain regions. Therefore, compared with the existing technology, the brain analysis results obtained using the method of the present invention are more accurate.

[0126] In order to solve the above technical problems existing in the prior art, an embodiment of the present invention further provides a phase-amplitude coupling analysis device based on electroencephalogram (EEG) and magnetoencephalogram (MEG).

[0127] Figure 4 FIG2 shows a schematic diagram of the structure of the phase amplitude coupling analysis device based on electroencephalogram and magnetoencephalogram according to an embodiment of the present invention, with reference to FIG2. Figure 4 As shown, the phase-amplitude coupling analysis device based on electroencephalogram and magnetoencephalogram in an embodiment of the present invention includes:

[0128] a data extraction module, configured to obtain a multi-band signal corresponding to the brain signal to be analyzed, and extract phase information of a first frequency interval signal and amplitude information of a second frequency interval signal from the multi-band signal as preset information;

[0129] A sliding acquisition module is used to extract all the preset information in time order using a sliding time window to obtain phase information and amplitude information corresponding to multiple time windows as window results, and obtain a covariance matrix corresponding to each time window based on the window results;

[0130] A coupling algorithm module, configured to extract the phase-amplitude coupling features of all the covariance matrices based on a preset phase-amplitude coupling algorithm, and obtain the phase-amplitude coupling features corresponding to each time point based on the number of times each time point is counted by the sliding time window;

[0131] A result acquisition module is used to input the phase amplitude coupling features corresponding to all the time points into a coupling feature standard database for comparison to obtain a brain analysis result corresponding to the brain supplement signal to be analyzed;

[0132] The first frequency is lower than a preset frequency, and the second frequency is higher than a preset frequency.

[0133] The phase-amplitude coupling analysis device based on electroencephalography and magnetoencephalography in an embodiment of the present invention uses the phase information and amplitude information of multi-band signals as the data basis, and uses a sliding time window to capture the changing trend of phase amplitude, and then obtains brain analysis results through phase-amplitude coupling feature extraction. It fully considers the interaction of signals in different frequency bands, and mines information exchange and dynamic coordination across brain regions through sliding time windows and phase-amplitude coupling. Therefore, compared with the existing technology, the brain analysis results obtained using the method of the present invention are more accurate.

[0134] In order to solve the above-mentioned technical problems existing in the prior art, an embodiment of the present invention further provides a storage medium on which a computer program is stored, characterized in that when the program is executed by a processor, all steps of the phase-amplitude coupling analysis method based on electroencephalogram and magnetoencephalogram are implemented in the embodiment.

[0135] The specific steps of the phase-amplitude coupling analysis method based on electroencephalogram and magnetoencephalogram and the beneficial effects obtained by applying the readable storage medium provided by the embodiment of the present invention are the same as those in the above embodiment and will not be described in detail here.

[0136] Those skilled in the art will appreciate that all or part of the steps in the method for implementing the above embodiment can be performed by instructing a processor through a program, and the program can be stored in a computer-readable storage medium, which is a non-transitory medium, such as a random access memory, a read-only memory, a flash memory, a hard disk, a solid-state drive, a magnetic tape, a floppy disk, an optical disc, and any combination thereof. The above storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a digital video disc (DVD)), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0137] In order to solve the above technical problems existing in the prior art, an embodiment of the present invention further provides a terminal. Figure 5 The schematic diagram of the structure of the terminal according to the embodiment of the present invention is shown. Figure 5 As shown, the terminal of an embodiment of the present invention includes a processor and a memory, and the memory and the processor are communicatively connected; the memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory, so that the terminal performs all steps of the phase-amplitude coupling analysis method based on electroencephalography and magnetoencephalography in the above embodiment.

[0138] The specific steps of the phase-amplitude coupling analysis method based on electroencephalogram and magnetoencephalogram and the beneficial effects obtained by applying the terminal provided by the embodiment of the present invention are the same as those in the above embodiment and will not be described in detail here.

[0139] It should be noted that the memory may include random access memory (RAM) and may also include non-volatile memory (non-volatile memory), such as at least one disk storage. Similarly, the processor may also be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0140] Although the embodiments disclosed herein are as described above, the contents described herein are merely embodiments for facilitating understanding of the present invention and are not intended to limit the present invention. Any person skilled in the art may make any modifications and variations in the form and details of the embodiments without departing from the spirit and scope of the present invention. However, the scope of protection of the present invention shall remain subject to the scope defined by the appended claims.

Claims

1. A phase-amplitude coupling analysis method based on electroencephalography and magnetoencephalography, comprising: Acquiring a multi-band signal corresponding to the brain signal to be analyzed, and extracting phase information of a first frequency interval signal and amplitude information of a second frequency interval signal from the multi-band signal as preset information; Extracting all the preset information in time order using a sliding time window to obtain phase information and amplitude information corresponding to multiple time windows as window results, and obtaining a covariance matrix corresponding to each time window based on the window results; Extracting phase-amplitude coupling features of all the covariance matrices based on a preset phase-amplitude coupling algorithm, and acquiring a phase-amplitude coupling feature corresponding to each time point based on the number of times each time point is counted by the sliding time window; Inputting the phase-amplitude coupling features corresponding to all the time points into a coupling feature standard database for comparison to obtain a brain analysis result corresponding to the brain supplement signal to be analyzed; The first frequency is lower than a preset frequency, and the second frequency is higher than a preset frequency.

2. The phase-amplitude coupling analysis method based on electroencephalogram and magnetoencephalogram according to claim 1, characterized in that: Extracting the preset information in time order using a sliding time window to obtain phase information and amplitude information corresponding to multiple time windows as window results includes: Arrange all the preset information in time order to obtain the window size parameter and step size parameter of the sliding time window; According to the window size parameter and the step size parameter, sequentially slide over all time points until the last time point to obtain all time windows; Get the preset information within all time windows as the window result.

3. The phase-amplitude coupling analysis method based on electroencephalogram and magnetoencephalogram according to claim 1, characterized in that: Acquiring the phase-amplitude coupling feature corresponding to the preset time point based on the number of times the preset time point is counted by the sliding time window includes: Filtering a time window containing a preset time point from all the time windows to serve as a target time window set corresponding to the preset time point; Obtaining an average value of the phase-amplitude coupling characteristics corresponding to all time windows in the target time window set as the phase-amplitude coupling characteristic corresponding to the preset time point; The preset time point is any time point.

4. The phase-amplitude coupling analysis method based on electroencephalogram and magnetoencephalogram according to claim 1, characterized in that: Methods for obtaining the covariance matrix corresponding to any time window include Gaussian mixture models.

5. The phase-amplitude coupling analysis method based on electroencephalogram and magnetoencephalogram according to claim 1, characterized in that: The preset phase-amplitude coupling algorithm is the Fisher-Rao distance algorithm.

6. The phase-amplitude coupling analysis method based on electroencephalogram and magnetoencephalogram according to claim 1, characterized in that: Obtaining multi-band signals corresponding to the brain signals to be analyzed includes: The 1 to 120 Hz signal in the brain supplement signal to be analyzed is obtained by wavelet transform as a corresponding multi-band signal, wherein the multi-band signal includes signals of delta band, theta band, alpha band, beta band and gamma band.

7. The phase-amplitude coupling analysis method based on electroencephalogram and magnetoencephalogram according to claim 5, characterized in that: The first frequency interval signal includes signals in the delta frequency band, theta frequency band, alpha frequency band and beta frequency band; The second frequency interval signal includes a gamma frequency band signal.

8. A phase-amplitude coupling analysis device based on electroencephalogram and magnetoencephalogram, characterized in that: include: a data extraction module, configured to obtain a multi-band signal corresponding to the brain signal to be analyzed, and extract phase information of a first frequency interval signal and amplitude information of a second frequency interval signal from the multi-band signal as preset information; A sliding acquisition module is used to extract all the preset information in time order using a sliding time window to obtain phase information and amplitude information corresponding to multiple time windows as window results, and obtain a covariance matrix corresponding to each time window based on the window results; A coupling algorithm module, configured to extract the phase-amplitude coupling features of all the covariance matrices based on a preset phase-amplitude coupling algorithm, and obtain the phase-amplitude coupling features corresponding to each time point based on the number of times each time point is counted by the sliding time window; A result acquisition module is used to input the phase amplitude coupling features corresponding to all the time points into a coupling feature standard database for comparison to obtain a brain analysis result corresponding to the brain supplement signal to be analyzed; The first frequency is lower than a preset frequency, and the second frequency is higher than a preset frequency.

9. A storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the phase-amplitude coupling analysis method based on electroencephalography and magnetoencephalography according to any one of claims 1 to 7 is implemented.

10. A terminal, characterized in that: It includes a processor and a memory, and the memory is communicatively connected to the processor; the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the terminal performs the phase-amplitude coupling analysis method based on electroencephalography and magnetoencephalography as described in any one of claims 1 to 7.

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