Brain function network site effect weakening method based on wavelet transform

Abstract

The invention discloses a brain function network site effect weakening method based on wavelet transformation, and relates to the technical field of biomedical signal processing, sampling frequencies and electrode sites are unified, wavelet transformation is carried out on converted electroencephalogram signals, the electroencephalogram signals are decomposed into wavelet signals with different frequencies, the average amplitude of the wavelet signals with different frequencies is calculated, and the average amplitude of the wavelet signals with different frequencies is calculated. The average amplitude of the wavelet signals of all frequencies is modulated through a ComBat algorithm, the modulated wavelet signals of all frequencies form broad-spectrum electroencephalogram signals, signal components of all rhythm frequency bands in the broad-spectrum electroencephalogram signals are extracted, and the brain function network is constructed by calculating correlation between electrode sites. According to the method, the main site effect involved in the construction of the cross-site brain function network is effectively weakened or eliminated, and the site effect problems of inconsistent electrode site distribution, inconsistent amplitude-frequency response characteristics, sample collection, environment variables and the like are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of biomedical signal processing, in particular to a method for weakening brain function network site effects based on wavelet transform. Background Art

[0002] In multi-site brain functional connectivity research projects, site effects refer to systematic biases introduced by differences in the location or equipment where data is collected. This effect can significantly affect the comparability of data and the accuracy of analysis. Site effects may arise from multiple aspects, including hardware equipment, data collection protocols, subject group characteristics, and environmental factors.

[0003] In the study of brain functional connectivity based on EEG signals, different sites may use different models of EEG signal acquisition equipment, including different types of EEG caps, different models of EEG signal amplifiers, different numbers of electrodes, and electrode position distribution arranged according to different standards, which ultimately leads to differences in data quality and characteristics. The design parameters of different EEG signal amplifiers will lead to different signal amplitude-frequency responses. The circuit design of high-pass and low-pass filters determines the sensitivity to signals of different frequencies. The high-pass filter weakens low-frequency drift, and the low-pass filter suppresses high-frequency noise. The configuration of the filter and gain circuit and the parameters of the digital-to-analog converter jointly determine the amplitude-frequency response of the acquired signal. In addition, the subjects of different sites may differ in demographic variables (such as age, gender, race, cultural background) or health status. The acquisition environment (such as noise level, temperature, humidity) and the training level of operators may also introduce slight deviations. These site effects may lead to differences in functional connectivity patterns and mask actual physiological or cognitive signals; reduce the validity of statistical analysis, and differences between sites may lead to false positive or false negative results; the incomparability of data between different sites will affect the feasibility of multi-center studies and the universality of results. Summary of the invention

[0004] In order to overcome the defects in the above-mentioned prior art, the present invention provides a method for weakening the site effect of brain functional network based on wavelet transform, which solves the site effect problems such as inconsistent electrode site distribution, inconsistent amplitude-frequency response characteristics, and collection samples and environmental variables.

[0005] To achieve the above object, the present invention adopts the following technical solutions, including:

[0006] A method for weakening the site effect of brain functional network based on wavelet transform, comprising the following contents:

[0007] Selecting a standard sampling frequency to unify the EEG signals of each site to the standard sampling frequency; the EEG signals of the site are composed of signals of each electrode site collected by the site equipment;

[0008] Selecting a target electrode site, converting the electrode sites of each site to the target electrode site, thereby converting the EEG signals of each site to obtain a converted EEG signal;

[0009] For each site, the converted EEG signal of the site is subjected to wavelet transform and decomposed into wavelet signals of different frequencies; the wavelet signals of each frequency are subjected to ComBat modulation; the modulated wavelet signals of each frequency are constructed into a broad-spectrum EEG signal; thereby, the broad-spectrum EEG signal of each site is obtained for constructing a brain function network.

[0010] Preferably, a finite impulse response filter is used to unify the EEG signals of each site to a standard sampling frequency.

[0011] Preferably, the lowest sampling frequency among all site devices is selected as the standard sampling frequency, and the EEG signals with sampling frequencies higher than the standard sampling frequency are downsampled to the standard sampling frequency.

[0012] Preferably, the electrode sites of the sites are interpolated using spherical interpolation method, and the target electrode sites are inserted, so as to convert the EEG signals of each site to obtain the converted EEG signals, which are specifically shown as follows:

[0013] S T =M·S o

[0014]

[0015] Among them, S T S is the EEG signal converted by the site to be converted; o is the original EEG signal of the site to be converted; M is the conversion matrix; the element m in the conversion matrix to represents the mapping relationship between the target electrode site t and the electrode site o in the site to be converted; θ to is the spherical angle between the target electrode site t and the electrode site o in the site to be converted; P n (·) is the n-th order Legendre polynomial, where N is the order.

[0016] Preferably, an electrode site of one of the sites is selected as a target electrode site, the selected site is recorded as the target site, the remaining sites are recorded as sites to be converted, and the electrode sites of the sites to be converted are recorded as electrode sites to be converted; the electrode sites to be converted are converted to the target electrode sites, thereby converting the EEG signals of the sites to be converted to obtain converted EEG signals; specifically as shown below:

[0017] The electrode sites to be converted are standardized to the spatial scale of the target site, and the calculation formula is:

[0018]

[0019] Among them, the superscript o represents the coordinate system of the site to be converted, that is, the coordinate system to be converted, and the superscript t represents the coordinate system of the target site, that is, the target coordinate system; o ,y o 、z o is the coordinate of the electrode site to be converted in the coordinate system to be converted; t ,y t 、z t is the coordinate of the electrode site to be converted in the target coordinate system;

[0020] For the site to be converted or the target site, the subscript c represents the center point of all electrodes in the site corresponding to each coordinate axis, i.e., the center of the site. is the center coordinate of the target site, is the center coordinate of the site to be converted, calculated as the mean value of the coordinates of all electrode sites in the site, N t is the number of electrodes at the target site, N o is the number of electrodes at the site to be converted;

[0021] The subscript + indicates the electrode site with the largest distance from the center of the station in the positive direction of the corresponding coordinate axis, and the subscript - indicates the electrode site with the largest distance from the center of the station in the negative direction of the corresponding coordinate axis. are the coordinates of the electrode sites in the target site with the largest projection distance from the center of the target site along the positive direction of the x, y, and z axes, are the coordinates of the electrode sites in the target site with the largest negative distance from the center of the target site along the x, y, and z axes, respectively. are the coordinates of the electrode sites with the largest projection distance from the center of the target site along the positive direction of the x, y, and z axes in the site to be converted, They are the coordinates of each electrode site in the site to be converted that has the largest projection distance from the center of the target site along the negative direction of the x, y, and z axes.

[0022] The standardized electrode sites to be converted are interpolated using the spherical interpolation method and inserted into the target electrode sites, so as to convert the EEG signals of the sites to be converted and obtain the converted EEG signals, as shown below:

[0023] S T =M·S o

[0024]

[0025] Among them, S T S is the EEG signal converted by the site to be converted; o is the original EEG signal of the site to be converted; M is the conversion matrix; the element m in the conversion matrixto represents the mapping relationship between the target electrode site t and the electrode site o in the site to be converted; θ to is the spherical angle between the target electrode site t and the electrode site o in the site to be converted; P n (·) is the n-th order Legendre polynomial, where N is the order.

[0026] Preferably, for each site, the converted EEG signal of the site is subjected to wavelet transform to be decomposed into wavelet signals of different frequencies. After Hilbert transform, the average amplitude f of the wavelet signals of each frequency is obtained, and the calculation formula is:

[0027]

[0028] Among them, H(s) l is the value of the wavelet signal s at the sampling point l after Hilbert transform, L is the signal length;

[0029] At the same electrode site at each site, the average amplitude f of the wavelet signal of the same frequency of all subjects is obtained, and the average amplitude f is modulated by ComBat to obtain the modulated wavelet signals of each frequency. The calculation formula is:

[0030]

[0031] in, is the modulated amplitude of the wavelet signal of frequency k of subject j at site i; f ijk is the amplitude of the wavelet signal of frequency k of subject j at site i; X ij is the covariate matrix, carrying the subject's physical information, and are the empirical Bayes estimated parameters of the regression model, and is the coefficient of the deviation term;

[0032] Preferably, the following content is also included: reconstructing the modulated wavelet signals of each frequency into Z rhythmic frequency bands, wherein the frequency range of the first rhythmic frequency band K1 is [0, k1], the frequency range of the second rhythmic frequency band K2 is (k1, k2], ..., the Zth rhythmic frequency band K Z The frequency range is (k Z-1 ,k Z ],0 <k1<k2<...<k Z-1 <k Z ;

[0033]

[0034] in, is the reconstructed signal of the zth rhythm frequency band, f k and are the average amplitudes of the wavelet signal of frequency k before and after modulation, c h,k is the component coefficient at level h and frequency k during wavelet packet decomposition, φ h,k (t) is the corresponding wavelet basis function, K z is the zth rhythm frequency band, z=1,2,...,Z.

[0035] Preferably, for each rhythm frequency band, for the reconstructed signals of any two electrode sites a and b at each site, the correlation corr between the two electrode sites a and b is calculated. ab , the calculation formula is:

[0036]

[0037] Among them, s a 、s b is the reconstructed signal of the two electrode sites a and b, They are s a 、s b The mean of

[0038] Based on the correlation between any two electrode sites a and b, the brain functional network of each site in each rhythm frequency band is obtained. ab If it is greater than the set threshold, it means that the two electrode sites a and b are related, and a and b are connected into an edge.

[0039] The present invention also provides an electronic device, which includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned method for weakening brain function network site effects based on wavelet transform is implemented.

[0040] The present invention also provides a computer program product, which includes a computer program / instruction, and when the computer program / instruction is executed by a processor, the method for weakening the brain function network site effect based on wavelet transform is implemented.

[0041] The advantages of the present invention are:

[0042] (1) The present invention proposes a site effect weakening method, which effectively weakens or eliminates the main site effects involved in the construction of cross-site brain functional networks through spherical interpolation, wavelet signal decomposition and reconstruction, ComBat signal modulation and other means, and solves the site effect problems such as inconsistent electrode site distribution, inconsistent amplitude-frequency response characteristics, and sample collection and environmental variables.

[0043] (2) The present invention provides a systematic method flow that can effectively weaken or eliminate the main site effects involved in the construction of cross-site brain functional networks. These site effects include but are not limited to differences in hardware equipment, data collection protocols, subject group characteristics, and environmental factors.

[0044] (3) By unifying the sampling rate, standardizing the electrode sites, modulating the signal amplitude and other steps, the present invention significantly improves the comparability of data between different sites and ensures the reliability and universality of the multi-center research results.

[0045] (4) Under the premise of ensuring signal quality, the present invention adopts advanced signal processing technologies, such as wavelet transform and ComBat algorithm, to ensure the consistency of amplitude-frequency response of EEG signals and reduce signal distortion caused by differences in equipment and environment.

[0046] (5) By standardizing the research subjects and collection environments at different sites, the present invention effectively addresses the differences in brain functional connectivity caused by demographic variables (such as age, gender, and cultural background) and environmental variables (such as noise level and temperature).

[0047] (6) The method of the present invention can reduce the occurrence of false positive and false negative results, improve the effectiveness and accuracy of statistical analysis, and thus more accurately reflect actual physiological or cognitive signals.

[0048] (7) By providing a unified signal processing method, the present invention improves the feasibility of multicenter research, promotes the integration and analysis of cross-site data, and enhances the versatility and application value of research results. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 The figure is a flow chart of the method of the present invention.

[0050] Figure 2 Schematic diagram of the distribution of EEG cap electrode sites.

[0051] Figure 3 Schematic diagram of the comparison of signal amplitude-frequency response before and after modulation.

[0052] Figure 4 Schematic diagram comparing the effects of brain functional network sites in each rhythm frequency band before and after modulation. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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 are within the scope of protection of the present invention.

[0054] Depend on Figure 1 As shown, this embodiment provides a method for weakening brain function network site effect based on wavelet transform, comprising the following steps:

[0055] S1, unification of sampling frequency: select a standard sampling frequency and unify the EEG signals of each site to the standard sampling frequency; the EEG signals of the site are composed of signals of each electrode site collected by the site equipment.

[0056] In this embodiment, the lowest sampling frequency among all site devices is selected as the standard sampling frequency, and the EEG signals with sampling frequencies higher than the standard sampling frequency are downsampled to the standard sampling frequency. Finite impulse response filters are used to unify the EEG signals of each site to the standard sampling frequency.

[0057] The EEG signal of the site is composed of the signals of each electrode site collected by the site equipment (EEG cap);

[0058] S2, electrode site unification: select the target electrode site, convert the electrode sites of each site to the target electrode site, thereby converting the EEG signals of each site to obtain the converted EEG signals, thereby unifying the data collected by the EEG caps with inconsistent electrode sites into data of the same electrode site.

[0059] S3, wavelet transform decomposition: perform wavelet transform on the converted EEG signal of the site, decompose it into wavelet signals of different frequencies, and calculate the average amplitude of the wavelet signal of each frequency.

[0060] S4, ComBat modulation: The average amplitude of the wavelet signal at each frequency is modulated using the ComBat algorithm.

[0061] S5, reconstruction of broad-spectrum EEG signals: The modulated wavelet signals of each frequency are used to construct broad-spectrum EEG signals.

[0062] S6, constructing a brain functional network: extracting the signal components of each rhythmic frequency band in the broad-spectrum EEG signal, and constructing a brain functional network by calculating the correlation between the signal components of each rhythmic frequency band between electrode sites.

[0063] S7, brain functional network edge modulation: all edges in the brain functional network are modulated using the ComBat algorithm.

[0064] This embodiment uses EEG data from three sites with an age range of 20-30 years old in the closed-eye resting state, where: Site 1 contains 147 people, with a male-female ratio of 15:34, an average age of 25.5 years old, a device sampling frequency of 1000 Hz, and 64 effective electrodes; Site 2 contains 97 people, with a male-female ratio of 49:48, an average age of 22.7 years old, a device sampling frequency of 1000 Hz, and 62 effective electrodes; Site 3 contains 132 people, with a male-female ratio of 7:5, an average age of 23.4 years old, a device sampling frequency of 512 Hz, and 64 effective electrodes. Three different types of EEG amplifier devices were used at the three sites. The electrode site distribution is as follows: Figure 2 As shown, the signal duration is uniformly 30s.

[0065] In this embodiment, the standard sampling frequency is selected as 512 Hz, and the EEG signals of all subjects in site 1 and site 22 are downsampled to 512 Hz. The filter involved is a finite impulse response low-pass filter with a filter length of 101 (order of 100), a cutoff frequency of 256 Hz, and a Nyquist frequency of 500 Hz.

[0066] In this embodiment, the electrode sites of site 1 are selected as the target electrode sites, site 1 is the target site, the electrode sites of site 1 are the target electrode sites, and sites 2 and 3 are sites to be converted. The electrode sites of sites 2 and 3, i.e., the converted electrode sites, are standardized to the spatial scale of site 1, and the calculation formula is:

[0067]

[0068]

[0069] Among them, the superscript o represents the coordinate system of the site to be converted, that is, the coordinate system to be converted, and the superscript t represents the coordinate system of the target site, that is, the target coordinate system; o ,y o 、z o is the coordinate of the electrode site to be converted in the coordinate system to be converted; t ,y t 、z t is the coordinate of the electrode site to be converted in the target coordinate system;

[0070] For the site to be converted or the target site, the subscript c represents the center point of all electrodes in the site corresponding to each coordinate axis, i.e., the center of the site. is the center coordinate of the target site, is the center coordinate of the site to be converted, calculated as the mean value of the coordinates of all electrode sites in the site, N t is the number of electrodes at the target site, N o is the number of electrodes at the site to be converted;

[0071] The subscript + indicates the electrode site with the largest distance from the center of the station in the positive direction of the corresponding coordinate axis, and the subscript - indicates the electrode site with the largest distance from the center of the station in the negative direction of the corresponding coordinate axis. are the coordinates of the electrode sites in the target site with the largest projection distance from the center of the target site along the positive direction of the x, y, and z axes, are the coordinates of the electrode sites in the target site with the largest negative distance from the center of the target site along the x, y, and z axes, respectively. are the coordinates of the electrode sites with the largest projection distance from the center of the target site along the positive direction of the x, y, and z axes in the site to be converted, They are the coordinates of each electrode site in the site to be converted that has the largest projection distance from the center of the target site along the negative direction of the x, y, and z axes.

[0072] The standardized electrode sites to be converted are interpolated using the spherical interpolation method and inserted into the target electrode sites, so as to convert the EEG signals of the sites to be converted and obtain the converted EEG signals, as shown below:

[0073] S T =M·S o

[0074]

[0075] Among them, S T S is the EEG signal converted by the site to be converted; o is the original EEG signal of the site to be converted; M is the conversion matrix; the element m in the conversion matrix to represents the mapping relationship between the target electrode site t and the electrode site o in the site to be converted; θ to is the spherical angle between the target electrode site t and the electrode site o in the site to be converted; P n (·) is the n-th order Legendre polynomial, N is the order, N=50.

[0076] The signal of each electrode site of each subject is subjected to discrete wavelet packet transform (WPT). In this embodiment, Daubechies 4 (db4) wavelet is used to decompose the target signal in the 1-50 Hz frequency band into 50 wavelet signals, and the frequency interval between each wavelet signal is 1 Hz.

[0077] After Hilbert Transform, the average amplitude f of the wavelet signal of each frequency is obtained, and the calculation formula is:

[0078]

[0079] Among them, H(s) l is the value of the wavelet signal s at the sampling point l after Hilbert transform, L is the signal length;

[0080] At the same electrode site at each site, the average amplitude f of the wavelet signal of the same frequency of all subjects is obtained, and the average amplitude f is modulated by ComBat to obtain the modulated wavelet signals of each frequency. The calculation formula is:

[0081]

[0082] in, is the modulated amplitude of the wavelet signal of frequency k of subject j at site i; f ijk is the amplitude of the wavelet signal of frequency k of subject j at site i; X ij is the covariate matrix, carrying the subject's physical information, and are the empirical Bayes estimated parameters of the regression model, and is the coefficient of the deviation term;

[0083] After modulation, the signal of each subject at each site was reconstructed into five rhythm frequency bands: the frequency range of the first rhythm frequency band K1 was [0, 4 Hz], the frequency range of the second rhythm frequency band K2 was (4 Hz, 8 Hz], the frequency range of the third rhythm frequency band K3 was (8 Hz, 13 Hz], the frequency range of the fourth rhythm frequency band K4 was (13 Hz, 30 Hz], and the frequency range of the fifth rhythm frequency band K6 was (30 Hz, 50 Hz], and the calculation formula was:

[0084]

[0085] in, is the reconstructed signal of the zth rhythm frequency band, f k and are the average amplitudes of the wavelet signal of frequency k before and after modulation, c h,j is the component coefficient at level h and frequency k during wavelet packet decomposition, φ h,k (t) is the corresponding wavelet basis function, K z is the zth rhythm frequency band, z=1,2,...,5.

[0086] like Figure 3 As shown, before reconstruction (ref. Figure 3 The signal amplitude-frequency response of site 2 is significantly different from that of the other two sites. After reconstruction (ref. Figure 3The difference in the amplitude-frequency response of the signals at the three sites is significantly reduced, and they are highly consistent. This result shows that this method effectively weakens the site effect in the amplitude-frequency response of the signals.

[0087] For each rhythm band, the correlation corr is calculated for the reconstructed signals of any two electrode sites a and b for each subject at each site. ab , the calculation formula is:

[0088]

[0089] Among them, s a 、s b is the reconstructed signal of the two electrode sites a and b, They are s a 、s b The mean of .

[0090] Based on the correlation between any two electrode sites a and b, a brain functional network with a specific rhythm frequency based on the correlation is obtained. ab If it is greater than the set threshold, it means that the two electrode sites a and b are related, and a and b are connected into an edge.

[0091] The signal correlation between each pair of electrode sites of all subjects at the three sites was normalized and corrected, using the same statistical correction method used for the signal amplitude-frequency characteristics. The modulation method of ComBat (using subject age and gender as covariates) was also used here. The calculation formula is:

[0092]

[0093] in, is the modulated v-th element of the upper triangle of the connectivity matrix for site i and subject j, is the vth element of the upper triangle of the connectivity matrix for site i and subject j, X ij is the covariate matrix, and are the empirical Bayes estimated parameters of the regression model, and is the coefficient of the bias term to be eliminated.

[0094] like Figure 4As shown, 10 heat maps represent the comparison of the results in five frequency bands before and after modulation, where each heat map contains 64 rows and 64 columns, each row and column corresponds to a signal channel, and each value is the Kruskal–Wallis test result p value of the correlation between the two electrode signals represented by the row and column among the subjects. Note that the values ​​on the diagonal of the heat map are based on the correlation between each signal channel and itself, and the correlation coefficient is always equal to 1, so it has no statistical significance. Figure 4 The null hypothesis of this test is that the correlations from the three sites are identically distributed across subjects, i.e., p < 0.05 indicates that the input distributions are different. Figure 4 In the 3D images, before ComBat modulation, there were a lot of different correlation distributions in each frequency band, but after modulation, for all five rhythm frequency bands, there was no significant difference in the correlation distribution between any electrode sites of all subjects among the three sites. This result shows that this method effectively weakens the site effect in the construction of brain functional networks.

[0095] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for weakening brain function network site effect based on wavelet transform, characterized in that: Includes the following: Selecting a standard sampling frequency to unify the EEG signals of each site to the standard sampling frequency; the EEG signals of the site are composed of signals of each electrode site collected by the site equipment; Selecting a target electrode site, converting the electrode sites of each site to the target electrode site, thereby converting the EEG signals of each site to obtain a converted EEG signal; For each site, the converted EEG signal of the site is subjected to wavelet transform and decomposed into wavelet signals of different frequencies; ComBat modulation is performed on the wavelet signals of each frequency; The modulated wavelet signals of each frequency are used to form a broad-spectrum EEG signal; thus, the broad-spectrum EEG signal of each site is obtained and used to construct a brain function network.

2. The method for weakening brain function network site effect based on wavelet transform according to claim 1, characterized in that: Finite impulse response filters were used to unify the EEG signals from each site to a standard sampling frequency.

3. A method for weakening brain function network site effect based on wavelet transform according to claim 1 or 2, characterized in that: The lowest sampling frequency among all site devices is selected as the standard sampling frequency, and the EEG signals with sampling frequencies higher than the standard sampling frequency are downsampled to the standard sampling frequency.

4. The method for weakening brain function network site effect based on wavelet transform according to claim 1, characterized in that: The electrode sites of the sites are interpolated using the spherical interpolation method, and the target electrode sites are inserted to convert the EEG signals of each site to obtain the converted EEG signals, as shown below: S T =M·S o Among them, S T S is the EEG signal converted by the site to be converted; o is the original EEG signal of the site to be converted; M is the conversion matrix; the element m in the conversion matrix to represents the mapping relationship between the target electrode site t and the electrode site o in the site to be converted; θ to is the spherical angle between the target electrode site t and the electrode site o in the site to be converted; P n (·) is the n-th order Legendre polynomial, where N is the order.

5. The method for weakening brain function network site effect based on wavelet transform according to claim 1, characterized in that: The electrode site of one of the sites is selected as the target electrode site, the selected site is recorded as the target site, the remaining sites are recorded as sites to be converted, and the electrode sites of the sites to be converted are recorded as electrode sites to be converted; the electrode sites to be converted are converted to the target electrode sites, so as to convert the EEG signals of the sites to be converted to obtain the converted EEG signals; the details are as follows: The electrode sites to be converted are standardized to the spatial scale of the target site, and the calculation formula is: Among them, the superscript o represents the coordinate system of the site to be converted, that is, the coordinate system to be converted, and the superscript t represents the coordinate system of the target site, that is, the target coordinate system; o ,y o 、z o is the coordinate of the electrode site to be converted in the coordinate system to be converted; t ,y t 、z t is the coordinate of the electrode site to be converted in the target coordinate system; For the site to be converted or the target site, the subscript c represents the center point of all electrodes in the site corresponding to each coordinate axis, i.e., the center of the site. is the center coordinate of the target site, is the center coordinate of the site to be converted, calculated as the mean value of the coordinates of all electrode sites in the site, N t is the number of electrodes at the target site, N o is the number of electrodes at the site to be converted; The subscript + indicates the electrode site with the largest distance from the center of the station in the positive direction of the corresponding coordinate axis, and the subscript - indicates the electrode site with the largest distance from the center of the station in the negative direction of the corresponding coordinate axis. are the coordinates of the electrode sites in the target site with the largest projection distance from the center of the target site along the positive direction of the x, y, and z axes, are the coordinates of the electrode sites in the target site with the largest negative distance from the center of the target site along the x, y, and z axes, respectively. are the coordinates of the electrode sites with the largest projection distance from the center of the target site along the positive direction of the x, y, and z axes in the site to be converted, They are the coordinates of each electrode site in the site to be converted that has the largest projection distance from the center of the target site along the negative direction of the x, y, and z axes. The standardized electrode sites to be converted are interpolated using the spherical interpolation method and inserted into the target electrode sites, so as to convert the EEG signals of the sites to be converted and obtain the converted EEG signals, as shown below: S T =M·S o Among them, S T S is the EEG signal converted by the site to be converted; o is the original EEG signal of the site to be converted; M is the conversion matrix; the element m in the conversion matrix to represents the mapping relationship between the target electrode site t and the electrode site o in the site to be converted; θ to is the spherical angle between the target electrode site t and the electrode site o in the site to be converted; P n (·) is the n-th order Legendre polynomial, where N is the order.

6. The method for weakening brain function network site effect based on wavelet transform according to claim 1, characterized in that: For each site, the converted EEG signal of the site is subjected to wavelet transform and decomposed into wavelet signals of different frequencies. After Hilbert transform, the average amplitude f of the wavelet signals of each frequency is obtained, and the calculation formula is: Among them, H(s) l is the value of the wavelet signal s at the sampling point l after Hilbert transform, L is the signal length; At the same electrode site at each site, the average amplitude f of the wavelet signal of the same frequency of all subjects is obtained, and the average amplitude f is modulated by ComBat to obtain the modulated wavelet signals of each frequency. The calculation formula is: in, is the modulated amplitude of the wavelet signal of frequency k of subject j at site i; f ijk is the amplitude of the wavelet signal of frequency k of subject j at site i; X ij is the covariate matrix, carrying the subject's physical information, and are the empirical Bayes estimated parameters of the regression model, and is the coefficient of the deviation term.

7. A method for weakening brain function network site effect based on wavelet transform according to claim 1 or 6, characterized in that: The method also includes the following steps: reconstructing the modulated wavelet signals of each frequency into Z rhythmic frequency bands, wherein the frequency range of the first rhythmic frequency band K1 is [0, k1], the frequency range of the second rhythmic frequency band K2 is (k1, k2], ..., the frequency range of the Zth rhythmic frequency band K Z The frequency range is (k Z-1 ,k Z ],0 <k1<k2<...<k Z-1 <k Z ; in, is the reconstructed signal of the zth rhythm frequency band, f k and are the average amplitudes of the wavelet signal of frequency k before and after modulation, c h,k is the component coefficient at level h and frequency k during wavelet packet decomposition, φ h,k (t) is the corresponding wavelet basis function, K z is the zth rhythm frequency band, z=1,2,...,Z.

8. The method for weakening brain function network site effect based on wavelet transform according to claim 7, characterized in that: For each rhythm frequency band, for the reconstructed signals of any two electrode sites a and b at each site, calculate the correlation corr between the two electrode sites a and b. ab , the calculation formula is: Among them, s a 、s b is the reconstructed signal of the two electrode sites a and b, They are s a 、s b The mean of Based on the correlation between any two electrode sites a and b, the brain functional network of each site in each rhythm frequency band is obtained. ab If it is greater than the set threshold, it means that the two electrode sites a and b are related, and a and b are connected into an edge.

9. An electronic device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a method for weakening brain function network site effects based on wavelet transform as described in any one of claims 1 to 8.

10. A computer program product, characterized in that It includes a computer program / instruction, which, when executed by a processor, implements a method for weakening brain function network site effects based on wavelet transform as described in any one of claims 1 to 8.

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