CN113974650A

CN113974650A - Electroencephalogram network function analysis method and device, electronic equipment and storage medium

Info

Publication number: CN113974650A
Application number: CN202110728554.3A
Authority: CN
Inventors: 梁九兴; 邱小凡; 欧阳中越; 郑皓月; 翁旭初
Original assignee: South China Normal University
Current assignee: South China Normal University
Priority date: 2021-06-29
Filing date: 2021-06-29
Publication date: 2022-01-28
Anticipated expiration: 2041-06-29
Also published as: CN113974650B

Abstract

The embodiment of the application discloses a method and a device for analyzing electroencephalogram network functions, electronic equipment and a storage medium, wherein the method comprises the following steps: performing signal sampling based on a plurality of sampling points to obtain an original electroencephalogram signal set; preprocessing an original electroencephalogram signal set to obtain an electroencephalogram signal set; calculating according to electroencephalogram signals corresponding to two sampling points in the plurality of sampling points to obtain an imaginary part cross frequency phase-locked value set; constructing a multivariate linear regression model of the electroencephalogram signals according to the electroencephalogram signal set; calculating the row-column standardized directed coherence corresponding to the two sampling points according to a multivariate linear regression model to obtain a row-column standardized directed coherence set; and determining the functional characteristic information of the electroencephalogram network according to the imaginary part cross frequency phase-lock value set and/or the row-column standardized directed coherent set. The electroencephalogram network function can be accurately and comprehensively analyzed based on the two optimized electroencephalogram function characteristic values and the calculation method thereof.

Description

Electroencephalogram network function analysis method and device, electronic equipment and storage medium

Technical Field

The present invention relates to the field of signal analysis, and in particular, to a method and an apparatus for analyzing brain electrical network functions, an electronic device, and a storage medium.

Background

The brain network is a network mode formed by integrating cortical areas with different brain spatial positions through structural or functional connection and is formed by nodes and connection among the nodes. The connection of nodes is divided into structural connection and functional connection, wherein the structural connection is based on brain structure, and the functional connection is based on functional activity. In functional connection analysis, functional connections are further subdivided into undirected connections and directed connections. MEG, EEG, SEEG, BOLD signals can all be used to analyze functional connections.

Analyzing the functional connections of the brain electricity, including interpreting brain-region interactions from an electrophysiological perspective, can help to interpret cognitive phenomena. For EEG signals, the analysis function connection direction firstly needs to construct an autoregressive model, the existing signal is estimated through a connection matrix between the past signal of a certain node and different nodes, and the residual error of an estimated value and a real value can be used for measuring a causal relationship between the nodes. The existing normalization method is to perform normalization on rows or columns of the coefficient matrix, that is, the relative size of the metric values in a certain row or a certain column in the matrix cannot be compared across rows or columns. The analysis of the functional connection strength can be realized by a frequency domain coherence method, but the existing phase synchronization is influenced by a volume conduction effect, and the real part of a phase-locked value may show false correlation and interfere the analysis result of the functional connection strength.

Disclosure of Invention

Aiming at the defects in the prior art, the embodiment of the disclosure provides an electroencephalogram network function analysis method, which can accurately and comprehensively analyze electroencephalogram network functions based on two optimized electroencephalogram function characteristic values and calculation methods thereof.

The embodiment of the application provides an electroencephalogram network function analysis method, which comprises the following steps: performing signal sampling based on a plurality of sampling points to obtain an original electroencephalogram signal set; preprocessing an original electroencephalogram signal set to obtain an electroencephalogram signal set; the electroencephalogram signals in the electroencephalogram signal set correspond to the sampling points one by one; calculating according to electroencephalogram signals corresponding to two sampling points in the plurality of sampling points to obtain an imaginary part cross frequency phase-locked value set; constructing a multivariate linear regression model of the electroencephalogram signals according to the electroencephalogram signal set; calculating the row-column standardized directed coherence corresponding to the two sampling points according to a multivariate linear regression model to obtain a row-column standardized directed coherence set; and determining functional characteristic information of the electroencephalogram network according to the imaginary part cross frequency phase-locked value set and/or the row-column standardized directional coherent set, wherein the functional characteristic information comprises functional connection information among brain area nodes in the electroencephalogram network and/or functional weight information of the brain area nodes.

In an optional embodiment, the electroencephalogram signals in the electroencephalogram signal set are filtered to obtain filtered target frequency band signals; performing Hilbert conversion on the target frequency band signal by using a first calculation formula to obtain a Hilbert-converted target frequency band signal; calculating the target frequency band signal after Hilbert conversion by using a second calculation formula to obtain an analysis signal; calculating the analysis signal by using a third calculation formula to obtain the instantaneous phase of the analysis signal; calculating the instantaneous phase by using a fourth calculation formula to obtain an imaginary part cross frequency phase-locked value set;

the first calculation formula is as follows:

wherein, x (t) is a target frequency band signal corresponding to any sampling point, and P.V. is a Cauchy main value;

the second calculation formula is as follows:

the third calculation formula is as follows:

the fourth calculation formula is as follows:

wherein,

for sampling points m and n at l₀To l₁Imaginary cross-frequency phase-locked value of l₀And l₁Two frequency of interest values (FOI); im is an imaginary value; n is the total number of sampling points; e is a natural base number; phi is a_k(m) is the instantaneous phase of node m at the kth sample point.

In an alternative embodiment, the formula of the multiple autoregressive model is as follows:

wherein A is_kIs a model coefficient matrix of NxN, where N is the number of sampling points; x is the number of_tThe method comprises the steps of (1) acquiring an electroencephalogram signal set in the form of a time sequence matrix of N rows; e.g. of the type_tIs a residual matrix of N rows.

In an alternative embodiment, a fifth calculation formula is used for performing fourier transform on the multiple autoregressive model to obtain an autoregressive model in a frequency domain; calculating by using a sixth calculation formula based on an autoregressive model in a frequency domain to obtain a row-column standardized directed coherent set of two sampling points;

the fifth calculation formula is as follows:

A(v)x_v＝e_v

wherein,

wherein A (v) is a three-dimensional model coefficient matrix, and v is frequency; i is an imaginary unit; Δ t is the time interval between adjacent sampling points; e is a natural base number; e.g. of the type_vIs a residual error matrix;

the sixth formula is as follows:

wherein,

and normalizing the row and column of the two sampling points to have directional coherence, wherein the direction is from the sampling point m to the sampling point n.

In an alternative embodiment, the brain electrical network comprises nodes and edges; the nodes of the electroencephalogram network represent brain area nodes corresponding to the sampling points; the edges of the brain electrical network represent functional connection information between brain area nodes.

In an optional embodiment, if the imaginary part cross frequency phase-locked value of two of the multiple sampling points is greater than or equal to the imaginary part cross frequency phase-locked value threshold, and the magnitude of the row-column normalized directional coherence of the two sampling points is greater than or equal to the row-column normalized directional coherence threshold, it is determined that functional connection information exists between brain nodes corresponding to the two sampling points of the multiple sampling points.

In an alternative embodiment, the functional connection information includes functional connection strength information; and if the functional connection information exists between the brain area nodes corresponding to two sampling points in the plurality of sampling points, determining the functional connection strength information between the two brain area nodes corresponding to the two sampling points in the brain electric network according to the imaginary part cross frequency phase-locked values and/or the row and column standardization directed coherence of the two sampling points.

In an alternative embodiment, the function connection information further includes function connection direction information; and if the functional connection information exists between the brain area nodes corresponding to two sampling points in the plurality of sampling points, determining the functional connection direction information between the brain area nodes corresponding to the two sampling points in the electroencephalogram network according to the row-column standardized directional coherent direction of the two sampling points.

In an optional embodiment, if functional connection information exists between brain area nodes corresponding to two sampling points in the plurality of sampling points, the functional weight information of the brain area nodes in the electroencephalogram network is determined according to the imaginary part cross frequency phase-locked values and the row and column standardization directed coherence of the two sampling points.

In an optional embodiment, artifact components of an original electroencephalogram signal set are removed through independent component analysis to obtain an artifact-removed signal set, wherein the artifact components refer to physiological interference signals; and carrying out high-pass filtering and notch filtering on the artifact-removed signal set to obtain an electroencephalogram signal set.

Correspondingly, this application embodiment provides an electroencephalogram network function analysis device, and the device includes:

the sampling module is used for sampling signals based on a plurality of sampling points to obtain an original electroencephalogram signal set; preprocessing an original electroencephalogram signal set to obtain an electroencephalogram signal set; the electroencephalogram signals in the electroencephalogram signal set correspond to the sampling points one by one;

the first calculation module is used for calculating according to electroencephalogram signals corresponding to two sampling points in the plurality of sampling points to obtain an imaginary part cross frequency phase-locked value set;

the modeling module is used for constructing a multi-element linear regression model of the electroencephalogram signals according to the electroencephalogram signal set;

the second calculation module is used for calculating the row-column standardized directed coherence corresponding to the two sampling points to obtain a row-column standardized directed coherence set;

the determining module is used for determining functional characteristic information of the brain electrical network according to the imaginary part cross frequency phase-locked value set and/or the line-row standardized directional coherent set, wherein the functional characteristic information comprises functional connection information among brain area nodes in the brain electrical network and/or functional weight information of the brain area nodes.

In an optional embodiment, the first computing module is configured to perform filtering processing on electroencephalogram signals in an electroencephalogram signal set to obtain filtered target frequency band signals; performing Hilbert conversion on the target frequency band signal by using a first calculation formula to obtain a target frequency band signal after Hilbert conversion; calculating the target frequency band signal after the Hilbert conversion by using a second calculation formula to obtain an analysis signal; calculating the analysis signal by using a third calculation formula to obtain the instantaneous phase of the analysis signal; calculating the instantaneous phase by using a fourth calculation formula to obtain an imaginary part cross frequency phase-locked value set;

the first calculation formula is as follows:

the second calculation formula is as follows:

the third calculation formula is as follows:

the fourth calculation formula is as follows:

wherein,

for sampling points m and n at l₀To l₁Cross over imaginary part ofFrequency phase-locked value,/₀And l₁Two frequency of interest values (FOI); im is an imaginary value; n is the total number of sampling points; e is a natural base number; phi is a_k(m) is the instantaneous phase of node m at the kth sample point.

In an alternative embodiment, the formula of the multiple autoregressive model in the modeling module is as follows:

In an optional embodiment, the second calculation module is configured to perform fourier transform on the multiple autoregressive model by using a fifth calculation formula to obtain an autoregressive model in a frequency domain; calculating by using a sixth calculation formula based on an autoregressive model under a frequency domain to obtain a row-column standardized directed coherent set of two sampling points;

the fifth calculation formula is as follows:

A(v)x_v＝e_v

wherein,

the sixth formula is as follows:

wherein,

In an optional embodiment, the electroencephalogram network function analyzing device further comprises an electroencephalogram network constructing device; in the electroencephalogram network construction device, an electroencephalogram network comprises nodes and edges; the nodes of the electroencephalogram network represent brain area nodes corresponding to the sampling points; the edges of the brain electrical network represent functional connection information between brain area nodes.

In an optional embodiment, the determining module is configured to determine that functional connection information exists between brain nodes corresponding to two of the plurality of sampling points if the imaginary part cross frequency phase-locked value of two of the plurality of sampling points is greater than or equal to the imaginary part cross frequency phase-locked value threshold value and the magnitude of the row-column normalized directional coherence of the two sampling points is greater than or equal to the row-column normalized directional coherence threshold value.

In an optional embodiment, in the determining module, the function connection information includes function connection strength information; the determining module is used for determining functional connection strength information between two brain area nodes corresponding to two sampling points in the electroencephalogram network according to imaginary part cross frequency phase-locked values and/or row and column standardization directional coherence of the two sampling points if functional connection information exists between the brain area nodes corresponding to the two sampling points in the plurality of sampling points.

In an optional embodiment, in the determining module, the function connection information further includes function connection direction information; the determining module is used for determining functional connection direction information between brain area nodes corresponding to two sampling points in the electroencephalogram network according to the row-column standardized directional coherent direction of the two sampling points if functional connection information exists between the brain area nodes corresponding to the two sampling points in the plurality of sampling points.

In an optional embodiment, the determining module is configured to determine, if functional connection information exists between brain area nodes corresponding to two sampling points in the plurality of sampling points, functional weight information of the brain area nodes in the electroencephalogram network according to imaginary part cross frequency phase-locked values and row-column standardization directional coherence of the two sampling points.

In an optional embodiment, the sampling module is configured to remove an artifact component of the original electroencephalogram signal set through independent component analysis to obtain an artifact-removed signal set, where the artifact component refers to a physiological interference signal; and carrying out high-pass filtering and notch filtering on the artifact-removed signal set to obtain an electroencephalogram signal set.

Accordingly, an embodiment of the present disclosure provides an electronic device, which is characterized in that the electronic device includes a processor and a memory, where the memory stores at least one instruction, at least one program, a code set, or an instruction set, and the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the electroencephalogram network function analysis method.

Accordingly, an embodiment of the present disclosure provides a computer-readable storage medium, where at least one instruction, at least one program, a code set, or an instruction set is stored in the storage medium, and the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the electroencephalogram network function analysis method.

The embodiment of the application has the following beneficial effects:

(1) based on the imaginary part cross frequency phase-locked value set obtained by calculation, the oscillation relationship between a certain node at a certain frequency and another node at another frequency can be considered, the influence of the volume conduction effect can be avoided, and an accurate characteristic value result can be obtained;

(2) the row-column standardized directed coherence obtained by calculation based on the multiple linear regression model can be standardized in the whole matrix range, each value of each row and each column in the matrix can be directly compared with each other, and the functional connection among all nodes can be systematically compared, so that the functional characteristics of the electroencephalogram network can be more completely embodied.

(3) The functional characteristic information of the brain electrical network is accurately and comprehensively determined, and the neural mechanism in the cognitive process is helped to be explored or verified.

Drawings

To more clearly illustrate the technical solutions and advantages of the embodiments or the prior art of the present application, the drawings used in the description of the embodiments or the prior art are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without inventive efforts.

Fig. 1 is a schematic flow chart of a brain electrical network function analysis method provided in an embodiment of the present application;

fig. 2 is a schematic flowchart of a brain electrical network function analysis method of step S102 shown in fig. 1 provided in an embodiment of the present application;

fig. 3 is a schematic flowchart of a brain electrical network function analysis method of step S104 shown in fig. 1 provided in an embodiment of the present application;

FIG. 4 is a schematic structural diagram of an electroencephalogram network of the electroencephalogram network function analysis method provided by the embodiment of the present application;

fig. 5 is a schematic structural diagram of an electroencephalogram network function analyzing apparatus provided in an embodiment of the present application;

fig. 6 is a block diagram of a hardware structure of a server of a brain electrical network function analysis method according to an embodiment of the present application.

Detailed Description

To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings. It should be apparent that the described embodiment is only one embodiment of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

An "embodiment" as referred to herein relates to a particular feature, structure, or characteristic that may be included in at least one implementation of the present application. In the description of the embodiments of the present application, it should be understood that the terms "upper", "lower", "left", "right", "top", "bottom", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are only used for convenience in describing the present application and simplifying the description, and do not indicate or imply that the referenced devices/systems or elements must have a particular orientation, be constructed and operated in a particular orientation, and therefore should not be taken as limiting the present application. The terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. Moreover, the terms "first," "second," and the like are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in other sequences than described or illustrated herein. Furthermore, the terms "comprises," "comprising," and "having"/"is" and any variations thereof, are intended to cover non-exclusive inclusions, such that a process, method, system/apparatus, article, or apparatus that comprises a list of steps or elements/modules is not necessarily limited to those steps or elements/modules expressly listed, but may include other steps or elements/modules not expressly listed or inherent to such process, method, article, or apparatus.

The following describes a specific embodiment of an electroencephalogram network function analysis method provided by the present application, fig. 1 is a schematic flow chart of an electroencephalogram network function analysis method provided by the embodiment of the present application, and the present specification provides method operation steps as shown in the embodiment or the flow chart, but more or fewer operation steps may be included based on conventional or non-creative labor. The sequence of steps recited in the embodiments is only one of many execution sequences, and does not represent a unique execution sequence, and in actual execution, the steps can be executed sequentially or in parallel according to the method shown in the embodiment or the figures (for example, in the environment of a parallel processor or a multi-thread processing). Specifically, as shown in fig. 1, the method includes:

step S101: performing signal sampling based on a plurality of sampling points to obtain an original electroencephalogram signal set; and preprocessing the original electroencephalogram signal set to obtain an electroencephalogram signal set.

In the embodiment of the application, the electroencephalogram signals in the electroencephalogram signal set correspond to the sampling points one by one.

According to some embodiments, the different sampling points may be different electrode points, corresponding to different brain region locations. Optionally, the electroencephalogram signal may be a time-series signal, and the electroencephalogram signal set may be a time-series signal set.

In one implementation, pre-processing the set of raw brain electrical signals may include: removing artifact components of the original electroencephalogram signal set through independent component analysis to obtain an artifact-removed signal set, wherein the artifact components refer to physiological interference signals; and carrying out high-pass filtering and notch filtering on the artifact-removed signal set to obtain an electroencephalogram signal set.

Optionally, removing the artifacts based on the independent component analysis may include: performing matrix transformation on an input original electroencephalogram signal set, and solving out independent components; eliminating partial components in the independent components through certain characteristic screening, thereby obtaining the analyzed independent components; and finally, restoring the independent components to obtain an artifact-removed signal set.

Alternatively, the artifact component may include eye movement trajectories such as blinking and eye movement-induced trajectories. Optionally, the artifact component may include a myoelectric artifact and an electrocardiograph artifact.

Alternatively, the high-pass filtering may be 0.5Hz high-pass filtering. By high-pass filtering, the noise signals with too low frequencies generated by the elements in the sampling circuit can be removed, and the noise signals are not related to the brain electrical signals. Alternatively, the notch filtering may be 50Hz notch filtering. By notch filtering, interference signals at a specific frequency, such as power frequency interference signals, can be filtered.

Step S102: and calculating according to the electroencephalogram signals corresponding to two sampling points in the plurality of sampling points to obtain an imaginary part cross frequency phase-locked value set.

According to some embodiments, the imaginary cross-frequency phase-locked value may be used to characterize the strength of phase synchronicity between nodes of the brain region.

Fig. 2 is a schematic flowchart of the electroencephalogram network function analysis method in step S102 shown in fig. 1 provided in the embodiment of the present application, that is, the schematic flowchart of the electroencephalogram signal corresponding to two sampling points in the plurality of sampling points in step S102 shown in fig. 1 provided in the embodiment of the present application, which is to obtain a set of imaginary cross-frequency phase-locked values, and the method shown in fig. 2 is described in detail below.

In an alternative embodiment, as shown in fig. 2, comprising:

step S201: and filtering the electroencephalogram signals in the electroencephalogram signal set to obtain filtered target frequency band signals.

In one implementation, the filtered target band signal may be obtained by a FIR filter. Optionally, the electroencephalogram signal set can be decomposed into 8 frequency bands, which are respectively delta (2-4Hz), theta (5-7Hz), alpha (8-12Hz), beta (15-29Hz), and gamma₁(30-59Hz)、γ₂(60-89Hz), ripple (90-249Hz), and fast ripple (250-500Hz), to which one or more of the bands the filtered target band signal may belong. Optionally, the frequency band to which the filtered target frequency band signal belongs may be specifically selected according to feature research needs, and feature results may be analyzed from multiple frequency bands, so that features calculated based on different frequency bands may be compared, thereby analyzing electroencephalogram features more comprehensively.

Step S202: and performing Hilbert conversion on the target frequency band signal by using a first calculation formula to obtain a Hilbert-converted target frequency band signal.

According to some embodiments, the first calculation formula is as follows:

where x (t) is a target frequency band signal corresponding to any one sampling point, and p.v. is a cauchy principal value.

Step S203: and calculating the target frequency band signal after the Hilbert conversion by using a second calculation formula to obtain an analysis signal.

According to some embodiments, the second calculation formula is as follows:

step S204: and calculating the analysis signal by using a third calculation formula to obtain the instantaneous phase of the analysis signal.

According to some embodiments, the third calculation formula is as follows:

step S205: and calculating the instantaneous phase by using a fourth calculation formula to obtain an imaginary part cross frequency phase-locked value set.

According to some embodiments, the fourth calculation formula is as follows:

wherein,

In an embodiment of the application, the imaginary cross frequency phase lock value is the result of taking the imaginary part of the cross frequency phase lock value. If the phase-locked value is simply considered, only the oscillation relation of different nodes under the same frequency is considered, and the mechanism of some cognitive phenomena cannot be explained sufficiently; if the cross-frequency phase-locked value is considered, although the oscillation relationship between a certain node at a certain frequency and another node at another frequency can be measured, the cross-frequency phase-locked value is easily influenced by the volume conduction effect, namely, the homologous signals are recorded by different electrodes through volume conduction, so that false correlation occurs between the electrodes, and the real part of the cross-frequency phase-locked value is greatly interfered. According to the method and the device, the imaginary part cross frequency phase-locked value set is obtained through calculation, electroencephalogram network function analysis is carried out according to the imaginary part cross frequency phase-locked value set, the oscillation relation between a certain node and another node under a certain frequency and another frequency can be considered, the influence of volume conduction effect can be avoided, and an accurate characteristic value result is obtained.

Continuing with the description based on the method illustrated in fig. 1, fig. 1 further includes:

step S103: and constructing a multivariate linear regression model of the electroencephalogram signals according to the electroencephalogram signal set.

According to some embodiments, when constructing the multiple linear regression Model of the electroencephalogram signals, a Bivariate Auto-Regressive (BVAR) Model may be constructed based on two electroencephalogram signals. Optionally, the formula of the bivariate autoregressive model is as follows:

wherein x is_tAnd y_tIs two brain electrical signals(ii) a t is the time to be estimated by the model; Δ t is the discrete time; a is a model coefficient of 2 x 2; the matrix k is the lag between samples; p is the order of the model, i.e. the maximum time lag of the model; e is the residual error.

Optionally, the bivariate autoregressive model is for electroencephalograms corresponding to two sampling points, and the bivariate autoregressive model can represent a relation between the electroencephalograms through residual errors and model coefficients, on this basis, in order to represent a relation between any two electroencephalograms, the bivariate autoregressive model can be popularized to a multiple autoregressive model, that is, a formula of the bivariate autoregressive model is accumulated and expressed in a simplified manner to obtain a formula of the multiple autoregressive model, and a formula of the multiple autoregressive model is obtained.

Alternatively, in the multiple autoregressive model or the bivariate autoregressive model, each of the current predicted values is predicted from a past value. For example, in a bivariate autoregressive model

Is formed by x_t-kΔtAnd y_t-kΔtAnd (4) predicting. Optionally, the difference between the current predicted value and the true value is a residual error.

Step S104: and calculating the row-column standardized directed coherence corresponding to the two sampling points according to the multiple linear regression model to obtain a row-column standardized directed coherence set.

According to some embodiments, row-column normalized directional coherence may be used to characterize the direction of connectivity between nodes of the brain region. Optionally, the row-column normalized directional coherence may be used to characterize the strength of the connection relationship between the nodes in the brain region.

Fig. 3 is a schematic flowchart of the electroencephalogram network function analysis method in step S104 shown in fig. 1 provided in the embodiment of the present application, that is, the schematic flowchart of the step S104 shown in fig. 1 provided in the embodiment of the present application, which calculates according to electroencephalogram signals corresponding to two sampling points in a plurality of sampling points to obtain a set of imaginary cross-frequency phase-locked values, and the method shown in fig. 3 is described in detail below.

An alternative embodiment, as shown in fig. 3, includes:

s301: and performing Fourier transform on the multiple autoregressive model by using a fifth calculation formula to obtain an autoregressive model in a frequency domain.

According to some embodiments, the fifth calculation formula is as follows:

A(v)x_v＝e_v

wherein,

wherein A (v) is a three-dimensional model coefficient matrix, and v is frequency; i is an imaginary unit; Δ t is the time interval between adjacent sampling points; e is a natural base number; e.g. of the type_vIs a residual matrix.

Optionally, a (v) is a three-dimensional model coefficient matrix after fourier transform, e_vIs a residual matrix at frequency v, x_vIs the electroencephalogram signal set under the frequency v.

S302: and calculating by using a sixth calculation formula based on an autoregressive model in a frequency domain to obtain a row-column standardized directed coherent set of the two sampling points.

According to some embodiments, the sixth formula is as follows:

wherein,

Alternatively, a is a three-dimensional matrix, and a (v) represents an N × N matrix when the frequency is v. Optionally, the values in the matrix may be used to characterize functional connection information between the brain region functional nodes corresponding to the two sampling points.

The embodiment of the application can be standardized in the whole matrix range, each value of each row and each column in the matrix obtained by standardization can be directly compared with each other, and the functional connection among all nodes can be systematically compared, so that the functional characteristics of the brain electrical network can be more completely embodied.

step S105: and determining functional characteristic information of the electroencephalogram network according to the imaginary part cross frequency phase-locked value set and/or the row-column standardized directional coherent set, wherein the functional characteristic information comprises functional connection information among brain area nodes in the electroencephalogram network and/or functional weight information of the brain area nodes.

In the embodiment of the application, the neural mechanism in the process of identification can be explored or verified by determining the functional characteristic information of the electroencephalogram network. Optionally, the cognitive process may include semantic processing, emotional expression, and the like.

Optionally, the function connection information may be directed function connection information or undirected function connection information. Three specific embodiments for determining that functional connection information exists between brain area nodes corresponding to two sampling points in a plurality of sampling points are described as follows:

firstly, if the imaginary part cross frequency phase locking value of two sampling points in the plurality of sampling points is greater than or equal to the imaginary part cross frequency phase locking value threshold value, and the magnitude of the row-column standardized directed coherence of the two sampling points is greater than or equal to the row-column standardized directed coherence threshold value, determining that functional connection information exists between brain area nodes corresponding to the two sampling points in the plurality of sampling points.

And secondly, if the magnitude of the row-column standardized directional coherence of the two sampling points is greater than or equal to a row-column standardized directional coherence threshold, determining that functional connection information exists between brain area nodes corresponding to the two sampling points in the plurality of sampling points. Alternatively, the function connection information may be directed function connection information.

Thirdly, if the imaginary part cross frequency phase locking value of two sampling points in the plurality of sampling points is greater than or equal to the imaginary part cross frequency phase locking value threshold value, determining that functional connection information exists between brain area nodes corresponding to the two sampling points in the plurality of sampling points. Alternatively, the function connection information may be undirected function connection information.

According to some embodiments, the functional connection information comprises functional connection strength information. Three specific embodiments for determining the functional connection strength information are described below:

firstly, if functional connection information exists between brain area nodes corresponding to two sampling points in a plurality of sampling points, determining functional connection strength information between the two brain area nodes corresponding to the two sampling points in the electroencephalogram network according to imaginary part cross frequency phase locking values and row and column standardization directional coherence of the two sampling points.

And secondly, if functional connection information exists between the brain area nodes corresponding to two sampling points in the plurality of sampling points, determining functional connection strength information between the two brain area nodes corresponding to the two sampling points in the electroencephalogram network according to the imaginary part cross frequency phase-locked values of the two sampling points.

Thirdly, if functional connection information exists between the brain area nodes corresponding to two sampling points in the plurality of sampling points, determining functional connection strength information between the two brain area nodes corresponding to the two sampling points in the electroencephalogram network according to the row-column standardized directional coherence of the two sampling points.

In the embodiment of the present application, the functional connection strength information may include a strength characteristic value, and the larger the strength characteristic value is, the closer the relation between the brain regions is represented, which may help to explain some cognitive phenomena.

Optionally, if the row-column normalized directional coherence corresponding to the first sampling point, that is, the normalized directional coherence of the first sampling point and any other one of the plurality of sampling points, values of the row-column normalized directional coherence are significantly higher than those of the other sampling points, it may be determined that the brain area corresponding to the first sampling point is abnormal in function. Optionally, if the brain region corresponding to the first sampling point is abnormal in function, no matter the direction flows into the row-column normalized directional coherence of the first sampling point, or the direction flows out of the row-column normalized directional coherence of the first sampling point, the numerical value is significantly higher than the row-column normalized directional coherence corresponding to other sampling points.

Optionally, if the imaginary part cross frequency phase-locked values of two sampling points are significantly higher than the imaginary part cross frequency phase-locked values corresponding to the other two sampling points, it may be determined that the brain areas corresponding to the two sampling points are abnormal in function.

In one embodiment, the brain node corresponding to the first sampling point is an epileptogenic focus. Optionally, if the row-column standardized directional coherence value corresponding to the first sampling point is significantly higher than the row-column standardized directional coherence value corresponding to the other sampling points, the abnormal phenomenon of information inflow and outflow of the seizure focus can be further explained from the angle of the functional connection direction characteristic. Optionally, if the imaginary part cross frequency phase-locked value of the first sampling point and the imaginary part cross frequency phase-locked value of the second sampling point are significantly higher than the imaginary part cross frequency phase-locked values corresponding to the other two sampling points, the brain area abnormal function phenomena of the seizure focus and other brain area nodes can be analyzed from the aspect of the functional connection strength characteristic. Optionally, the other brain area nodes may be other epileptogenic foci, and the brain area abnormal function phenomenon may be neuron abnormal synchronous discharge phenomenon.

According to some embodiments, the functional connection information comprises functional connection direction information. Optionally, the function connection information is directed function connection information, and the directed function connection information includes function connection direction information. Optionally, if it is determined that functional connection information exists between brain area nodes corresponding to two sampling points in the plurality of sampling points, determining functional connection direction information between brain area nodes corresponding to the two sampling points in the electroencephalogram network according to the row-column standardized directional coherent direction of the two sampling points.

In one implementation, the two brain area nodes corresponding to two of the plurality of sampling points are a nucleus accumbens and a globus pallidus, respectively. The electroencephalogram network function analysis method provided by the embodiment of the application can determine the function connection information of the nucleus accumbens and the globus pallidus, including function connection strength information and function connection direction information. If the characteristic direction of the functional connection direction information is from the nucleus accumbens to the globus pallidus, the research result that the descending structure of the nucleus accumbens is the globus pallidus can be further explained from the aspect of the functional connection direction characteristics. If its functional connection strength information characterizes a high connection strength, the mechanism by which both participate in reward feedback together can be further explained from the point of view of the functional connection strength characteristics.

According to some embodiments, if functional connection information exists between brain area nodes corresponding to two sampling points in the plurality of sampling points, functional weight information of the brain area nodes in the electroencephalogram network can be determined according to imaginary part cross frequency phase-locked values and row-column standardization directional coherence of the two sampling points. Optionally, the functional weight information of the brain area nodes in the electroencephalogram network is determined according to the imaginary part cross frequency phase-locked values and the row-column standardized directional coherence of any sampling point and all other sampling points in the plurality of sampling points. Optionally, the functional weight information of the brain area nodes in the electroencephalogram network can be determined according to any one of the imaginary part cross frequency phase-locked values and the row-column normalized directional coherence of any one of the sampling point and all other sampling points in the plurality of sampling points.

Optionally, different electroencephalogram signal sets can be obtained based on different sampling objects, so that different imaginary part cross frequency phase-lock value sets and row-column normalized directional coherent sets are obtained. By comparing the data characteristics in the line-row standardized directional coherent set and the line-row standardized directional coherent set obtained based on different sampling objects, the brain function difference of specific people and healthy people can be compared in the angle of resting electroencephalogram data, and the brain function characteristics can be further explained.

Fig. 4 is a schematic structural diagram of an electroencephalogram network of the electroencephalogram network function analysis method provided by the embodiment of the present application.

According to some embodiments, as illustrated in fig. 4, the brain electrical network is a directional weighted network.

Optionally, each circle represents a node, the node is used for representing a brain region node corresponding to the sampling point, and the larger the circle is, the closer the node is to other nodes.

Optionally, a solid line with an arrow represents an edge, the edge is used for representing functional connection information between nodes in the brain region, a thickness of the solid line represents functional connection strength between the nodes, and an implemented arrow direction represents a functional connection direction between the nodes.

Optionally, based on the original electroencephalogram signal set obtained by sampling, an imaginary part cross frequency phase-locked value set and a row-column standardized directed coherent set are obtained through calculation in steps S101 to S104 according to the electroencephalogram network function analysis method illustrated in fig. 1; determining functional characteristic information of the electroencephalogram network according to the imaginary part cross frequency phase-locked value set and/or the row-column standardized directional coherent set by the step S105, wherein the functional characteristic information comprises directional functional connection information, undirected functional connection information, functional connection strength information, functional connection direction information and functional weight information of brain area nodes; based on the functional feature information determined in step S105, functional structures between the brain region nodes are analyzed and determined to construct an electroencephalogram network as shown in fig. 4.

In the embodiment of the application, according to the electroencephalogram network structure, the functions of the electroencephalogram network can be analyzed more vividly and more comprehensively, and comparison among a plurality of electroencephalogram networks can be performed more conveniently.

The embodiment of the present application further provides an electroencephalogram network function analysis apparatus, fig. 5 is a schematic diagram of an electroencephalogram network function analysis apparatus 500 provided in the embodiment of the present application, and as shown in fig. 5, the apparatus includes:

the sampling module 501 is configured to perform signal sampling based on multiple sampling points to obtain an original electroencephalogram signal set; preprocessing an original electroencephalogram signal set to obtain an electroencephalogram signal set; the electroencephalogram signals in the electroencephalogram signal set correspond to the sampling points one by one;

the first calculation module 502 is configured to calculate according to electroencephalogram signals corresponding to two sampling points in the multiple sampling points, so as to obtain an imaginary part cross frequency phase-locked value set;

the modeling module 503 is configured to construct a multiple linear regression model of the electroencephalogram signals according to the electroencephalogram signal set;

a second calculating module 504, configured to calculate a row-column normalized directed coherence corresponding to the two sampling points, to obtain a row-column normalized directed coherence set;

the determining module 505 is configured to determine functional characteristic information of the electroencephalogram network according to the imaginary part cross frequency phase-lock value set and/or the row-column standardization directed coherent set, where the functional characteristic information includes functional connection information between brain region nodes in the electroencephalogram network and/or functional weight information of the brain region nodes.

In an optional embodiment, the first calculating module 502 is configured to perform filtering processing on electroencephalogram signals in an electroencephalogram signal set to obtain filtered target frequency band signals; performing Hilbert conversion on the target frequency band signal by using a first calculation formula to obtain a target frequency band signal after Hilbert conversion; calculating the target frequency band signal after the Hilbert conversion by using a second calculation formula to obtain an analysis signal; calculating the analysis signal by using a third calculation formula to obtain the instantaneous phase of the analysis signal; calculating the instantaneous phase by using a fourth calculation formula to obtain an imaginary part cross frequency phase-locked value set;

the first calculation formula is as follows:

the second calculation formula is as follows:

the third calculation formula is as follows:

the fourth calculation formula is as follows:

wherein,

In an alternative embodiment, the formula of the multiple autoregressive model in the modeling module 503 is as follows:

In an alternative embodiment, the second calculation module 504 is configured to perform fourier transform on the multiple autoregressive model by using a fifth calculation formula, so as to obtain an autoregressive model in a frequency domain; calculating by using a sixth calculation formula based on an autoregressive model in a frequency domain to obtain a row-column standardized directed coherent set of two sampling points;

the fifth calculation formula is as follows:

A(v)x_v＝e_v

wherein,

the sixth formula is as follows:

wherein,

In an alternative embodiment, the electroencephalogram network function analyzing apparatus 500 further includes an electroencephalogram network constructing apparatus. In the electroencephalogram network construction device, an electroencephalogram network comprises nodes and edges; representing brain area nodes corresponding to the sampling points by the nodes of the electroencephalogram network; the edges of the brain electrical network represent functional connection information between brain area nodes.

In an alternative embodiment, the determining module 505 is configured to determine that there is functional connection information between brain nodes corresponding to two of the plurality of sampling points if the imaginary part cross frequency phase-locked value of two of the plurality of sampling points is greater than or equal to the imaginary part cross frequency phase-locked value threshold and the magnitude of the row-column normalized directional coherence of the two sampling points is greater than or equal to the row-column normalized directional coherence threshold.

In an alternative embodiment, in the determining module 505, the functional connection information includes functional connection strength information; the determining module 505 is configured to determine, if functional connection information exists between brain area nodes corresponding to two sampling points in the multiple sampling points, functional connection strength information between the two brain area nodes corresponding to the two sampling points in the electroencephalogram network according to imaginary part cross frequency phase-locked values and/or row-column standardized directional coherence of the two sampling points.

In an alternative embodiment, in the determining module 505, the function connection information further includes function connection direction information; the determining module 505 is configured to determine, if it is determined that functional connection information exists between brain area nodes corresponding to two sampling points in the plurality of sampling points, functional connection direction information between brain area nodes corresponding to the two sampling points in the electroencephalogram network according to a row-column standardized directional coherent direction of the two sampling points.

In an alternative embodiment, the determining module 505 is configured to determine the functional weight information of the brain area nodes in the electroencephalogram network according to the imaginary part cross-frequency phase-locked values and the row-column normalization directional coherence of the two sampling points if it is determined that functional connection information exists between the brain area nodes corresponding to two sampling points in the multiple sampling points.

In an optional embodiment, the sampling module 501 is configured to remove an artifact component of an original electroencephalogram signal set through independent component analysis to obtain an artifact-removed signal set, where the artifact component refers to a physiological interference signal; and carrying out high-pass filtering and notch filtering on the artifact-removed signal set to obtain an electroencephalogram signal set.

The apparatus in the embodiments of the present application is based on the same application concept as the method embodiments described above.

The embodiment of the present application further provides an electronic device, which may be disposed in the server to store at least one instruction, at least one program, a code set, or an instruction set related to implementing the electroencephalogram network function analysis method in the method embodiment, where the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the memory to implement the electroencephalogram network function analysis method.

The method provided by the embodiment of the application can be executed in a computer terminal, a server or a similar operation device. Taking the example of running on a server, fig. 6 is a hardware structure block diagram of the server of the electroencephalogram network function analysis method provided in the embodiment of the present application. As shown in fig. 6, the server 600 may have a relatively large difference due to different configurations or performances, and may include one or more Central Processing Units (CPUs) 610 (the processor 610 may include, but is not limited to, a Processing device such as a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 630 for storing data, and one or more storage media 620 (e.g., one or more mass storage devices) for storing applications 623 or data 622. Memory 630 and storage medium 620 may be, among other things, transient or persistent storage. The program stored on the storage medium 620 may include one or more modules, each of which may include a series of instruction operations for the server. Still further, the central processor 610 may be configured to communicate with the storage medium 620 to execute a series of instruction operations in the storage medium 620 on the server 600. The server 600 may also include one or more power supplies 660, one or more wired or wireless network interfaces 650, one or more input-output interfaces 640, and/or one or more operating systems 621, such as Windows Server, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, and so forth.

The input/output interface 640 may be used to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the server 600. In one embodiment, i/o Interface 640 includes a Network adapter (NIC) that may be coupled to other Network devices via a base station to communicate with the internet. In one example, the input/output interface 640 may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.

It will be understood by those skilled in the art that the structure shown in fig. 6 is merely illustrative and is not intended to limit the structure of the electronic device. For example, server 600 may also include more or fewer components than shown in FIG. 6, or have a different configuration than shown in FIG. 6.

The embodiment of the present application further provides a storage medium, which may be disposed in a server to store at least one instruction, at least one section of program, a code set, or an instruction set related to implementing the electroencephalogram network function analysis method in the method embodiment, where the at least one instruction, the at least one section of program, the code set, or the instruction set is loaded and executed by the processor to implement the electroencephalogram network function analysis method.

Optionally, in this embodiment, the storage medium may be located in at least one network server of a plurality of network servers of a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to include: various media capable of storing program codes, such as a usb disk, a Read-Only Memory (ROM), a removable hard disk, a magnetic disk, or an optical disk.

In the present invention, unless otherwise expressly stated or limited, the terms "connected" and "connected" are to be construed broadly, e.g., as meaning either a fixed connection or a removable connection, or an integral part; can be mechanically or electrically connected; either directly or indirectly through intervening media, either internally or in any other relationship. The specific meaning of the above terms in the present invention can be understood according to specific situations by those skilled in the art.

It should be noted that: the foregoing sequence of the embodiments of the present application is for description only and does not represent the superiority and inferiority of the embodiments, and the specific embodiments are described in the specification, and other embodiments are also within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in the order of execution in different embodiments and to achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown or connected to enable the desired results to be achieved, and in some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the embodiments of the apparatus/system, since they are based on embodiments similar to the method embodiments, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiments.

The foregoing is a preferred embodiment of the present invention, and it should be noted that it would be apparent to those skilled in the art that various modifications and enhancements can be made without departing from the principles of the invention, and such modifications and enhancements are also considered to be within the scope of the invention.

Claims

1. An electroencephalogram network function analysis method is characterized by comprising the following steps:

performing signal sampling based on a plurality of sampling points to obtain an original electroencephalogram signal set; preprocessing the original electroencephalogram signal set to obtain an electroencephalogram signal set; the electroencephalogram signals in the electroencephalogram signal set correspond to the sampling points one by one;

calculating according to the electroencephalogram signals corresponding to two sampling points in the plurality of sampling points to obtain an imaginary part cross frequency phase-locked value set;

constructing a multiple linear regression model of the electroencephalogram signals according to the electroencephalogram signal set;

calculating the row-column standardized directed coherence corresponding to the two sampling points according to the multiple linear regression model to obtain a row-column standardized directed coherence set;

and determining functional characteristic information of the electroencephalogram network according to the imaginary part cross frequency phase-locked value set and/or the row-column standardized directional coherent set, wherein the functional characteristic information comprises functional connection information among brain area nodes in the electroencephalogram network and/or functional weight information of the brain area nodes.

2. The electroencephalogram network function analyzing method according to claim 1, wherein the calculating according to the electroencephalogram signals corresponding to two sampling points in the plurality of sampling points to obtain an imaginary part cross frequency phase-locked value set comprises:

filtering the electroencephalogram signals in the electroencephalogram signal set to obtain filtered target frequency band signals;

performing Hilbert conversion on the target frequency band signal by using a first calculation formula to obtain a Hilbert-converted target frequency band signal;

calculating the target frequency band signal after the Hilbert conversion by using a second calculation formula to obtain an analysis signal;

calculating the analysis signal by using a third calculation formula to obtain the instantaneous phase of the analysis signal;

calculating the instantaneous phase by using a fourth calculation formula to obtain the imaginary part cross frequency phase-locked value set;

the first calculation formula is as follows:

wherein, x (t) is the target frequency band signal corresponding to any sampling point, and p.y. is a cauchy main value;

the second calculation formula is as follows:

the third calculation formula is as follows:

the fourth calculation formula is as follows:

wherein,

3. The electroencephalogram network function analyzing method according to claim 1, wherein the formula of the multiple autoregressive model is as follows:

wherein A is_kIs a model coefficient matrix of NxN, where N is the number of the sampling points; x is the number of_tThe method comprises the steps of (1) acquiring an electroencephalogram signal set in the form of a time sequence matrix of N rows; e.g. of the type_tIs a residual matrix of N rows.

4. The electroencephalogram network function analysis method according to claim 3, wherein the calculating the row-column normalized directed coherence corresponding to the two sampling points according to the multiple linear regression model to obtain a row-column normalized directed coherence set comprises:

performing Fourier transform on the multiple autoregressive model by using a fifth calculation formula to obtain an autoregressive model in a frequency domain;

calculating based on an autoregressive model under the frequency domain by using a sixth calculation formula to obtain a row-column standardized directed coherent set of the two sampling points;

the fifth calculation formula is as follows:

A(v)x_v＝e_v

wherein,

the sixth formula is as follows:

wherein,

and carrying out the row-column standardization directed coherence on the two sampling points, wherein the direction is from the sampling point m to the sampling point n.

5. The brain electrical network function analysis method of claim 1, wherein the brain electrical network comprises nodes and edges;

the nodes of the electroencephalogram network represent brain area nodes corresponding to the sampling points;

the edges of the brain electrical network represent functional connection information among the brain area nodes.

6. The electroencephalogram network function analysis method according to claim 5, wherein the determining the function characteristic information of the electroencephalogram network according to the imaginary part cross frequency phase-lock value set and the row-column normalized directional coherent set comprises:

and if the imaginary part cross frequency phase locking value of two sampling points in the plurality of sampling points is greater than or equal to an imaginary part cross frequency phase locking value threshold value, and the row-column standardized directed coherence value of the two sampling points is greater than or equal to a row-column standardized directed coherence threshold value, determining that functional connection information exists between brain area nodes corresponding to the two sampling points in the plurality of sampling points.

7. The electroencephalogram network function analyzing method according to claim 6, wherein the function connection information includes function connection strength information;

the determining the functional characteristic information of the electroencephalogram network according to the imaginary part cross frequency phase-locked value set and the row-column standardized directional coherent set comprises:

and if functional connection information exists between the brain area nodes corresponding to two sampling points in the plurality of sampling points, determining functional connection strength information between the two brain area nodes corresponding to the two sampling points in the electroencephalogram network according to the imaginary part cross frequency phase-locked values and/or the row and column standardization directional coherence of the two sampling points.

8. The electroencephalogram network function analyzing method according to claim 7, wherein the function connection information further includes function connection direction information;

and if determining that functional connection information exists between brain area nodes corresponding to two sampling points in the plurality of sampling points, determining functional connection direction information between the brain area nodes corresponding to the two sampling points in the electroencephalogram network according to the row-column standardized directional coherent direction of the two sampling points.

9. The electroencephalogram network function analysis method according to claim 6, wherein the determining the function characteristic information of the electroencephalogram network according to the imaginary part cross frequency phase-lock value set and the row-column normalized directional coherent set comprises:

and if functional connection information exists between the brain area nodes corresponding to two sampling points in the plurality of sampling points, determining functional weight information of the brain area nodes in the electroencephalogram network according to the imaginary part cross frequency phase-locked values of the two sampling points and the row-column standardization directed coherence.

10. The electroencephalogram network function analyzing method according to claim 1, wherein the preprocessing the original electroencephalogram signal set to obtain an electroencephalogram signal set comprises:

removing artifact components of the original electroencephalogram signal set through independent component analysis to obtain an artifact-removed signal set, wherein the artifact components refer to physiological interference signals;

and carrying out high-pass filtering and notch filtering on the artifact-removed signal set to obtain an electroencephalogram signal set.

11. An electroencephalogram network function analyzing apparatus, characterized in that the apparatus comprises:

the sampling module is used for sampling signals based on a plurality of sampling points to obtain an original electroencephalogram signal set; preprocessing the original electroencephalogram signal set to obtain an electroencephalogram signal set; the electroencephalogram signals in the electroencephalogram signal set correspond to the sampling points one by one;

the first calculation module is used for calculating according to the electroencephalogram signals corresponding to two sampling points in the plurality of sampling points to obtain an imaginary part cross frequency phase-locked value set;

the modeling module is used for constructing a multiple linear regression model of the electroencephalogram signals according to the electroencephalogram signal set;

and the determining module is used for determining the functional characteristic information of the electroencephalogram network according to the imaginary part cross frequency phase-locked value set and/or the row-column standardized directional coherent set, wherein the functional characteristic information comprises functional connection information among brain area nodes in the electroencephalogram network and/or functional weight information of the brain area nodes.

12. An electronic device, comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, a set of codes, or a set of instructions, which is loaded and executed by the processor to implement the brain electrical network function analyzing method of any one of claims 1-10.

13. A computer readable storage medium having stored therein at least one instruction, at least one program, a set of codes, or a set of instructions, which is loaded and executed by a processor to implement the brain electrical network functional analysis method of any one of claims 1-10.