Method and system for measuring attention and memory processing of children and teenagers based on dynamic brain network, and medium

Abstract

The invention relates to a method for measuring attention and memory processing of children and teenagers based on a dynamic brain network, and belongs to the technical field of brain cognitive processing mechanism research, and the method comprises the following steps: S1, enabling a testee to autonomously practice for a certain time according to an n-back test requirement and method, and carrying out an n-back test after a period of time; s2, in the n-back testing process, collecting an electroencephalogram data set of a testee for preprocessing, and obtaining a brain network sequence of the preprocessed electroencephalogram data set based on MS-sPDC; and S3, obtaining evaluation characteristics of the preprocessed electroencephalogram data set from the brain network sequence, and obtaining a detection report of attention and memory processing of the testee based on the evaluation characteristics.

Description

Technical Field

[0001] The present invention belongs to the technical field of research on brain cognitive processing mechanisms, and relates to a method, system and medium for measuring attention and memory processing of children and adolescents based on dynamic brain networks. Background Art

[0002] Attention and memory are core components of intelligence, and there is a close connection between the two. Attention is the prerequisite for memory, and only information that is noticed can enter the brain for further processing. In the study of the relationship between attention and working memory, people found that when there are distractions, the attention selection mechanism can protect working memory from interference. This is because the capacity of working memory is limited, and attention can filter out information that is not related to the target during the awareness stage, thereby reducing the occupation of irrelevant information on the working memory capacity, reducing memory load, and improving memory efficiency.

[0003] Working memory is an information processing system with limited energy. It has the function of temporarily processing and storing memory information and is an important basis for individuals to handle complex cognitive activities. The concept of working memory is proposed to supplement and improve the concept of short-term memory. It reveals the psychological process of information being retained within a few seconds or minutes after entering the brain. In daily life, working memory plays a vital role in cognitive activities such as learning and calculation. For example, when adding multiple numbers, we need to remember the sum of the first two numbers first, and then use this result to add the following numbers. This process involves the use of working memory.

[0004] In summary, there is a close connection and interaction between attention and working memory. The selective mechanism and inhibitory mechanism of attention have a promoting effect on working memory ability, while the content of working memory and the breadth of working memory will also affect attention ability. Since working memory is a system with limited capacity, the childhood and adolescent stage is a critical period for the development of attention. Improving the attention level of children and adolescents can effectively improve the utilization of working memory, reduce memory load, and thus improve memory efficiency. However, there is currently no relevant research in this regard. Summary of the invention

[0005] In view of this, the purpose of the present invention is to provide a method, system and medium for measuring attention and memory processing of children and adolescents based on dynamic brain networks.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A method for measuring attention and memory processing in children and adolescents based on dynamic brain networks, comprising the following steps:

[0008] S1: Ask the subjects to practice according to the n-back test requirements and methods for a certain period of time, and then take the n-back test after a certain period of time;

[0009] S2: During the n-back test, the EEG data set of the subject is collected for preprocessing, and the brain network sequence of the preprocessed EEG data set is obtained based on MS-sPDC;

[0010] S3: Obtaining evaluation features of the preprocessed EEG data group from the brain network sequence, and obtaining a test report of the subject's attention and memory processing based on the evaluation features.

[0011] Furthermore, the EEG data set collected from the subject in step S2 is preprocessed, specifically including: performing de-meaning processing, de-trending processing and noise elimination processing on the EEG data set in sequence.

[0012] Further, the step S2 of obtaining the brain network sequence of the preprocessed EEG data group based on MS-sPDC specifically includes the following steps:

[0013] The adaptive dynamic brain network estimation method based on Laplace distribution describes the dynamic connectivity changes between brain regions. Given D joint time series, the dynamic driving relationship at time k is represented by a D-dimensional time-varying multiple autoregressive model (tv-MVAR):

[0014]

[0015] Among them, β ij (σ, k), σ = 1, 2, ..., p; i = 1, 2, ..., D; j = 1, 2 ..., D. D represents the number of time series; p represents the model order, and formula (1) is simplified to the following form:

[0016] z k =H k x k +ε k (46)

[0017] where zk=[X1(k),…,X j (k),…,X D (k)] T ∈R D×1 ;x k =[β 11 (1,k),…,β 1D (1,k),…,β 11 (p,k),…,(p,k),…,β 1D (p,k),…,βD1 (1,k),…,β DD (1,k),…,β D1 (p,k),…,β DD (p,k)] T ∈R (D×p×D)×1 ; Observation matrix H k The construction form is expressed as:

[0018]

[0019] The tv-MVAR model is represented by the following state-space model:

[0020] x k =F k-1 x k-1 +w k-1 (48)

[0021] z k =H k x k +ε k (49)

[0022] Wherein, formula (4) is the state equation; formula (5) is the observation equation; k represents a discrete time point; x k ∈R D×p×D represents the state vector; z k represents the observation vector; F k-1 represents the state transfer matrix; H k represents the observation matrix; w k ∈R D×p×D and ε k ∈R D ×1 Represent state noise and observation noise respectively; the parameter update process of the state-space model is divided into a single-step prediction process and a parameter update process;

[0023] The single-step prediction process is:

[0024]

[0025]

[0026] The parameter updating process is as follows:

[0027]

[0028] P k|k =(IK k H k ) k|k-1 (54)

[0029] When the model coefficient x is estimated at time kk After that, sPDC is used to calculate the time-frequency representation of directed connectivity. The corresponding formula for sPDC estimation is as follows:

[0030]

[0031] Among them, f,k is the state x at time k and frequency f k The frequency domain representation of is obtained by Z transform:

[0032]

[0033] Wherein, τ represents an imaginary unit; based on the calculation results of sPDC, the causal relationship from node j to node i in the frequency band [f1, f2] at time k is expressed as:

[0034]

[0035] Introduce the t distribution calculation threshold to achieve the sparse effect of the network:

[0036] thre=T(1-p,n·(n-1)) (58)

[0037] Where T(1-p,v) represents the cumulative t distribution probability density function with v degrees of freedom; 1-p is the confidence level; for L k Perform standardization and ignore the diagonal elements of the network at time k:

[0038]

[0039] Where △ represents the combination of any points between [1, n], but ignores the diagonal elements in the network matrix; if Lsk(i,j) is less than the threshold, then L k The corresponding position (i, j) in is 0; the causal network at time k is sparse based on threshold judgment;

[0040] Introducing Bayesian inference into the state-space model, the probability density functions of the state value and observation value under Bayesian are expressed as:

[0041]

[0042] P k|k-1 and R k The prior distribution function p(P k|k-1 |z 1:k-1 ) and p(R k |z 1:k-1 ) is expressed as inverse-Wishart distribution (IW):

[0043] p(P k|k-1 |z1:k-1 )=IW(P k|k-1 ;t k|k-1 ,T k|k-1 ), p(R k |z 1:k-1 )=IW(R k ;u k|k-1 ,U k|k-1 ) (61)

[0044] Among them, t k|k-1 and T k|k-1 Respectively represent p(P k|k-1 |z 1:k-1 )'s degrees of freedom parameter and inverse scale matrix; u k|k-1 and U k|k-1 Respectively represent p(R k |z 1:k-1 )'s degrees of freedom parameter and inverse scale matrix; based on the characteristics of the inverse-Wiechter distribution, p(P k|k-1 |z 1:k-1 ) is expressed as:

[0045] E[P k|k-1 ]=T k|k-1 / (t k|k-1 -m-1) (62)

[0046] Assume t k|k-1 =m+τ+1, where τ≥0 is a tuning parameter; therefore, formula (18) is further expressed as T k|k-1 =τP k|k-1 ; Introducing Bayesian inference into the covariance matrix R of the observed value noise k , expressed as:

[0047] p(R k |z 1:k-1 )=∫p(R k |R k-1 )p(R k-1 |z 1:k-1 )dR k-1 (63)

[0048] Among them, p(R k-1 |z 1:k-1 ) obeys the inverse-Wiechter distribution, that is, p(R k-1 |z 1:k-1 )=IW(R k-1 ;u k-1|k-1 ,U k-1|k-1 ); prior parameter u k|k-1 and U k|k-1 Replace with u k|k-1 =ρ(u k-1|k-1 -n-1)+n+1,Uk|k-1 =ρU k-1|k-1 , ρ∈[0,1] is the forgetting factor;

[0049] Multivariate Laplace distribution is introduced, and a Bayesian robust expression is constructed under heavy tail measurement noise (HTMN). That is, HTMN is modeled as the following multivariate Laplace distribution form:

[0050]

[0051] Among them, ML(z k ;H k x k ,,R k ) represents z k The multidimensional Laplace distribution function, K·(·) represents the second-kind Bessel function of order n / 2-1; p(z k |x k ) can be rewritten as follows:

[0052] p(z k |x k )=∫N(z k ;H k x k ,π k R k )Exp(π k ; k )dπ k (65)

[0053] Among them, Exp(·) indicates that the parameter is λ k exponential distribution; p(z k |x k ) is rewritten as a mixed Gaussian distribution:

[0054] p(z k |x k ,π k )=N(z k ;H k x k ,π k R k ),p(π k )=Exp(π k ; k ) (66)

[0055] Combined with variational Bayes, the joint parameter posterior distribution p(x k ,π k ,P k|k-1 ,R k |z1:k ) is further calculated as:

[0056] p(x k ,π k ,P k|k-1 ,R k |z 1:k )≈q(x k )q(π k )q(P k|k-1 )q(R k ) (67)

[0057] Where q(·) is the approximate posterior probability density function; based on variational Bayesian approximation, the approximate probability density function is achieved by minimizing the Kullback-Leibler divergence (KLD) between the approximate probability density function and the true probability density function, that is:

[0058]

[0059] in, The solution of each parameter in the parameter set is expressed as:

[0060]

[0061] in, is a set of parameters; Φ is Θ k Any element in ; is the parameter set after ignoring the element Φ; c is a constant term; E[·] represents the expected calculation symbol; based on the joint probability density function p(Θ,z 1:k ) is expressed as:

[0062]

[0063] For parameter update, first set Φ = π k , considering the posterior distribution function q (i+1) (π k ) shows a form similar to the generalized inverse Gaussian distribution (GIG), that is,

[0064]

[0065] The update of relevant parameters is expressed as:

[0066]

[0067] Based on the characteristics of generalized inverse Gaussian distribution, The expected value of is expressed as:

[0068]

[0069] Definition Φ=R k , q( i+1 )(R k ) is expressed as:

[0070]

[0071] in,

[0072]

[0073] Definition Φ=P k|k-1 , P k|k-1 The posterior probability density function is expressed as:

[0074]

[0075] Then we have:

[0076]

[0077] Let Φ = x k , state estimator and the corresponding estimation error covariance matrix Obtained by using the standard Kalman filter update process formula (9) and formula (10); According to the probability density function characteristics of IW, where, and It is expressed as:

[0078]

[0079] Updated measurement noise covariance matrix and the one-step forecast error covariance matrix Further expressed as:

[0080]

[0081] The posterior probability density function q( i+1 )(x k ) is expressed as:

[0082]

[0083] By using an L1 smoothing dynamic process to suppress non-Gaussian noise, formula (7) can be rewritten as:

[0084]

[0085] Among them, there are:

[0086]

[0087] Q k and R k The initial value of is defined as 10 -4 I, I is the identity matrix with relevant dimensions; when the time-varying model coefficients x are estimated k Finally, formulas (11) to (15) are used to further estimate the dynamic causal brain network.

[0088] Furthermore, the evaluation characteristics in step S3 include temporal variability of working memory brain regions, ground state link fluctuations of working memory processing, variability of short-term memory network structure, and working memory encoding efficiency;

[0089] The temporal variability of the working memory brain region is extracted by the brain region laterality, and the calculation formula is as follows:

[0090]

[0091] in, Respectively represent the out-degree strength and in-degree strength of network node i at time t; N and M are the node sets of the specified brain region and the reference brain region respectively; T is the number of brain network time points; γ t It reflects the dominance of a specified brain area in the entire working memory time process; Γ is a cognitive processing index of the temporal variability of the working memory brain area.

[0092] The ground state link fluctuations of the working memory processing are extracted by calculating the average distance of the brain network, and the calculation formula is as follows:

[0093]

[0094] Among them, A t is the brain network matrix at time t, D(A,B) represents the distance between matrices A and B;

[0095] The short-term memory network structure variability is extracted by calculating the overall spatial diversity degree, and the calculation formula is:

[0096]

[0097]

[0098] in: It is accumulated t After the distribution probability density function is sparse t The binary network i at time j Connection of nodes;

[0099] The working memory encoding efficiency is extracted by calculating the network time efficiency, and the calculation formula is:

[0100]

[0101] in, express t At the moment network node i, j The average shortest path length between .

[0102] Further, the step S3 of obtaining the test report of the subject's attention and memory processing based on the evaluation characteristics specifically includes:

[0103] The working memory ability of children and adolescents is measured based on the temporal variability of the working memory brain area. The larger the value of the temporal variability index of the working memory brain area, the stronger the working memory ability of the subject. The ground state link fluctuation based on the working memory processing reflects the "readiness state" or adaptability of the brain in the working memory task; the short-term memory network structure variability reflects the cognitive resource allocation of the subjects in the working memory task; and the working memory encoding efficiency reflects the ability of children and adolescents to withstand mental workload.

[0104] On the other hand, the present invention provides a measurement system for attention and memory processing of children and adolescents based on dynamic brain networks, including an attention and memory induction module, an EEG preprocessing module, a brain network sequence acquisition module, and an attention and memory processing feedback module;

[0105] The attention and memory induction module is used to explain the test requirements and methods of n-back to the test subject, allowing him to practice independently for a certain period of time according to the requirements;

[0106] The EEG preprocessing module is used to collect and preprocess the EEG data set of the subject;

[0107] The brain network sequence acquisition module obtains a dynamic network sequence of the preprocessed EEG data group based on MS-sPDC;

[0108] The attention and memory processing feedback module obtains the subject's attention and memory processing measurement indicators based on the brain network sequence obtained by MS-sPDC, and obtains the subject's attention and memory processing detection results based on the measurement indicators.

[0109] Furthermore, it also includes a human-computer interaction module for outputting the detection results of attention and memory processing to form an evaluation report.

[0110] In a third aspect, the present invention provides a computer storage medium storing a computer program, which, when executed on a computer, can execute the method for measuring attention and memory processing of children and adolescents based on dynamic brain networks as described above.

[0111] The beneficial effects of the present invention are:

[0112] 1. Standardize the collection, processing, analysis and feedback of EEG signals into a system;

[0113] 2. Using the advantages of the n-back experiment in inducing attention and memory and the attention and memory processing mechanisms of children and adolescents, a feasible method for measuring attention and memory processing in children and adolescents was realized;

[0114] 3. The advanced dynamic brain network construction algorithm is used to achieve robustness against the influence of outlier noise and accurately describe the dynamic connectivity changes between brain regions;

[0115] 4. Online real-time test training system, easy to operate, no pre-training required.

[0116] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0117] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0118] Figure 1 This is a flow chart of the attention and memory processing measurement method of the present invention;

[0119] Figure 2 This is a framework diagram of the attention and memory processing measurement method of the present invention;

[0120] Figure 3 This is a schematic diagram of the attention and memory test task of the present invention;

[0121] Figure 4 This is the flow chart of the EEG preprocessing module;

[0122] Figure 5 Generate a flow chart for attention and memory test reports. DETAILED DESCRIPTION

[0123] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0124] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0125] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0126] like Figure 1-5 As shown, a method for measuring attention and memory processing of children and adolescents based on a dynamic brain network of the present invention comprises the following steps:

[0127] S101, before the test, explain the test requirements and methods of n-back to the children and adolescents being tested, let them practice independently for 15 minutes according to the requirements, and then take the formal test after the first interval;

[0128] S102, in the formal test, the EEG data set of children and adolescents is collected and preprocessed, and a network model of the preprocessed EEG data set is obtained based on MS-sPDC;

[0129] S103, based on the dynamic network sequence calculated by MS-sPDC, the evaluation characteristics of the preprocessed EEG data group are obtained, and based on the evaluation characteristics, a detection report on the attention and memory processing of children and adolescents is obtained.

[0130] In step S101, the test requirements and methods of n-back are explained to the students before the test, and the students are asked to practice independently for 15 minutes according to the requirements, and then the formal test is carried out after the first interval. The 15 minutes of independent practice is used for children and adolescents to fully understand the test task of n-back. The test task of n-back is used here to induce children and adolescents to pay attention and memory processing. The first interval is the rest time, and no visual stimulation is given to children and adolescents here. The actual application is as follows:

[0131] Step 1) Provide children and adolescents with a 5-second start reminder to remind them that the test has started;

[0132] Step 2) Provide children and adolescents with 2 seconds of digital stimulation to induce their attention and memory processing;

[0133] Step 3) Provide children and adolescents with a reaction time of 0.3s to test their stimulus response;

[0134] Step 3) Provide children and adolescents with 0.5s of result feedback to determine whether the result is correct;

[0135] Step 4) Provide children and adolescents with a 5-second end reminder to remind them that the test is over.

[0136] like Figure 2 , first of all, before the test, the test requirements and methods of n-back are explained to the children and adolescents being tested, and they are allowed to practice independently for 15 minutes as required. After a short break, the real test begins. By collecting the EEG data group of children and adolescents, the attention and memory processing patterns of children and adolescents are portrayed. In the present invention, the response of children and adolescents is to recall the previous n-th digital stimulus according to the current digital stimulus, and add it to the current digital stimulus (addition within 10). At this time, the response of children and adolescents will be obtained in the form of EEG data group. For example, when children and adolescents are tested 3-back, it means that children and adolescents need to recall the third number in front and add it to the current number. Note that these tasks need to be completed before the end of the current digital stimulus. After the end of the current digital stimulus, there will be a reaction time of 0.3s. During this period, children and adolescents need to input the addition result immediately, and finally feedback the result according to the input result. Among them, if the result is correct, a smiling face will appear in the result feedback stage; if the result is wrong or timeout, an expressionless face will appear.

[0137] In step S102, the EEG data group of children and adolescents is collected and preprocessed, and the dynamic network sequence of the preprocessed EEG data group is obtained based on MS-sPDC. The EEG cap used by the system, that is, a non-invasive EEG electrode, an electrode wire and a fixed cap in one, conforms to the international standard 10-20 system, and selects all 64 channels as data acquisition channels. Preprocessing includes: performing mean removal, trend removal and noise elimination on the collected EEG data in turn, so as to improve signal quality and analyzability. Among them, the EEG signals after digital stimulation are subjected to 4-8Hz bandpass filtering to remove useless signals and eliminate other useless noise interference signals.

[0138] The brain network properties are extracted from the preprocessed EEG data, and the brain network properties can characterize the causal driving relationship between brain regions. The present invention adopts an adaptive dynamic brain network estimation method based on Laplace distribution, which is robust to the influence of artifact noise in the EEG and can accurately describe the dynamic connectivity changes between brain regions. The algorithm principle is as follows:

[0139] Given D joint time series, the dynamic driving relationship at time k can be expressed by the following D-dimensional time-varying multiple autoregressive model (tv-MVAR), which is:

[0140]

[0141] Among them, β ij (σ, k), σ = 1, 2, ..., p; i = 1, 2, ..., D; j = 1, 2 ..., D. D represents the number of time series; p represents the model order. Therefore, formula (1) can be further simplified to the following form:

[0142] z k =H k x k +ε k (90)

[0143] Among them, z k =[X1(k),…,X j (k),…,X D (k)] T ∈R D×1 ;x k =[β 11 (1,k),…,β 1D (1,k),…,β 11 (p,k),…,(p,k),…,β 1D (p,k),…,β D1(1,k),…,β DD (1,k),…,β D1 (p,k),…,β DD (p,k)] T ∈R (D ×p×D)×1 At the same time, the observation matrix H k The construction form can be expressed as:

[0144]

[0145] In summary, the tv-MVAR model can be expressed as the following state-space model, then:

[0146] x k =F k-1 x k-1 +w k-1 (92)

[0147] z k =H k x k +ε k (93)

[0148] Wherein, formula (4) is the state equation; formula (5) is the observation equation; k represents a discrete time point; x k ∈R D×p×D represents the state vector; z k represents the observation vector; F k-1 represents the state transfer matrix; H k represents the observation matrix; w k ∈R D×p×D and ε k ∈R D ×1 Represent state noise and observation noise respectively. In theory, the parameter update process of the state-space model can be divided into two parts, namely the single-step prediction process and the parameter update process, then:

[0149] One-step forecast:

[0150]

[0151]

[0152] Parameter update:

[0153]

[0154] P k|k =(IK k H k ) k|k -1 (98)

[0155] When the model coefficient x is estimated at time k k After that, sPDC is used to calculate the time-frequency representation of directed connectivity. Compared with the traditional PDC-based dynamic causal network construction method, sPDC can more accurately and stably measure the directed causal relationship from time series j to i. Then, the corresponding formula for sPDC estimation is as follows:

[0156]

[0157] Among them, f,k is the frequency domain representation of state xk at time k and frequency f. f,k It can be obtained by Z transform, then:

[0158]

[0159] Where τ represents an imaginary unit. Then, based on the calculation results of sPDC, the causal relationship from node j to node i in the frequency band [f1, f2] at time k can be expressed as:

[0160]

[0161] Next, this paper introduces the t distribution to calculate the corresponding threshold to achieve the sparse effect of the network, then we have:

[0162] thre=T(1-p,n·(n-1)) (102)

[0163] Where T(1-p,v) represents the cumulative t distribution probability density function with v degrees of freedom; 1-p is the confidence level. Then, for L k Perform normalization, which ignores the diagonal elements of the network at time k, namely:

[0164]

[0165] Where △ represents the combination of any points between [1, n], but ignores the diagonal elements in the network matrix. This paper defines the p value as 0.001. If Lsk(i,j) is less than the threshold, then L k The corresponding position (i, j) in is 0. Based on the threshold judgment, the causal network at time k is sparse.

[0166] Considering that the above method is not suitable for dynamic network estimation under unknown noise, the adaptability of Bayesian inference in the dynamic causal estimation process is considered, and Bayesian inference is introduced into the state-space model. Then the probability density functions of the state value and the observation value under Bayes can be expressed as:

[0167]

[0168] Considering P k|k-1 and R k are positive definite matrices, their prior distribution function p(P k|k-1 |z 1:k-1 ) and p(R k |z 1:k-1 ) can be expressed as an inverse-Wishart distribution (IW), then:

[0169] p(P k|k-1 |z 1:k-1 )=IW(P k|k-1 ;t k|k-1 ,T k|k-1 ), p(R k |z 1:k-1 )=IW(R k ;u k|k-1 ,U k|k-1 ) (105)

[0170] Among them, t k|k-1 and T k|k-1 Respectively represent p(P k|k-1 |z 1:k-1 )'s degrees of freedom parameter and inverse scale matrix; u k|k-1 and U k|k-1 Respectively represent p(R k |z 1:k-1 )'s degrees of freedom parameter and inverse scale matrix. Based on the characteristics of the inverse-Wiechter distribution, p(P k|k-1 |z 1:k-1 ) can be expressed as:

[0171] E[P k|k-1 ]=T k|k-1 / (t k|k-1 -m-1) (106)

[0172] Next, let t k|k -1=m+τ+1, where τ≥0 is a tuning parameter; therefore, formula (18) can be further expressed as T k|k -1=τP k|k-1 In addition, Bayesian inference is further introduced into the covariance matrix R of the observed value noise k , then its expression form is:

[0173] p(R k |z 1:k-1 )=∫p(R k |R k-1 )p(R k-1 |z 1:k-1 )dR k-1 (107)

[0174] Among them, p(R k-1 |z 1:k-1 ) obeys the inverse-Wiechter distribution, that is, p(R k-1 |z 1:k-1 )=IW(R k-1 ;u k-1|k-1 ,U k-1|k-1 ). Therefore, the prior parameter u k|k-1 and U k|k-1 Can be replaced by u k|k-1 =ρ(u k-1|k-1 -n-1)+n+1,U k|k-1 =ρU k-1|k-1 , ρ∈[0,1] is the forgetting factor. In general, formulas (16) to (19) construct the adaptive parameter estimation process in the Gaussian state space based on Bayesian. Based on the estimated time-varying model coefficients, formulas (11) to (15) are used to further estimate the dynamic causal brain network

[0175] Considering that the Bayesian dynamic causal network realizes Bayesian modeling, in essence, the observation noise of the algorithm is still assumed to be L2 norm constraint. Therefore, in order to achieve robust estimation of the dynamic causal network, the present invention introduces the multivariate Laplace distribution and constructs a Bayesian robust expression under heavy tail measurement noise (HTMN), that is, HTMN can be modeled as the following multivariate Laplace distribution form:

[0176]

[0177] Among them, ML(z k ;H k x k ,,R k ) represents z k The multidimensional Laplace distribution function of is, and K·(·) represents the second-order Bessel function of n / 2-1. In theory, the specific distribution in the Monte Carlo experiment can be expressed as a proportional mixture of normal distributions. Therefore, p(z k |x k ) can be rewritten as follows:

[0178] p(z k |x k )=∫N(z k ;H k x k ,π k R k )Exp(π k ; k )dπ k (109)

[0179] Among them, Exp(·) indicates that the parameter is λ k exponential distribution. Next, p(z k |x k ) can be rewritten as a mixed Gaussian distribution, then:

[0180] p(z k |x k ,π k )=N(z k ;H k x k ,π k R k ),p(π k )=Exp(π k ; k ) (110)

[0181] In general, formula (16) to formula (22) constitute an adaptive dynamic causal brain network estimation model based on Bayesian-multidimensional Laplace constraints, which is adaptive and robust under noise. Therefore, in order to accurately estimate the state value x k , combined with variational Bayes, the joint parameter posterior distribution p(x k ,π k ,P k|k-1 ,R k |z 1:k ) can be further calculated as:

[0182] p(x k ,π k ,P k|k-1 ,R k |z 1:k )≈q(x k )q(π k )q(P k|k-1 )q(R k )(111)

[0183] Where q(·) is the approximate posterior probability density function. Based on variational Bayesian approximation, the approximate probability density function can be achieved by minimizing the Kullback-Leibler divergence (KLD) between the approximate probability density function and the true probability density function, that is:

[0184]

[0185] in, Therefore, the solution of each parameter in the parameter set can be expressed as:

[0186]

[0187] in, is a set of parameters; Φ is Θ k Any element in ; is the parameter set after ignoring the element Φ; c is a constant term; E[·] represents the expected calculation symbol. In summary, based on the joint probability density function p(Θ,z 1:k ) can be expressed as:

[0188]

[0189] For parameter update, first set Φ = π k , considering the posterior distribution function q( i+1 )(π k ) shows a form similar to the generalized inverse Gaussian distribution (GIG), that is,

[0190]

[0191] The update of relevant parameters can be expressed as:

[0192]

[0193] Based on the characteristics of generalized inverse Gaussian distribution, The expected value of can be expressed as:

[0194]

[0195] Next, define Φ = R k , then q (i+1) (R k ) can be expressed as:

[0196]

[0197] in,

[0198]

[0199] Definition Φ=P k|k-1 , P k|k-1 The posterior probability density function can be expressed as:

[0200]

[0201] Then we have:

[0202]

[0203] Let Φ = xk , considering that the state-space equation still conforms to the Gaussian property, then the state estimator and the corresponding estimation error covariance matrix It can be obtained by using the standard Kalman filter update process formula (9) and formula (10). In addition, according to the probability density function characteristics of IW, where, It can be expressed as:

[0204]

[0205] Updated measurement noise covariance matrix and the one-step forecast error covariance matrix It can be further expressed as:

[0206]

[0207] Then, the posterior probability density function q( i+1 )(x k ) is expressed as:

[0208]

[0209] It is worth noting that this patent adopts an advanced method to initially generate a suitable Q k Value. That is, non-Gaussian noise is suppressed through an L1 smoothing dynamic process. This method has better performance in systems with impulse and Laplace noise, while improving the effect of coefficient sparseness. Therefore, formula (7) can be rewritten as:

[0210]

[0211] Among them, there are:

[0212]

[0213] In addition, Q k and R k The initial value of is defined as 10 -4 I, I is the identity matrix with relevant dimensions. Then, when the time-varying model coefficients x are estimated k Finally, formulas (11) to (15) are used to further estimate the dynamic causal brain network.

[0214] The present invention named this dynamic brain network estimation method MS-sPDC to distinguish it from other time-varying PDCs. The calculated network model is used to draw a brain network topology map to visualize the brain's connection pattern and information transmission path, and the time-varying network is used to extract network properties and characterize the brain's cognitive processing mode.

[0215] Using the calculated brain network sequence, the present invention adopts the following steps to extract working memory cognitive processing indicators, which are as follows:

[0216] (1) Temporal variability of working memory brain regions

[0217] Different brain regions have different activation levels in working memory cognitive processing. The present invention uses brain region laterality to extract this index, and the calculation formula is as follows:

[0218]

[0219] in, Respectively expressed in t The out-degree strength and in-degree strength of network node i at the moment. N, M are the node sets of the specified brain region and the reference brain region respectively. T is the number of time points in the brain network. t Reflects the dominance of a given brain region over the entire working memory time course. Γ It is the cognitive processing index of the time variability of the working memory brain area. In actual operation, the designated area of ​​the present invention is the prefrontal cortex and the parietal cortex, and the reference brain area is other brain areas except the above two brain areas. The larger the value of the time variability index of the working memory brain area, the stronger the working memory ability of the subject.

[0220] (2) Ground state link fluctuations in working memory processing

[0221] Moderate network fluctuations may be associated with improved cognitive abilities, especially in situations where tasks such as working memory require high flexibility. The brain needs to be able to flexibly adjust network connectivity according to task requirements in order to effectively store, retain, and retrieve information. The present invention extracts this indicator by calculating the average distance of the brain network, and the calculation formula is as follows:

[0222]

[0223] Among them, A t is the brain network matrix at time t, D( A, B) represents the distance between matrices A and B. The ground state link fluctuation index of working memory processing reflects the brain's "readiness" or adaptability in working memory tasks.

[0224] (3) Structural variability of short-term memory networks

[0225] The overall spatial diversity index of the dynamic brain network reflects the complexity and breadth of the connectivity pattern, which is the basis for efficient cognitive processing. The present invention extracts this index by calculating the overall spatial diversity degree:

[0226]

[0227] in: It is accumulated t After the distribution probability density function is sparse t The binary network i at time j The connectivity of nodes. The short-term memory network structural variability index characterizes the allocation of cognitive resources of subjects in working memory tasks.

[0228] (4) Working memory encoding efficiency

[0229] Executing cognitive tasks with increasing difficulty requires more brain resources, resulting in a higher mental workload, and the brain needs to process tasks with higher work efficiency. The present invention extracts this indicator by calculating the network time efficiency:

[0230]

[0231] in, express t At the moment network node i, j The working memory encoding efficiency index represents the mental load that the subjects can bear.

[0232] The embodiment of the present application also provides a measurement system for attention and memory processing of children and adolescents based on dynamic brain networks, and the system includes at least one software function module stored in a storage module or solidified in an operating system (Operating System, 0s) in the form of software or firmware (Firmware). The attention and memory induction module and the brain network sequence acquisition module are used to execute executable modules stored in the storage module, such as the software function modules and computer program modules included in the measurement system for attention and memory processing of children and adolescents based on dynamic brain networks.

[0233] The system also includes an attention and memory induction module, an EEG preprocessing module, a brain network sequence acquisition module, and an attention and memory processing feedback module.

[0234] In the attention and memory induction module, the test requirements and methods of n-back were explained to the children and adolescents before the test, and they were asked to practice independently for 15 minutes according to the requirements, and then the formal test was carried out after the first interval.

[0235] The EEG preprocessing module and the brain network sequence acquisition module collected and preprocessed the EEG data sets of children and adolescents in the formal test, and obtained the network model of the preprocessed EEG data sets based on MS-sPDC.

[0236] The attention and memory processing feedback module obtains the evaluation characteristics of the preprocessed EEG data group based on the dynamic network sequence calculated by MS-sPDC, and obtains the detection report of attention and memory processing of children and adolescents based on the evaluation characteristics.

[0237] The attention and memory processing feedback module is coupled with the human-computer interaction module to output an evaluation report on the effects of attention and memory processing.

[0238] In this embodiment, the storage module can be, but is not limited to, a random access memory, a read-only memory, a programmable read-only memory, an erasable programmable read-only memory, an electrically erasable programmable read-only memory, etc. In this embodiment, the storage module can be used to store the MS-sPDC algorithm in the brain network sequence acquisition module, etc. Of course, the storage module can also be used to store a program, and the processing module executes the program after receiving the execution instruction.

[0239] Through the description of the above implementation methods, technical personnel in this field can clearly understand that the present application can be implemented by hardware, and can also be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, an electronic device, or a network device, etc.) to execute the methods described in each implementation scenario of the present application.

[0240] In summary, the present application provides a rehabilitation training method, system and medium based on multimodal brain-computer interface. The rehabilitation training method based on multimodal brain-computer interface comprises the following steps: S101, explaining the test requirements and methods of n-back to the tested children and adolescents before the test, allowing them to practice autonomously for 15 minutes as required, and conducting a formal test after the first interval; S102, in the formal test, the EEG data group of children and adolescents is collected and preprocessed, and the network model of the EEG data group after preprocessing is obtained based on MS-sPDC; S103, based on the dynamic network sequence calculated by MS-sPDC, the evaluation characteristics of the EEG data group after preprocessing are obtained, and the detection report of attention and memory processing of children and adolescents is obtained based on the evaluation characteristics. The present invention has high accuracy, high information transmission rate, convenient operation, no training, and high practical value.

[0241] In the above embodiments, the description's reference to "this embodiment" indicates that a particular feature, structure, or characteristic described in conjunction with the embodiment is included in at least some embodiments, but not necessarily all embodiments. Multiple occurrences of "this embodiment" do not necessarily all refer to the same embodiment.

[0242] In the above-described embodiments, although the invention has been described in conjunction with specific embodiments of the invention, many substitutions, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art based on the foregoing description. For example, other storage structures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed. Embodiments of the invention are intended to encompass all such substitutions, modifications, and variations that fall within the broad scope of the appended claims.

[0243] This embodiment further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, any one of the methods in this embodiment is implemented.

[0244] This embodiment also provides an electronic terminal, including: a processor and a memory;

[0245] The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the terminal executes any one of the methods in this embodiment.

[0246] The computer-readable storage medium in this embodiment can be understood by ordinary technicians in this field: all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to the computer program. The aforementioned computer program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk and other media that can store program codes.

[0247] The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication with each other. The memory is used to store computer programs, the communication interface is used to communicate, and the processor and the transceiver are used to run computer programs so that the electronic terminal executes each step of the above method.

[0248] In this embodiment, the memory may include a random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0249] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0250] The present invention can be used in many general or special computing system environments or configurations, such as personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like.

[0251] The present invention may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0252] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A method for measuring attention and memory processing in children and adolescents based on dynamic brain networks, characterized by: The following steps are involved: S1: Ask the subjects to practice according to the n-back test requirements and methods for a certain period of time, and then take the n-back test after a certain period of time; S2: During the n-back test, the EEG data set of the subject is collected for preprocessing, and the brain network sequence of the preprocessed EEG data set is obtained based on MS-sPDC; S3: Obtaining evaluation features of the preprocessed EEG data group from the brain network sequence, and obtaining a test report of the subject's attention and memory processing based on the evaluation features.

2. The method for measuring attention and memory processing of children and adolescents based on dynamic brain networks according to claim 1 is characterized by: The EEG data set collected from the subject in step S2 is preprocessed, specifically including: performing de-meaning processing, de-trending processing and noise elimination processing on the EEG data set in sequence.

3. The method for measuring attention and memory processing of children and adolescents based on dynamic brain networks according to claim 1, characterized in that: The step S2 of obtaining the brain network sequence of the preprocessed EEG data set based on MS-sPDC specifically includes the following steps: The adaptive dynamic brain network estimation method based on Laplace distribution describes the dynamic connectivity changes between brain regions. Given D joint time series, the dynamic driving relationship at time k is represented by the D-dimensional time-varying multivariate autoregressive model tv-MVAR: Among them, β ij (σ, k), σ = 1, 2, ..., p; i = 1, 2, ..., D; j = 1, 2 ..., D; D represents the number of time series; p represents the model order, formula (1) is simplified to the following form: z k =H k x k +ε k (2) Among them, z k =[X1(k),…,X j (k),…,X D (k)] T ∈R D×1 ;x k =[β 11 (1,k),…,β 1D (1,k),…,β 11 (p,k),…,(p,k),…,β 1D (p,k),…,β D1 (1,k),…,β DD (1,k),…,β D1 (p,k),…,β DD (p,k)] T ∈R (D ×p×D)×1 ; Observation matrix H k The construction form is expressed as: The tv-MVAR model is represented by the following state-space model: x k =F k-1 x k-1 +w k-1 (4) z k =H k x k +ε k (5) Wherein, formula (4) is the state equation; formula (5) is the observation equation; k represents a discrete time point; x k ∈R D×p×D represents the state vector; z k represents the observation vector; F k-1 represents the state transfer matrix; H k represents the observation matrix; w k ∈R D×p×D and ε k ∈R D×1 Represent state noise and observation noise respectively; the parameter update process of the state-space model is divided into a single-step prediction process and a parameter update process; The single-step prediction process is: The parameter updating process is as follows: P k|k =(I-K k H k )P k|k-1 (10) When the model coefficient x is estimated at time k k After that, sPDC is used to calculate the time-frequency representation of directed connectivity. The corresponding formula for sPDC estimation is as follows: Among them, f,k is the state x at time k and frequency f k The frequency domain representation of is obtained by Z transform: Wherein, τ represents an imaginary unit; based on the calculation results of sPDC, the causal relationship from node j to node i in the frequency band [f1, f2] at time k is expressed as: Introduce the t distribution calculation threshold to achieve the sparse effect of the network: thre=T(1-p,n·(n-1)) (14) Where T(1-p,υ) represents the cumulative t distribution probability density function with degrees of freedom υ; 1-p is the confidence level; for L k Perform standardization and ignore the diagonal elements of the network at time k: Where △ represents the combination of any points between [1, n], but ignores the diagonal elements in the network matrix; if Lsk(i,j) is less than the threshold, then L k The corresponding position (i, j) in is 0; the causal network at time k is sparse based on threshold judgment; Introducing Bayesian inference into the state-space model, the probability density functions of the state value and observation value under Bayesian are expressed as: P k|k-1 and R k The prior distribution function p(P k|k-1 |z 1:k-1 ) and p(R k |z 1:k-1 ) is expressed as inverse-Wishart distribution (IW): p(P k|k-1 |z 1:k-1 )=IW(P k|k-1 ;t k|k-1 ,T k|k-1 ),p(R k |z 1:k-1 )=IW(R k ;u k|k-1 ,U k|k-1 ) (17) Among them, t k|k-1 and T k|k-1 Respectively represent p(P k|k-1 |z 1:k-1 )'s degrees of freedom parameter and inverse scale matrix; u k|k-1 and U k|k-1 Respectively represent p(R k |z 1:k-1 )'s degrees of freedom parameter and inverse scale matrix; based on the characteristics of the inverse-Wiechter distribution, p(P k|k-1 |z 1:k-1 ) is expressed as: E[P k|k-1 ]=T k|k-1 / (t k|k-1 -m-1) (18) Assume t k|k-1 =m+τ+1, where τ≥0 is a tuning parameter; therefore, formula (18) is further expressed as T k|k-1 =τP k|k-1 ; Introducing Bayesian inference into the covariance matrix R of the observed value noise k , expressed as: p(R k |z 1:k-1 )=∫p(R k |R k-1 )p(R k-1 |z 1:k-1 )dR k-1 (19) Among them, p(R k-1 |z 1:k-1 ) obeys the inverse-Wiechter distribution, that is, p(R k-1 |z 1:k-1 )=IW(R k-1； u k-1|k-1 ,U k-1|k-1 ); prior parameter u k|k-1 and U k|k-1 Replace with u k|k-1 =ρ(u k-1|k-1 -n-1)+n+1,U k|k-1 =ρU k-1|k-1 , ρ∈[0,1] is the forgetting factor; The multivariate Laplace distribution is introduced to construct a Bayesian robust expression under the heavy-tailed observation noise HTMN, that is, the HTMN is modeled as the following multivariate Laplace distribution form: Among them, ML(z k ;H k x k ,,R k ) represents z k The multidimensional Laplace distribution function, K·(·) represents the second-kind Bessel function of order n / 2-1; p(z k |x k ) can be rewritten as follows: p(z k |x k )=∫N(z k ;H k x k ,p k R k )Exp(π k ;l k )dπ k (21) Among them, Exp(·) indicates that the parameter is λ k exponential distribution; p(z k |x k ) is rewritten as a mixed Gaussian distribution: p(z k |x k ,p k )=N(z k ;H k x k ,p k R k ),p(π k )=Exp(π k ;l k ) (22) Combined with variational Bayes, the joint parameter posterior distribution p(x k ,π k ,P k|k-1 ,R k |z 1:k ) is further calculated as: p(x k ,π k ,P k|k-1 ,R k |z 1:k )≈q(x k )q(π k )q(P k|k-1 )q(R k )(23) Where q(·) is the approximate posterior probability density function; based on variational Bayesian approximation, the approximate probability density function is achieved by minimizing the Kullback-Leibler divergence (KLD) between the approximate probability density function and the true probability density function, that is: in, The solution of each parameter in the parameter set is expressed as: in, is a set of parameters; Φ is Θ k Any element in ; is the parameter set after ignoring the element Φ; c is a constant term; E[·] represents the expected calculation symbol; based on the joint probability density function p(Θ,z 1:k ) is expressed as: For parameter update, first set Φ = π k , considering the posterior distribution function q (i+1) (π k ) shows a form similar to the generalized inverse Gaussian distribution GIG, that is: The update of relevant parameters is expressed as: Based on the characteristics of generalized inverse Gaussian distribution, The expected value of is expressed as: Definition Φ=R k ,q (i+1) (R k ) is expressed as: in, Definition Φ=P k|k-1 , P k|k-1 The posterior probability density function is expressed as: Then we have: Let Φ = x k , state estimator and the corresponding estimation error covariance matrix Obtained by using the standard Kalman filter update process formula (9) and formula (10); According to the probability density function characteristics of IW, where, and It is expressed as: Updated measurement noise covariance matrix and the one-step forecast error covariance matrix Further expressed as: The posterior probability density function q (i+1 )(x k ) is expressed as: By using an L1 smoothing dynamic process to suppress non-Gaussian noise, formula (7) can be rewritten as: Among them, there are: Q k and R k The initial value of is defined as 10 -4 I, I is the identity matrix with relevant dimensions; when the time-varying model coefficients x are estimated k Finally, formulas (11) to (15) are used to further estimate the dynamic causal brain network.

4. The method for measuring attention and memory processing of children and adolescents based on dynamic brain networks according to claim 1 is characterized by: The evaluation characteristics in step S3 include temporal variability of working memory brain regions, ground state link fluctuations of working memory processing, variability of short-term memory network structure, and working memory encoding efficiency; The temporal variability of the working memory brain region is extracted by the brain region laterality, and the calculation formula is as follows: in, Respectively expressed in t The out-degree strength and in-degree strength of network node i at the moment; N, M are the node sets of the designated brain region and the reference brain region respectively; T is the number of time points in the brain network; γ t Reflects the dominance of a given brain region over the entire working memory time course; Γ It is an index of cognitive processing of temporal variability in working memory brain areas; The ground state link fluctuations of the working memory processing are extracted by calculating the average distance of the brain network, and the calculation formula is as follows: Among them, A t is the brain network matrix at time t, D( A, B) represents the distance between matrices A and B; The short-term memory network structure variability is extracted by calculating the overall spatial diversity degree, and the calculation formula is: in: is the connection between nodes i and j in the binary network at time t after the cumulative t-distribution probability density function is sparse; The working memory encoding efficiency is extracted by calculating the network time efficiency, and the calculation formula is: in, It represents the average shortest path length between network nodes i and j at time t.

5. The method for measuring attention and memory processing of children and adolescents based on dynamic brain networks according to claim 4 is characterized by: The step S3 of obtaining the test report of the subject's attention and memory processing based on the evaluation characteristics specifically includes: The working memory ability of children and adolescents is measured based on the temporal variability of the working memory brain area. The larger the value of the temporal variability index of the working memory brain area, the stronger the working memory ability of the subject. The ground state link fluctuation based on the working memory processing reflects the "readiness state" or adaptability of the brain in the working memory task. The short-term memory network structure variability reflects the cognitive resource allocation of the subject in the working memory task. The working memory encoding efficiency reflects the ability of children and adolescents to withstand mental workload.

6. A measurement system for children and adolescents' attention and memory processing based on dynamic brain networks, characterized by: It includes attention and memory induction module, EEG preprocessing module, brain network sequence acquisition module and attention and memory processing feedback module; The attention and memory induction module is used to explain the test requirements and methods of n-back to the test subject, allowing him to practice independently for a certain period of time according to the requirements; The EEG preprocessing module is used to collect and preprocess the EEG data set of the subject; The brain network sequence acquisition module obtains a dynamic network sequence of the preprocessed EEG data group based on MS-sPDC; The attention and memory processing feedback module obtains the subject's attention and memory processing measurement indicators based on the brain network sequence obtained by MS-sPDC, and obtains the subject's attention and memory processing detection results based on the measurement indicators.

7. The dynamic brain network-based attention and memory processing measurement system for children and adolescents according to claim 6 is characterized by: It also includes a human-computer interaction module for outputting the detection results of attention and memory processing and forming an evaluation report.

8. A computer storage medium storing a computer program, wherein: When the computer program is run on a computer, it can execute the method for measuring attention and memory processing of children and adolescents based on dynamic brain networks as described in any one of claims 1 to 5.

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