Microstate source positioning method, device, electronic device and storage medium

Abstract

The present disclosure relates to a method, device, electronic device and storage medium for localizing a microstate source, which obtains electroencephalogram signal data composed of electroencephalogram signals obtained by at least one subject through multiple leads, and draws at least one electroencephalogram topological map for characterizing the signal intensity difference of each lead position at the same time. At least one type of microstate and the corresponding characteristic topological map are obtained by clustering the electroencephalogram topological map. The microstate sequence corresponding to the subject is determined according to the characteristic topological map corresponding to each type of microstate and the electroencephalogram signal data of each subject. At the same time, a magnetic resonance file for characterizing the time sequence change of the position of the activated brain area in three-dimensional space is determined according to the electroencephalogram signal data of each subject, and the target activated brain area corresponding to each type of microstate is determined according to the magnetic resonance file of each subject and the microstate sequence. The present disclosure performs microstate source localization by fitting the microstate sequence with the magnetic resonance file, and the localization process is simple and efficient, and the localization result is accurate.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and in particular to a micro-state source positioning method, device, electronic device and storage medium. Background Art

[0002] Microstates are tools for studying the dynamic properties of brain neurons. The broadband EEG signals in the resting state can be represented by a limited number of brain topological maps, which remain stable for a short period of time (60-120 milliseconds) and then quickly transition to another steady state. These discrete topological steady states are called "microstates" and can reflect the instantaneous state of the overall neuronal activity in the brain. As an analysis method for EEG activity, it has received widespread attention. Scholars believe that microstates are the basic modules of spontaneous consciousness, and their temporal dynamics determine the quality of the mental state. Related technologies have defects in the study of source localization of microstates, such as long analysis time, low efficiency and small application range. Summary of the invention

[0003] In view of this, the present disclosure proposes a microstate source positioning method, device, electronic device and storage medium, aiming to quickly and efficiently locate the source of the microstate.

[0004] According to a first aspect of the present disclosure, a microstate source positioning method is provided, the method comprising:

[0005] Acquiring electroencephalogram (EEG) signal data of at least one subject, wherein the EEG signal data includes EEG signals acquired through a plurality of leads;

[0006] Drawing at least one EEG topography map according to the EEG signal data of each subject, wherein the EEG topography map is used to characterize the signal strength difference of each lead position at the same time;

[0007] Clustering the EEG topological map to obtain at least one type of microstate and a characteristic topological map corresponding to each type of microstate;

[0008] According to the characteristic topological map corresponding to each type of the microstate and the EEG signal data of each subject, a microstate sequence corresponding to each subject is obtained;

[0009] Determine a corresponding magnetic resonance image file according to the electroencephalogram signal data of each subject, wherein the magnetic resonance image file is used to characterize the temporal changes in the position of the activated brain region of the corresponding subject in three-dimensional space;

[0010] According to the magnetic resonance imaging file and microstate sequence of each subject, the target activated brain area corresponding to each type of microstate is determined.

[0011] In a possible implementation, the acquiring of EEG signal data of at least one subject includes:

[0012] Collecting raw EEG data of at least one subject;

[0013] The original EEG data of each subject is preprocessed to obtain EEG signal data.

[0014] In a possible implementation, preprocessing the original EEG data of each subject to obtain EEG signal data includes:

[0015] The original EEG data of each subject is processed by at least one of downsampling, filtering, independent component analysis, bad track difference and re-referencing to obtain EEG signal data.

[0016] In a possible implementation, drawing at least one EEG topology map according to the EEG signal data of each subject includes:

[0017] Sampling the EEG signal data of each subject according to a preset time frequency to obtain signal values ​​of the multiple leads;

[0018] For each of the subjects, calculating the standard deviation of a plurality of the signal values ​​at the same time to obtain the corresponding signal intensity;

[0019] Determine the time corresponding to the highest signal intensity of each subject as the target time;

[0020] An EEG topology map is drawn according to the EEG signal value of each subject at the target moment and the position of the corresponding lead.

[0021] In a possible implementation, drawing an EEG topology map according to the EEG signal value of each subject at the target time and the position of the corresponding lead includes:

[0022] Determine multiple intensity ranges and corresponding colors;

[0023] Initializing an original topological map according to the position of each of the leads;

[0024] For each of the subjects, the original topological map is rendered according to the color corresponding to the intensity range of the electroencephalogram signal value corresponding to each of the leads to obtain an electroencephalogram topological map.

[0025] In a possible implementation, clustering the EEG topological map to obtain at least one type of microstate and a characteristic topological map corresponding to each type of microstate includes:

[0026] Clustering the EEG topology map based on a plurality of preset numbers of categories to obtain corresponding clustering results, wherein the clustering results include microstates of a corresponding number of categories and an EEG spectrogram as a cluster center of each category of the microstates;

[0027] Calculate the clustering characteristic value of each clustering result corresponding to the number of categories;

[0028] The optimal number of categories is determined according to the clustering characteristic value, and the micro-states in the clustering result corresponding to the optimal number of categories are obtained, and the cluster center corresponding to each of the micro-states is determined as a characteristic topological graph.

[0029] In a possible implementation, obtaining a microstate sequence corresponding to each subject according to the characteristic topological map corresponding to each type of microstate and the EEG signal data of each subject includes:

[0030] For each of the subjects, sampling the EEG signal data according to a preset time frequency to obtain signal values ​​of the multiple leads;

[0031] Draw a corresponding instantaneous topological diagram according to the signal value of each lead at the same moment;

[0032] By comparing the characteristic topological graph of each type of microstate with the instantaneous topological graphs at the multiple moments, a microstate sequence corresponding to each type of microstate is obtained.

[0033] In a possible implementation, determining the corresponding magnetic resonance image file according to the EEG signal data of each subject includes:

[0034] Intercepting the EEG signal data according to a preset time window, and processing to obtain a plurality of interval signal values;

[0035] Calculating the current distribution of the cerebral cortex of the subject according to the interval signal value of each subject, the position information of each lead and a preset brain mapping model;

[0036] For each of the subjects, determining a magnetic resonance image of a brain rendering model with annotated activated brain regions according to the current distribution of the subject, wherein the activated brain regions are regions of the cerebral cortex where the current value of the subject is relatively high;

[0037] The plurality of magnetic resonance images are stored according to a time sequence relationship to obtain a magnetic resonance file.

[0038] In a possible implementation, determining the target activated brain region corresponding to each type of microstate according to the magnetic resonance image and microstate sequence of each subject includes:

[0039] For each of the subjects, acquiring at least one corresponding magnetic resonance image in the magnetic resonance file according to the microstate sequence of each type of microstate;

[0040] Fitting each type of the microstate sequence and the corresponding at least one magnetic resonance image to obtain an activated brain area image of each type of microstate sequence of the subject;

[0041] Monte Carlo analysis is performed on the activated brain area images corresponding to the same microstate sequence of all the subjects to obtain the target activated brain area of ​​the microstate sequence.

[0042] According to a second aspect of the present disclosure, a microstate source positioning device is provided, the device comprising:

[0043] A data acquisition module, used to acquire EEG signal data of at least one subject, wherein the EEG signal data includes EEG signals acquired through multiple leads;

[0044] A topology drawing module, used for drawing at least one EEG topology map according to the EEG signal data of each subject, wherein the EEG topology map is used to characterize the signal strength difference of each lead position at the same time;

[0045] A clustering module, used for clustering the EEG topological map to obtain at least one type of microstate and a characteristic topological map corresponding to each type of microstate;

[0046] A signal fitting module, used for obtaining a microstate sequence corresponding to each subject according to a characteristic topological map corresponding to each type of microstate and the EEG signal data of each subject;

[0047] A file determination module, used to determine a corresponding magnetic resonance file according to the EEG signal data of each subject, wherein the magnetic resonance file is used to characterize the temporal changes of the position of the activated brain region of the corresponding subject in three-dimensional space;

[0048] The activated brain region determination module is used to determine the target activated brain region corresponding to each type of microstate according to the magnetic resonance image and microstate sequence of each subject.

[0049] In a possible implementation, the data acquisition module is further used to:

[0050] Collecting raw EEG data of at least one subject;

[0051] The original EEG data of each subject is preprocessed to obtain EEG signal data.

[0052] In a possible implementation, the data acquisition module is further used to:

[0053] The original EEG data of each subject is processed by at least one of downsampling, filtering, independent component analysis, bad track difference and re-referencing to obtain EEG signal data.

[0054] In a possible implementation, the topology drawing module is further used to:

[0055] Sampling the EEG signal data of each subject according to a preset time frequency to obtain signal values ​​of the multiple leads;

[0056] For each of the subjects, calculating the standard deviation of a plurality of the signal values ​​at the same time to obtain the corresponding signal intensity;

[0057] Determine the time corresponding to the highest signal intensity of each subject as the target time;

[0058] An EEG topology map is drawn according to the EEG signal value of each subject at the target moment and the position of the corresponding lead.

[0059] In a possible implementation, the topology drawing module is further used to:

[0060] Determine multiple intensity ranges and corresponding colors;

[0061] Initializing an original topological map according to the position of each of the leads;

[0062] For each of the subjects, the original topological map is rendered according to the color corresponding to the intensity range of the electroencephalogram signal value corresponding to each of the leads to obtain an electroencephalogram topological map.

[0063] In a possible implementation, the clustering module is further used to:

[0064] Clustering the EEG topology map based on a plurality of preset numbers of categories to obtain corresponding clustering results, wherein the clustering results include microstates of a corresponding number of categories and an EEG spectrogram as a cluster center of each category of the microstates;

[0065] Calculate the clustering characteristic value of each clustering result corresponding to the number of categories;

[0066] The optimal number of categories is determined according to the clustering characteristic value, and the micro-states in the clustering result corresponding to the optimal number of categories are obtained, and the cluster center corresponding to each of the micro-states is determined as a characteristic topological graph.

[0067] In a possible implementation, the signal fitting module is further used to:

[0068] For each of the subjects, sampling the EEG signal data according to a preset time frequency to obtain signal values ​​of the multiple leads;

[0069] Draw a corresponding instantaneous topological diagram according to the signal value of each lead at the same moment;

[0070] By comparing the characteristic topological graph of each type of microstate with the instantaneous topological graphs at the multiple moments, a microstate sequence corresponding to each type of microstate is obtained.

[0071] In a possible implementation, the file determination module is further used to:

[0072] Intercepting the EEG signal data according to a preset time window, and processing to obtain a plurality of interval signal values;

[0073] Calculating the current distribution of the cerebral cortex of the subject according to the interval signal value of each subject, the position information of each lead and a preset brain mapping model;

[0074] For each of the subjects, determining a magnetic resonance image of a brain rendering model with annotated activated brain regions according to the current distribution of the subject, wherein the activated brain regions are regions of the cerebral cortex where the current value of the subject is relatively high;

[0075] The plurality of magnetic resonance images are stored according to a time sequence relationship to obtain a magnetic resonance file.

[0076] In a possible implementation, the activated brain region determination module is further used to:

[0077] For each of the subjects, acquiring at least one corresponding magnetic resonance image in the magnetic resonance file according to the microstate sequence of each type of microstate;

[0078] Fitting each type of the microstate sequence and the corresponding at least one magnetic resonance image to obtain an activated brain area image of each type of microstate sequence of the subject;

[0079] Monte Carlo analysis is performed on the activated brain area images corresponding to the same microstate sequence of all the subjects to obtain the target activated brain area of ​​the microstate sequence.

[0080] According to a third aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to implement the above method when executing the instructions stored in the memory.

[0081] According to a fourth aspect of the present disclosure, a non-volatile computer-readable storage medium is provided, on which computer program instructions are stored, wherein the computer program instructions implement the above method when executed by a processor.

[0082] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above method.

[0083] In an embodiment of the present disclosure, EEG signal data consisting of EEG signals obtained by at least one subject through multiple leads is obtained, and at least one EEG topological map for characterizing the signal intensity difference of each lead position at the same time is drawn. At least one type of microstate and the corresponding characteristic topological map are obtained by clustering the EEG topological map. The microstate sequence corresponding to the subject is determined according to the characteristic topological map corresponding to each type of microstate and the EEG signal data of each subject. At the same time, a magnetic resonance file for characterizing the time series change of the position of the activated brain area in three-dimensional space is determined according to the EEG signal data of each subject, and the target activated brain area corresponding to each type of microstate is determined according to the magnetic resonance file of each subject and the microstate sequence. The present disclosure performs microstate source positioning by fitting the microstate sequence with the magnetic resonance file, and the positioning process is simple and efficient, and the positioning result is accurate.

[0084] Further features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.

[0086] Figure 1 A flow chart showing a microstate source positioning method according to an embodiment of the present disclosure;

[0087] Figure 2 A schematic diagram showing an electroencephalogram according to an embodiment of the present disclosure;

[0088] Figure 3 A schematic diagram showing a microstate sequence according to an embodiment of the present disclosure;

[0089] Figure 4 A schematic diagram showing a brain mapping model according to an embodiment of the present disclosure;

[0090] Figure 5 A schematic diagram showing a microstate source positioning process according to an embodiment of the present disclosure;

[0091] Figure 6 A schematic diagram showing a micro-state source positioning device according to an embodiment of the present disclosure;

[0092] Figure 7 A schematic diagram showing an electronic device according to an embodiment of the present disclosure;

[0093] Figure 8 A schematic diagram showing another electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0094] Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0095] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0096] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. It should be understood by those skilled in the art that the present disclosure can also be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present disclosure.

[0097] The micro-state source positioning method of the embodiment of the present disclosure can be performed by an electronic device such as a terminal device or a server. Among them, the terminal device can be any fixed or mobile terminal such as a user equipment (User Equipment, UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (Personal Digital Assistant, PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. The server can be a single server or a server cluster composed of multiple servers. Any electronic device can implement the micro-state source positioning method of the embodiment of the present disclosure by a processor calling a computer-readable instruction stored in a memory.

[0098] Figure 1 FIG. 5 is a flow chart showing a method for locating a microstate source according to an embodiment of the present disclosure. Figure 1 As shown, the microstate source positioning method of the embodiment of the present disclosure may include the following steps S10-S60.

[0099] Step S10: Obtaining EEG signal data of at least one subject.

[0100] In a possible implementation, the embodiment of the present disclosure can obtain EEG signal data of multiple subjects through an electronic device. The EEG signal data of each subject may include multiple EEG signals obtained through multiple leads, that is, the EEG signal data may be signal data composed of EEG signals obtained by collecting EEG at different positions through multiple leads on the subject's head. The electronic device can directly collect the EEG of the subject's head through the leads, and process it to obtain the EEG signal data, or obtain the EEG signal data collected and processed by other devices and sent. Optionally, when collecting the EEG signal data of each subject, the embodiment of the present disclosure needs to ensure that the subject is in the same state, such as eyes open or eyes closed.

[0101] Optionally, the electronic device can directly obtain the processed EEG signal data. Alternatively, the electronic device can also first collect at least one original EEG signal of a subject through multiple leads attached to the subject's head to obtain original EEG data, and then pre-process the original EEG data of each subject to obtain EEG signal data that is convenient for subsequent processing. Among them, the pre-processing process can be to perform at least one of downsampling, filtering, independent component analysis, bad track difference and re-reference on the original EEG data of each subject to obtain EEG signal data. The downsampling process is used to reduce the sampling rate of the original EEG data to a commonly used value for microstate analysis. The filtering process is used to remove signal noise in the original EEG data, for example, it can be filtered by a second-order zero-phase non-causal bandpass finite impulse response filter (finite-impulser response, FIR) with 1-30Hz as a parameter. Independent component analysis (ICA) is a commonly used processing method in the bioelectric signal processing process, which is used to remove electrooculogram and electrocardiogram artifacts. Bad lead interpolation is used to replace the bad lead with the average value of the surrounding leads to facilitate subsequent analysis when there is a short-circuited lead among multiple leads. The interpolation method used can be spherical spline interpolation. The re-reference process can be performed on the signal data after independent component analysis and bad lead difference processing to adopt the average reference value.

[0102] Step S20: drawing at least one EEG topology map according to the EEG signal data of each subject.

[0103] In a possible implementation, the EEG signal data of each subject can be plotted to obtain one or more EEG topology maps, which are used to characterize the signal strength differences at each lead position at the same time. Optionally, the electronic device can select the moment for each subject to draw the EEG topology map according to a preset rule, so as to draw the corresponding EEG topology map according to the signal strength collected at each lead position at the selected moment. Among them, the preset rule can be to directly determine the moment for each subject to draw the EEG topology map according to a preset frequency. Alternatively, multiple moments are first determined according to a preset time frequency, and the EEG signal data of the subject at each moment is analyzed to obtain the moment when the overall EEG signal meets the preset requirements as the moment for drawing the EEG topology map.

[0104] Optionally, the electronic device can draw a corresponding EEG topology map at the moment when the overall signal strength of the EEG signal data meets the requirements, wherein the signal strength can be determined by calculating the standard deviation of multiple lead signal values. That is, the EEG signal data of each subject can be sampled according to a preset time frequency to obtain the signal values ​​of multiple leads. For each subject, the standard deviation of multiple signal values ​​at the same time is calculated to obtain the corresponding signal strength. The moment corresponding to the highest signal strength of each subject is determined as the target moment. The EEG topology map is drawn according to the EEG signal value of each subject at the target moment and the position of the corresponding lead.

[0105] Furthermore, the EEG topology map can be drawn by marking different lead positions with different colors based on signal strength. That is, multiple intensity ranges and corresponding colors can be determined first, and the original topology map can be initialized according to the position of each lead. For each subject, the original topology map is rendered according to the color corresponding to the intensity range of the EEG signal value corresponding to each lead to obtain the EEG topology map. The color of each pixel in the EEG topology map obtained by the electronic device in the above manner represents the strength of the EEG signal value at the corresponding position of the brain.

[0106] Figure 2 FIG. 1 is a schematic diagram showing an electroencephalogram according to an embodiment of the present disclosure. Figure 2 As shown, the EEG topology map of the embodiment of the present disclosure may include feature points that characterize each lead of the subject's head, and the position of each feature point corresponding to the lead is determined according to the actual physical position of the lead. At the same time, the electronic device can pre-set multiple intensity ranges for limiting the signal value intensity, and the color corresponding to each intensity range. After initializing the original topology map according to the position of each lead, the EEG topology map is obtained by rendering the original topology map based on the color corresponding to the intensity range to which the signal value of each lead position of the subject at the corresponding target moment belongs. In the rendering process, color rendering can be performed starting from the pixel at the feature point position and gradually from dark to light toward nearby pixels.

[0107] Step S30: clustering the EEG topological map to obtain at least one type of microstate and a characteristic topological map corresponding to each type of microstate.

[0108] In a possible implementation, after obtaining at least one EEG topography map of each subject, the electronic device can generate the EEG topography maps of all subjects. Figure 1 Clustering is performed at the same time, and then at least one type of microstate that reflects the instantaneous state of the overall neuronal activity of the brain is obtained based on the clustering results, as well as a characteristic topological map of each type of microstate. Optionally, the disclosed embodiment can perform multiple clustering of the EEG topological map based on different numbers of categories to obtain multiple clustering results. Multiple clustering results are then compared to select the optimal clustering result, so as to determine at least one type of microstate based on the optimal clustering result, as well as a characteristic topological map corresponding to each type of microstate.

[0109] Optionally, the process of clustering electronic devices can be to cluster the EEG topology map based on multiple preset numbers of categories to obtain corresponding clustering results. The clustering results corresponding to each number of categories include microstates of the corresponding number of categories, and an EEG spectrogram as the clustering center of each microstate. The clustering feature value of the clustering result corresponding to each number of categories is calculated, and then the optimal number of categories is determined according to the clustering feature value, and the microstates in the clustering result corresponding to the optimal number of categories are obtained, and the clustering center corresponding to each microstate is determined as the characteristic topology map.

[0110] Further, the clustering method of the embodiment of the present disclosure can be K-means clustering, that is, when the preset number of categories is 1, 2, 3, 4 and 5, five clusterings can be performed, namely, 1-means clustering, 2-means clustering, 3-means clustering, 4-means clustering and 5-means clustering, and clustering results including 1 cluster cluster, 2 cluster clusters, 3 cluster clusters, 4 cluster clusters and 5 cluster clusters are obtained respectively. In the clustering process, the embodiment of the present disclosure can use the Pearson correlation coefficient r of the spatial association degree instead of the Euclidean distance for clustering, and the Pearson correlation coefficient can be calculated by the following formula.

[0111]

[0112] in, and are the mean values ​​of the EEG topological maps in the clusters obtained by clustering, X i and Y i They are any EEG topological maps in the corresponding clusters.

[0113] After obtaining clustering results corresponding to multiple numbers of categories, the disclosed embodiment can evaluate the pros and cons of the clustering results by calculating the clustering feature values ​​corresponding to each clustering result, so as to obtain the best clustering result and determine the corresponding number of categories as the optimal number of categories, and obtain the optimal number of categories of microstates and the characteristic topology map of each microstate according to the clustering result. Among them, the clustering feature value can be obtained by calculating at least one evaluation parameter of the clustering result, and the evaluation parameter can include at least one of GEV, Silhouettes, Davies and Bouldin, Point-Biserial and Dunn. GEV is used to indicate the degree of matching between the microstate and the EEG signal data, which can be determined by calculating the following formula:

[0114]

[0115] Among them, t i represents the time point assigned to the i-th microstate. T represents t i Brain topology map drawn using EEG signal data from . It represents the absolute spatial correlation between microstate i and the EEG topography T at position ti.

[0116] Silhouettes is an indicator used to measure clustering results. It is used to measure the difference between the average distance (a) between each EEG topology map and other EEG topology maps in the cluster to which it belongs and the average distance (b) between each EEG topology map and all EEG topology maps in other clusters. The calculation formula of Silhouette coefficient is: s = (ba) / max (a, b). The value range of Silhouette coefficient is [-1, 1]. The larger the coefficient calculated by the clustering result, the better the clustering effect of the clustering result.

[0117] Davies and Bouldin is an indicator for evaluating the quality of clustering algorithms, which is used to measure the dispersion of EEG topology within each cluster and the difference between the distances between clusters. DBI defines a dispersion value S i , can characterize the dispersion degree of EEG topology in the i-th cluster. The calculation formula is: S i =(1 / T i )*Σ(X j -A i ), where X j A represents the jth EEG topology map in the i-th cluster; i represents the cluster center of the i-th cluster; T i Represents the number of EEG topology maps in the i-th cluster.

[0118] Point-Biserial is an indicator to measure the correlation between continuous variables and binary variables. It is a special form of Pearson's correlation coefficient, which only reflects linear correlation. The calculation formula of Point-biserial correlation coefficient is: PB = (M1-M0) / s*sqrt(n1*n0 / n^2), where M1 and M0 represent the mean of the continuous variable when the binary variable takes the value of 1 and 0 respectively; s represents the standard deviation of the continuous variable; n1 and n0 represent the frequency when the binary variable takes the value of 1 and 0 respectively; n represents the total number of samples.

[0119] Dunn is an indicator used to evaluate the quality of clustering algorithms. It is used to measure the dispersion of EEG topology within clusters and the difference between the distances between clusters. The calculation formula of Dunn index is: D = min(δ(i,j)) / max(Δ(k)), where δ(i,j) represents the distance between the i-th cluster and the j-th cluster, and Δ(k) represents the dispersion of EEG topology within the k-th cluster. The larger the Dunn index, the better the clustering effect.

[0120] After determining the above evaluation parameters corresponding to the clustering results of each number of categories, the electronic device can obtain the corresponding clustering feature value by calculating the weighted sum of the evaluation parameters. Further, by comparing the clustering feature values ​​of each clustering result, the number of categories corresponding to the clustering result with the largest clustering feature value is determined to be the optimal number of categories, and each cluster cluster in the clustering result of the optimal number of categories is determined to be a type of micro-state, and the corresponding cluster center is the characteristic topology map.

[0121] Step S40: obtaining a microstate sequence corresponding to each of the subjects according to the characteristic topological map corresponding to each type of the microstate and the EEG signal data of each of the subjects.

[0122] In a possible implementation, after determining at least one type of microstate and the characteristic topology corresponding to each type of microstate, the electronic device matches the EEG signal data of each subject with the characteristic topology of each type of microstate to obtain the corresponding microstate sequence. In other words, each subject can determine the microstate sequence corresponding to each type of microstate based on the EEG signal data and the characteristic topology of at least one type of microstate.

[0123] Optionally, for each subject, multiple instantaneous topological maps can be drawn by sampling, and then the microstate sequence is determined by matching the instantaneous topological map with the characteristic topological map of each type of microstate. That is, for each subject, the electronic device can first sample the EEG signal data according to a preset time frequency to obtain the signal values ​​of multiple leads. Draw the corresponding instantaneous topological map according to the signal value of each lead at the same time, and then compare the characteristic topological map of each type of microstate with the instantaneous topological map at multiple times to obtain the microstate sequence corresponding to each type of microstate. Among them, the drawing method of the instantaneous topological map can be the same as the drawing method of the EEG topological map. When comparing the characteristic topological map of each type of microstate with the instantaneous topological map at multiple times, it can be determined that the microstate signal value of the instantaneous topological map corresponding to the same time as the characteristic topological map is 1, and the microstate signal value of the instantaneous topological map corresponding to the time different from the characteristic topological map is 0, so as to determine the microstate sequence with a value of 0 or 1.

[0124] Figure 3 A schematic diagram of a microstate sequence according to an embodiment of the present disclosure is shown. Figure 3 As shown, each subject can determine the microstate sequence corresponding to each type of microstate based on the EEG signal data and the characteristic topological diagram of multiple types of microstates. In the case of six types of microstates, the microstate sequences corresponding to the six microstates of each subject can be determined, which are used to characterize the fluctuation of the corresponding microstates. When the subject's brain is in the corresponding microstate, 1 is marked in the microstate sequence, and when the subject's brain is not in the corresponding microstate, 0 is marked in the microstate sequence.

[0125] Step S50: determining a corresponding magnetic resonance image file according to the EEG signal data of each subject.

[0126] In a possible implementation, the electronic device also determines a three-dimensional magnetic resonance file based on the subject's EEG signal data, which is used to characterize the temporal changes in the position of the activated brain area of ​​each subject in the three-dimensional space. Among them, the activated brain area is the area where the EEG signal is relatively active on the cerebral cortex at the corresponding moment. Optionally, the embodiment of the present disclosure can predetermine a brain rendering model, and then perform inverse solution based on the lead position and EEG signal data of each subject to determine the magnetic resonance file of each subject.

[0127] Optionally, the electronic device may first intercept the EEG signal data of each subject according to a preset time window, and process it to obtain multiple interval signal values. Then, according to the interval signal value of each subject, the position information of each lead and the preset brain rendering model, the current distribution of the subject's cerebral cortex is calculated by inverse solution. Then, for each subject, according to the current distribution of the subject, a magnetic resonance image of the brain rendering model with the activated brain area marked is determined, and the activated brain area is the cerebral cortex area with a higher current value of the subject. Finally, a plurality of magnetic resonance images are stored according to the time series relationship to obtain a magnetic resonance file. Among them, the interval signal value can be the sum of multiple signals sampled in the interval, and the preset brain rendering model can be a general three-dimensional model for characterizing the brain structure. The electronic device can first generate a brain rendering model with leads according to the preset brain rendering model and the lead position of the subject's head, and then perform inverse solution based on the interval signal value of the subject to obtain the current distribution of the subject's cerebral cortex.

[0128] For example, the electronic device can first characterize the entire brain through a preset P pixel points, and then calculate the strength combination of multiple pixel points to obtain the current distribution of the subject's cerebral cortex. When the number of leads of the subject is N, assuming that the source localization strength of each pixel point is Q, the inverse solution operator M with a dimension of Q×N can be calculated by inverse solution:

[0129] M=R′G T (GR ′GT +C) -1

[0130] Where G is the gain matrix that associates the source localization strength with the EEG signal data, C is the noise covariance matrix of the EEG signal data, and R′ is the covariance matrix of the source localization strength. The source localization strength is used to characterize the current signal strength of the cerebral cortex. The calculation method of the covariance matrix is ​​the same as that of the noise covariance matrix. The interval signal values ​​corresponding to the multiple segments of EEG signal data of each subject can be determined first, and the interval signal values ​​corresponding to the N leads can be determined as the N×1 matrix F i . Further determine the matrices of the same dimension Then calculate the noise covariance matrix C = F*F T / Nsamples, the dimension of the calculated noise covariance matrix is ​​N×N.

[0131] After obtaining the inverse solver M, we can calculate The current distribution of the subject's cerebral cortex is obtained, where D is a diagonal matrix, and the elements in the matrix are k =γ k λ k ,λ n is the regularization coefficient of the corresponding position in the diagonal matrix, After obtaining the current distribution in the cerebral cortex, the electronic device marks the activated brain area on the brain rendering model that matches the lead position, and then performs transverse slices on the three-dimensional brain rendering model with the activated brain area marked to obtain a magnetic resonance image that includes multiple two-dimensional images representing the differences in brain signal intensity at different locations.

[0132] Figure 4 FIG. 1 is a schematic diagram of a brain rendering model according to an embodiment of the present disclosure. Figure 4 As shown, on the left is a brain rendering model with leads that can be generated by the electronic device based on the preset brain rendering model and the lead position of the subject's head. On the right is a three-dimensional brain rendering model in which the electronic device performs a reverse solution based on the interval signal value of the subject, obtains the current distribution of the subject's cerebral cortex and annotates the activated brain area based on the current distribution. For each subject, the electronic device can determine the interval signal value through the EEG signal data of different time intervals to obtain the brain rendering model corresponding to the time interval, and obtain the corresponding magnetic resonance image by slicing. Furthermore, after time sequence sorting through the corresponding time intervals, the magnetic resonance file corresponding to the subject is obtained.

[0133] Step S60: Determine the target activated brain region corresponding to each type of microstate according to the magnetic resonance image file and microstate sequence of each subject.

[0134] In one possible implementation, after the electronic device determines the magnetic resonance file and microstate sequence of each subject through the above process, since the magnetic resonance images corresponding to the moments in each microstate sequence may be different, the electronic device needs to further fit the magnetic resonance file and the microstate sequence to obtain a common and representative activated brain area corresponding to each type of microstate as the target activated brain area.

[0135] Optionally, for each subject, the electronic device can obtain at least one corresponding magnetic resonance image in the magnetic resonance file according to the microstate sequence of each type of microstate, that is, obtain the magnetic resonance image corresponding to each moment in the microstate sequence. Then, each type of microstate sequence and the corresponding at least one magnetic resonance image are fitted to obtain the activated brain area image of each type of microstate sequence of the subject. Finally, Monte Carlo analysis is performed on the activated brain area images corresponding to the same microstate sequence of all subjects to obtain the target activated brain area of ​​the microstate sequence.

[0136] Furthermore, for each subject, the process of fitting each type of microstate sequence and magnetic resonance image can be to first determine the fitting formula Y=Xβ+∈, where Y is the magnetic resonance image-time series of the subject. X is a matrix composed of multiple types of microstate sequences of the subject, β is a vector used to construct the fitting relationship between the magnetic resonance image and the microstate sequence, and ∈ is an independent Gaussian noise that can be pre-set. Exemplarily, when the subject has five types of microstate sequences, the fitting formula can be determined to be Y=β1·A+β2·B+β3·C+β4·D+β5·C ′ +∈, where A, B, C, D and C ′ are all a type of microstate sequence. The electronic device can determine the optimal solution for each β by the least squares method.

[0137] The electronic device can minimize the actual magnetic resonance image Y and the predicted image based on each β and microstate sequence. The error is determined to correspond to all microstate sequences As a result, it can be calculated by the following formula

[0138]

[0139]

[0140] Considering Gaussian noise ∈ as a normal distribution, the Lagrange multiplier method can be constructed under constraints to solve the value of the matrix β as Among them, σ is the standard error of β, and its formula is N is the number of predicted images obtained from observations, which is equivalent to the number of subjects, and k represents the number of regression functions, that is, the number of microstate categories.

[0141] The fitting parameters are calculated After that, the explained variance can be calculated by The quality of the fitting parameters can be evaluated by . i Represents the magnetic resonance image obtained by source localization analysis at time i. Represents the predicted image obtained by fitting the function at time i. represents the average MRI image in the complete time series. 2 The value of is in the range of [0,1], which represents the percentage of the effect of fitting Y with X. If the evaluation result is not good, the fitting parameters can be re-determined.

[0142] Optionally, after determining each fitting parameter, the electronic device can calculate the activated brain region image corresponding to each microstate through the fitting function. Further, the activated brain region images of all subjects are subjected to Monte Carlo analysis to obtain the central distribution of active brain regions as the target activated brain region. The result of the Monte Carlo analysis is in the formula Y j,p The value of the pth voxel in the three-dimensional image representing the brain structure that represents the composition of the activated brain area image. The Z value obtained by the above formula represents the overall activation level of voxel point p in the source localization result. The final Z value represents the overall activation level of pixel point p in the current microstate. Finally, the Z values ​​of all voxel points are used as the final target activated brain area. Furthermore, the target activated brain area can be visualized in three dimensions.

[0143] Figure 5 FIG. 1 is a schematic diagram showing a microstate source positioning process according to an embodiment of the present disclosure. Figure 5 As shown, the disclosed embodiment can first obtain the EEG signal data of at least one subject, then draw the EEG topology map for clustering to obtain at least one type of microstate, and determine the microstate sequence corresponding to each subject and each type of microstate. At the same time, the corresponding magnetic resonance file is determined by the EEG signal data of the subject, and the target activated brain area corresponding to each type of microstate is obtained by fitting the magnetic resonance file and the microstate sequence.

[0144] Based on the above technical features, the disclosed embodiment can determine the microstate sequence that characterizes the microstate changes in time series and the magnetic resonance file that characterizes the position changes of the activated brain area in time series through the EEG signal data, and accurately determine the position of the activated brain area on the brain in each microstate by fitting the microstate sequence with the magnetic resonance file. At the same time, by determining a type of parameter for each type of microstate during the fitting process, the result is more accurate, the variances of several microstates are independent and do not affect each other, and the final error is smaller. In addition, the calculation process of the disclosed embodiment is simple, which speeds up the source localization process and improves efficiency.

[0145] Figure 6 FIG. 1 is a schematic diagram showing a micro-state source positioning device according to an embodiment of the present disclosure. Figure 6 As shown, the micro-state source positioning device of the embodiment of the present disclosure includes:

[0146] A data acquisition module 60, used to acquire EEG signal data of at least one subject, wherein the EEG signal data includes EEG signals acquired through multiple leads;

[0147] A topology drawing module 61, used for drawing at least one EEG topology map according to the EEG signal data of each subject, wherein the EEG topology map is used to characterize the signal strength difference of each lead position at the same time;

[0148] A clustering module 62, configured to cluster the EEG topological map to obtain at least one type of microstate and a characteristic topological map corresponding to each type of microstate;

[0149] A signal fitting module 63, used to obtain a microstate sequence corresponding to each subject according to the characteristic topological map corresponding to each type of microstate and the EEG signal data of each subject;

[0150] A file determination module 64, for determining a corresponding magnetic resonance file according to the EEG signal data of each subject, wherein the magnetic resonance file is used to characterize the temporal changes of the position of the activated brain region of the corresponding subject in the three-dimensional space;

[0151] The activated brain region determination module 65 is used to determine the target activated brain region corresponding to each type of microstate according to the magnetic resonance image file and microstate sequence of each subject.

[0152] In a possible implementation, the data acquisition module 60 is further configured to:

[0153] Collecting raw EEG data of at least one subject;

[0154] The original EEG data of each subject is preprocessed to obtain EEG signal data.

[0155] In a possible implementation, the data acquisition module 60 is further configured to:

[0156] The original EEG data of each subject is processed by at least one of downsampling, filtering, independent component analysis, bad track difference and re-referencing to obtain EEG signal data.

[0157] In a possible implementation, the topology drawing module 61 is further configured to:

[0158] Sampling the EEG signal data of each subject according to a preset time frequency to obtain signal values ​​of the multiple leads;

[0159] For each of the subjects, calculating the standard deviation of a plurality of the signal values ​​at the same time to obtain the corresponding signal intensity;

[0160] Determine the time corresponding to the highest signal intensity of each subject as the target time;

[0161] An EEG topology map is drawn according to the EEG signal value of each subject at the target moment and the position of the corresponding lead.

[0162] In a possible implementation, the topology drawing module 61 is further configured to:

[0163] Determine multiple intensity ranges and corresponding colors;

[0164] Initializing an original topological map according to the position of each of the leads;

[0165] For each of the subjects, the original topological map is rendered according to the color corresponding to the intensity range of the electroencephalogram signal value corresponding to each of the leads to obtain an electroencephalogram topological map.

[0166] In a possible implementation, the clustering module 62 is further configured to:

[0167] Clustering the EEG topology map based on a plurality of preset numbers of categories to obtain corresponding clustering results, wherein the clustering results include microstates of a corresponding number of categories and an EEG spectrogram as a cluster center of each category of the microstates;

[0168] Calculate the clustering characteristic value of each clustering result corresponding to the number of categories;

[0169] The optimal number of categories is determined according to the clustering characteristic value, and the micro-states in the clustering result corresponding to the optimal number of categories are obtained, and the cluster center corresponding to each of the micro-states is determined as a characteristic topological graph.

[0170] In a possible implementation, the signal fitting module 63 is further configured to:

[0171] For each of the subjects, sampling the EEG signal data according to a preset time frequency to obtain signal values ​​of the multiple leads;

[0172] Draw a corresponding instantaneous topological diagram according to the signal value of each lead at the same moment;

[0173] By comparing the characteristic topological graph of each type of microstate with the instantaneous topological graphs at the multiple moments, a microstate sequence corresponding to each type of microstate is obtained.

[0174] In a possible implementation, the file determination module 64 is further configured to:

[0175] Intercepting the EEG signal data according to a preset time window, and processing to obtain a plurality of interval signal values;

[0176] Calculating the current distribution of the cerebral cortex of the subject according to the interval signal value of each subject, the position information of each lead and a preset brain mapping model;

[0177] For each of the subjects, determining a magnetic resonance image of a brain rendering model with annotated activated brain regions according to the current distribution of the subject, wherein the activated brain regions are regions of the cerebral cortex where the current value of the subject is relatively high;

[0178] The plurality of magnetic resonance images are stored according to a time sequence relationship to obtain a magnetic resonance file.

[0179] In a possible implementation, the activated brain region determination module 65 is further configured to:

[0180] For each of the subjects, acquiring at least one corresponding magnetic resonance image in the magnetic resonance file according to the microstate sequence of each type of microstate;

[0181] Fitting each type of the microstate sequence and the corresponding at least one magnetic resonance image to obtain an activated brain area image of each type of microstate sequence of the subject;

[0182] Monte Carlo analysis is performed on the activated brain area images corresponding to the same microstate sequence of all the subjects to obtain the target activated brain area of ​​the microstate sequence.

[0183] In some embodiments, the functions or modules included in the device provided by the embodiments of the present disclosure can be used to execute the method described in the above method embodiments. The specific implementation can refer to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0184] The embodiment of the present disclosure also provides a computer-readable storage medium on which computer program instructions are stored, and the computer program instructions implement the above method when executed by a processor. The computer-readable storage medium can be a volatile or non-volatile computer-readable storage medium.

[0185] An embodiment of the present disclosure further proposes an electronic device, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to implement the above method when executing the instructions stored in the memory.

[0186] The embodiments of the present disclosure also provide a computer program product, including a computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above method.

[0187] Figure 7A schematic diagram of an electronic device 800 according to an embodiment of the present disclosure is shown. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0188] Reference Figure 7 , the electronic device 800 may include one or more of the following components: a processing component 802 , a memory 804 , a power component 806 , a multimedia component 808 , an audio component 810 , an input / output (I / O) interface 812 , a sensor component 814 , and a communication component 816 .

[0189] The processing component 802 generally controls the overall operation of the electronic device 800, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the above-mentioned method. In addition, the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.

[0190] The memory 804 is configured to store various types of data to support operations on the electronic device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, pictures, videos, etc. The memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0191] The power supply component 806 provides power to the various components of the electronic device 800. The power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 800.

[0192] The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the electronic device 800 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0193] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC), and when the electronic device 800 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 804 or sent via the communication component 816. In some embodiments, the audio component 810 also includes a speaker for outputting audio signals.

[0194] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: home button, volume button, start button, and lock button.

[0195] The sensor assembly 814 includes one or more sensors for providing various aspects of status assessment for the electronic device 800. For example, the sensor assembly 814 can detect the open / closed state of the electronic device 800, the relative positioning of the components, such as the display and keypad of the electronic device 800, and the sensor assembly 814 can also detect the position change of the electronic device 800 or a component of the electronic device 800, the presence or absence of contact between the user and the electronic device 800, the orientation or acceleration / deceleration of the electronic device 800, and the temperature change of the electronic device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0196] The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0197] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above methods.

[0198] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 804 including computer program instructions, which can be executed by a processor 820 of an electronic device 800 to perform the above method.

[0199] Figure 8 1 is a schematic diagram showing another electronic device 1900 according to an embodiment of the present disclosure. For example, the electronic device 1900 may be provided as a server or a terminal device. Figure 8 , the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions executable by the processing component 1922, such as an application. The application stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above method.

[0200] The electronic device 1900 may also include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input / output (I / O) interface 1958. The electronic device 1900 may operate based on an operating system stored in the memory 1932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™ or the like.

[0201] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions, which can be executed by the processing component 1922 of the electronic device 1900 to perform the above method.

[0202] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0203] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0204] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0205] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0206] Various aspects of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0207] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0208] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0209] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of the module, program segment or instruction includes one or more executable instructions for realizing the specified logical function. In some alternative implementations, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of special hardware and computer instructions.

[0210] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A micro-state source positioning method, characterized in that: The method comprises: Acquiring electroencephalogram (EEG) signal data of at least one subject, wherein the EEG signal data includes EEG signals acquired through a plurality of leads; Drawing at least one EEG topography map according to the EEG signal data of each subject, wherein the EEG topography map is used to characterize the signal strength difference of each lead position at the same time; Clustering the EEG topological map to obtain at least one type of microstate and a characteristic topological map corresponding to each type of microstate; According to the characteristic topological map corresponding to each type of the microstate and the EEG signal data of each subject, a microstate sequence corresponding to each subject is obtained; Determine a corresponding magnetic resonance image file according to the electroencephalogram signal data of each subject, wherein the magnetic resonance image file is used to characterize the temporal changes in the position of the activated brain region of the corresponding subject in three-dimensional space; Determining the target activated brain region corresponding to each type of microstate according to the magnetic resonance image file and microstate sequence of each subject; Determining the corresponding magnetic resonance image file according to the EEG signal data of each subject includes: Intercepting the EEG signal data according to a preset time window, and processing to obtain a plurality of interval signal values; Calculating the current distribution of the cerebral cortex of the subject according to the interval signal value of each subject, the position information of each lead and a preset brain mapping model; For each of the subjects, determining a magnetic resonance image of a brain rendering model with annotated activated brain regions according to the current distribution of the subject, wherein the activated brain regions are regions of the cerebral cortex where the current value of the subject is relatively high; The plurality of magnetic resonance images are stored according to a time sequence relationship to obtain a magnetic resonance file.

2. The method according to claim 1, characterized in that The step of obtaining brain electrical signal data of at least one subject comprises: Collecting raw EEG data of at least one subject; The original EEG data of each subject is preprocessed to obtain EEG signal data.

3. The method according to claim 2, characterized in that The preprocessing of the original EEG data of each subject to obtain EEG signal data includes: The original EEG data of each subject is processed by at least one of downsampling, filtering, independent component analysis, bad track difference and re-referencing to obtain EEG signal data.

4. The method according to any one of claims 1 to 3, characterized in that: Drawing at least one EEG topology map according to the EEG signal data of each subject includes: Sampling the EEG signal data of each subject according to a preset time frequency to obtain signal values ​​of the multiple leads; For each of the subjects, calculating the standard deviation of a plurality of the signal values ​​at the same time to obtain the corresponding signal intensity; Determine the time corresponding to the highest signal intensity of each subject as the target time; An EEG topology map is drawn according to the EEG signal value of each subject at the target moment and the position of the corresponding lead.

5. The method according to claim 4, characterized in that Drawing an EEG topology map according to the EEG signal value of each subject at the target time and the position of the corresponding lead includes: Determine multiple intensity ranges and corresponding colors; Initializing an original topological map according to the position of each of the leads; For each of the subjects, the original topological map is rendered according to the color corresponding to the intensity range of the electroencephalogram signal value corresponding to each of the leads to obtain an electroencephalogram topological map.

6. The method according to any one of claims 1 to 3, characterized in that: The step of clustering the EEG topological map to obtain at least one type of microstate and a characteristic topological map corresponding to each type of microstate includes: Clustering the EEG topology map based on a plurality of preset numbers of categories to obtain corresponding clustering results, wherein the clustering results include microstates of a corresponding number of categories and an EEG spectrogram as a cluster center of each category of the microstates; Calculate the clustering characteristic value of each clustering result corresponding to the number of categories; The optimal number of categories is determined according to the clustering characteristic value, and the micro-states in the clustering result corresponding to the optimal number of categories are obtained, and the cluster center corresponding to each of the micro-states is determined as a characteristic topological graph.

7. The method according to any one of claims 1 to 3, characterized in that: The step of obtaining a microstate sequence corresponding to each subject according to the characteristic topological map corresponding to each type of microstate and the EEG signal data of each subject comprises: For each of the subjects, sampling the EEG signal data according to a preset time frequency to obtain signal values ​​of the multiple leads; Draw a corresponding instantaneous topological diagram according to the signal value of each lead at the same moment; By comparing the characteristic topological graph of each type of microstate with the instantaneous topological graphs at the multiple moments, a microstate sequence corresponding to each type of microstate is obtained.

8. The method according to claim 1, characterized in that Determining the target activated brain region corresponding to each type of microstate according to the magnetic resonance image and microstate sequence of each subject includes: For each of the subjects, acquiring at least one corresponding magnetic resonance image in the magnetic resonance file according to the microstate sequence of each type of microstate; Fitting each type of the microstate sequence and the corresponding at least one magnetic resonance image to obtain an activated brain area image of each type of microstate sequence of the subject; Monte Carlo analysis is performed on the activated brain area images corresponding to the same microstate sequence of all the subjects to obtain the target activated brain area of ​​the microstate sequence.

9. A micro-state source positioning device, characterized in that: The device comprises: A data acquisition module, used to acquire EEG signal data of at least one subject, wherein the EEG signal data includes EEG signals acquired through multiple leads; A topology drawing module, used for drawing at least one EEG topology map according to the EEG signal data of each subject, wherein the EEG topology map is used to characterize the signal strength difference of each lead position at the same time; A clustering module, used for clustering the EEG topological map to obtain at least one type of microstate and a characteristic topological map corresponding to each type of microstate; A signal fitting module, used for obtaining a microstate sequence corresponding to each subject according to a characteristic topological map corresponding to each type of microstate and the EEG signal data of each subject; A file determination module, used to determine a corresponding magnetic resonance file according to the EEG signal data of each subject, wherein the magnetic resonance file is used to characterize the temporal changes of the position of the activated brain region of the corresponding subject in three-dimensional space; An activated brain region determination module, used to determine the target activated brain region corresponding to each type of microstate according to the magnetic resonance image and microstate sequence of each subject; The file determination module is further used for: Intercepting the EEG signal data according to a preset time window, and processing to obtain a plurality of interval signal values; Calculating the current distribution of the cerebral cortex of the subject according to the interval signal value of each subject, the position information of each lead and a preset brain mapping model; For each of the subjects, determining a magnetic resonance image of a brain rendering model with annotated activated brain regions according to the current distribution of the subject, wherein the activated brain regions are regions of the cerebral cortex where the current value of the subject is relatively high; The plurality of magnetic resonance images are stored according to a time sequence relationship to obtain a magnetic resonance file.

10. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to implement the method described in any one of claims 1 to 8 when executing the instructions stored in the memory.

11. A non-volatile computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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