Method and device for detecting inter-brain synchrony of multiple persons, equipment and medium

Abstract

The application relates to a multi-person inter-brain synchrony detection method, device, equipment and medium, wherein the method comprises the following steps: acquiring multiple groups of test data, constructing a matrix corresponding to each test data, and obtaining multiple initial matrices; integrating the initial matrices in the space and time dimensions to obtain a target matrix; performing independent component analysis on the target matrix to obtain a mixing matrix and a source component matrix; calculating the initial brain network components of each interaction group based on the mixing matrix and the source component matrix; filtering out target component information from the initial brain network components in a space template mode or a neural activity time sequence mode, and generating a target detection result based on the target component information. The application can distinguish different brain networks involved in inter-brain interaction, reveal the mode characteristics of different brain networks of the brain during inter-brain interaction, and improve the detection accuracy of multi-person inter-brain synchrony.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular to a method, apparatus, device and medium for detecting synchronization among multiple brains. Background Technology

[0002] Hyperscanning is a technique that simultaneously measures changes in brain activity between two (or more) subjects during social interactions. It utilizes brain imaging techniques such as functional magnetic resonance imaging (fMRI), electroencephalography (EEG), or functional near-infrared spectroscopy (fNIRS) to track the neural activity of interacting subjects, allowing researchers to gain a deeper understanding of how brains communicate. Before the advent of hyperscanning, traditional social neuropsychology was limited to observing the overt behavior and brain activity of individual subjects, which was quite limiting because social interaction is a process of interaction between people, not an individual activity. In the past decade or so, the development of hyperscanning has enabled researchers to observe a state of brain-to-brain synchronization that occurs when two or more brains interact. This refers to the phenomenon where the neural activity in a specific brain region changes in conjunction with the participants' brains during various social activities such as conversation, cooperative games, and ensemble playing. Typically, this synchronization is caused by the transmission of information between the participants. The higher the degree of brain-to-brain synchronization, the more efficient the information exchange between individuals.

[0003] Existing methods for assessing inter-brain synchronization can be broadly categorized into four types. The first utilizes the correlation between time-series brain activity, such as inter-subject correlation (ITC), which uses methods like Pearson correlation and Spearman correlation to calculate the similarity between brain activities. The second method transforms neural activity into time-frequency activity and then calculates the coherence at different frequencies, such as wavelet transform coherence. The third method calculates the covariance of phases between different subjects' brain activities, such as phase locking value calculation and inter-subject phase coherence. The fourth method differs from the first three in that it measures the direction of information transmission between brains, with Granger causality being the most common. However, these four commonly used methods typically perform calculations at the channel or voxel level, indicating that they cannot distinguish the different brain networks involved in inter-brain interaction and cannot reveal the pattern characteristics of different brain networks during inter-brain interaction, resulting in low detection accuracy. Summary of the Invention

[0004] The purpose of this application is to provide a method, apparatus, device, and medium for detecting synchronization among multiple brains, so as to improve the accuracy of detecting synchronization among multiple brains.

[0005] To address the aforementioned technical problems, embodiments of this application provide a method for detecting synchronization among multiple brains, comprising:

[0006] Multiple sets of subject data were obtained, and a matrix corresponding to each set of subject data was constructed to obtain multiple initial matrices;

[0007] The initial matrix is ​​integrated in both spatial and temporal dimensions to obtain the target matrix;

[0008] Independent component analysis is performed on the target matrix to obtain the mixture matrix and the source component matrix;

[0009] The initial brain network components for each interaction group are calculated based on the mixture matrix and the source component matrix.

[0010] Using spatial templates or time-series methods of neural activity, target component information is screened from the initial brain network components, and target detection results are generated based on the target component information.

[0011] To address the aforementioned technical problems, embodiments of this application provide a device for detecting multi-person brain synchronization, comprising:

[0012] The test data acquisition unit is used to acquire multiple sets of test data and construct a matrix corresponding to each set of test data to obtain multiple initial matrices;

[0013] The target matrix generation unit is used to integrate the initial matrix in spatial and temporal dimensions to obtain the target matrix;

[0014] An independent component analysis unit is used to perform independent component analysis on the target matrix to obtain a mixture matrix and a source component matrix;

[0015] A brain network component calculation unit is used to calculate the initial brain network components of each interaction group based on the mixing matrix and the source component matrix;

[0016] The target detection result generation unit is used to filter target component information from the initial brain network components using a spatial template method or a time series method of neural activity, and generate target detection results based on the target component information.

[0017] To solve the above-mentioned technical problems, one technical solution adopted by the present invention is to provide a computer device, including one or more processors; and a memory for storing one or more programs, so that the one or more processors implement the method for detecting synchronization between multiple brains as described in any one of the above-mentioned methods.

[0018] To solve the above-mentioned technical problems, one technical solution adopted by the present invention is: a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the method for detecting synchronization among multiple brains as described above.

[0019] This invention provides a method, apparatus, device, and medium for detecting multi-person brain synchrony. The method includes: acquiring multiple sets of subject data and constructing a matrix corresponding to each set of subject data to obtain multiple initial matrices; integrating the initial matrices in spatial and temporal dimensions to obtain a target matrix; performing independent component analysis on the target matrix to obtain a mixed matrix and a source component matrix; calculating the initial brain network components for each interaction group based on the mixed matrix and the source component matrix; using a spatial template approach or a time-series approach of neural activity to filter target component information from the initial brain network components, and generating a target detection result based on the target component information. This invention can distinguish different brain networks involved in brain-brain interaction, revealing the pattern characteristics of different brain networks during brain-brain interaction, which is beneficial for improving the accuracy of detecting multi-person brain synchrony. Attached Figure Description

[0020] To more clearly illustrate the solutions in this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of an implementation of the method for detecting synchronization among multiple brains provided in this application embodiment;

[0022] Figure 2 This is a flowchart illustrating the implementation of a sub-process in the method for detecting inter-brain synchronization provided in this application embodiment;

[0023] Figure 3 This is a flowchart illustrating the implementation of a sub-process in the method for detecting inter-brain synchronization provided in this application embodiment;

[0024] Figure 4 This is a flowchart illustrating the implementation of a sub-process in the method for detecting inter-brain synchronization provided in this application embodiment;

[0025] Figure 5 This is a flowchart illustrating the implementation of a sub-process in the method for detecting inter-brain synchronization provided in this application embodiment;

[0026] Figure 6 This is a flowchart illustrating the implementation of a sub-process in the method for detecting inter-brain synchronization provided in this application embodiment;

[0027] Figure 7 This is a schematic diagram of a multi-brain synchronization detection device provided in an embodiment of this application;

[0028] Figure 8 This is a schematic diagram of the computer device provided in the embodiments of this application. Detailed Implementation

[0029] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings of this application, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings of this application are used to distinguish different objects, not to describe a particular order.

[0030] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0031] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0032] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0033] It should be noted that the method for detecting synchronization between multiple brains provided in this application is generally executed by a server, and correspondingly, the device for detecting synchronization between multiple brains is generally configured in the server.

[0034] Please see Figure 1 , Figure 1 This paper illustrates a specific implementation of a method for detecting synchronization among multiple brains.

[0035] It should be noted that if substantially the same result is obtained, the method of this invention is not based on... Figure 1 Limited to the order of the processes shown, this method includes the following steps:

[0036] S1: Obtain multiple sets of subject data and construct a matrix corresponding to each set of subject data to obtain multiple initial matrices.

[0037] This application's embodiments are based on the principles of Independent Component Analysis (ICA). ICA, based on the assumptions of independence and non-Gaussianity between source signals, can separate signals from different sources in complex signals. In other words, it treats the measured signals of the entire brain as a mixture of signals from different brain networks. By performing ICA on the brain activity of subjects under different tasks or in a resting state, the composition and function of different brain networks and the activity levels of these networks can be determined, thereby inferring which brain network is associated with this cognitive activity. This application's embodiments consider the brains of all individuals during the interaction process as a single entity, called a "hyperbrain." The "hyperbrain network" obtained by separating the hyperbrain network is derived from the synchronization between the subjects' brain networks during social interaction. From this perspective, this application's embodiments can use hyperscanning technology and ICA algorithms to directly separate the neural signals of this hyperbrain into different brain networks, obtaining their participation levels and pattern characteristics in different social interactions. Based on the spatial and temporal characteristics of the isolated brain network, embodiments of this application can combine the subject's behavioral sequence and prior knowledge (the brain regions involved in the cognitive activity) to select one or more of the most likely components.

[0038] Specifically, multiple sets of subject data are acquired, with each set consisting of two participants in fNIRS (functional near-infrared spectroscopy). In one specific embodiment, 27 sets of subject data are acquired, with each set consisting of fNIRS data comprising 32 measurement channels and 2109 sampling points, over a duration of 270 seconds and a sampling frequency of 7.8125 Hz. In this embodiment, a single subject's data is used to construct an initial matrix X. ij In the diagram, i represents 1, 2, ..., n, where n represents the number of participants in each group during the experiment, j represents 1, 2, ..., N, where N represents the total number of groups of participants, and X represents the number of participants in each group. ij Each column represents one sampling point from all channels, and each row represents brain activity measured at all time points for one channel.

[0039] S2: Integrate the initial matrix in both spatial and temporal dimensions to obtain the target matrix.

[0040] Please see Figure 2 , Figure 2 A specific implementation of step S2 is shown below:

[0041] S21: Integrate the initial matrices of the same group of interactors in the spatial dimension to obtain the data matrix of the same group of interactors.

[0042] S22: Connect all the data matrices in the time dimension to obtain the target matrix.

[0043] Specifically, the measured data of the same group of interactors are spatially integrated, that is, the data measured by the same group of interactors on all channels at the same time point are connected to obtain the data matrix X of the same group of interactors. j , where X j for:

[0044]

[0045] Furthermore, in order to extract common brain network components across all interaction groups, embodiments of this application perform matrix X obtained from all interaction groups before applying independent component analysis. j Connecting these over time, we obtain the target matrix X, where X is:

[0046]

[0047] S3: Perform independent component analysis on the target matrix to obtain the mixture matrix and the source component matrix.

[0048] Specifically, the target matrix is ​​subjected to independent component analysis using a preset independent component analysis algorithm to obtain a mixture matrix and a source component matrix.

[0049] Furthermore, a specific implementation of step S3 is provided, including: performing independent component analysis on the target matrix using a preset independent component analysis algorithm to decompose the target matrix into the mixture matrix and the source component matrix, wherein the mixture matrix represents the proportion of each component in each channel, the source component matrix is ​​all the separated components, and each column in the source component matrix represents a different source component.

[0050] Specifically, the preset independent component analysis algorithms include FastICA, InfomaxICA, TDSEP (Temporal Decorrelation Source Separation), etc. In one specific embodiment, the TDSEP algorithm is selected for independent component analysis. In this embodiment, an independent component analysis algorithm is used to perform independent component analysis on the target matrix to decompose the target matrix X into a mixture matrix A. g Source component matrix S g (The superscript g indicates the result obtained from the group ICA analysis). Wherein, the mixture matrix A... gThe source component matrix S represents the proportion of each component in each channel. g It represents all the separated components, with each column in the source component column representing a different source component.

[0051] Where X = A g S g X is the target matrix.

[0052] S4: Calculate the initial brain network components for each interaction group based on the mixing matrix and the source component matrix.

[0053] Specifically, a dual regression method was used to calculate the initial brain network components for each interaction group based on the mixture matrix and the source component matrix.

[0054] Please see Figure 3 , Figure 3 A specific implementation of step S4 is shown below:

[0055] S41: Input the hybrid matrix and the initial matrix in the corresponding interaction group into the first preset formula for calculation to obtain the target source component matrix.

[0056] S42: Input the target source component matrix and the initial matrix in the corresponding interaction group into the second preset formula for calculation to obtain the target mixing matrix.

[0057] S43: Merge the target source component matrix and the target mixing matrix of the same interaction group as the initial brain network component of the interaction group.

[0058] Specifically, the mixing matrix A g and the initial matrix X in the corresponding interaction group j Substitute into the first preset formula: X j =A g S j The target source component matrix S is obtained. j =(A g，T A g ) -1 A g，T X j Then the target source component matrix S j and the initial matrix X in the corresponding interaction group j Enter into the second preset formula: X j =A j S j The target mixing matrix is ​​obtained. Finally, the target source component matrix and target mixture matrix of the same interaction group are merged as the initial brain network components of the interaction group.

[0059] S5: Using a spatial template approach or a time-series approach to neural activity, target component information is screened from the initial brain network components, and target detection results are generated based on the target component information.

[0060] Please see Figure 4 , Figure 4 A specific implementation of step S5 is shown below:

[0061] S51: Calculate the goodness-of-fit index based on the initial brain network components using a spatial template, and generate the target detection result based on the goodness-of-fit index.

[0062] Please see Figure 5 , Figure 5 A specific implementation of step S51 is shown below:

[0063] S511: Convert each channel in the initial brain network component into a brain imaging coordinate system, and select the brain region related to the interactive task based on the brain imaging coordinate system as the target vector. The target vector stores the sequence number of each channel associated with the brain region of interest.

[0064] S512: Standardize the hybrid matrix to generate a standardized hybrid matrix.

[0065] S513: Calculate the goodness-of-fit index based on the target vector and the standardized mixing matrix, and generate the target detection result based on the goodness-of-fit index.

[0066] In this embodiment, a spatial template method is used to calculate the goodness-of-fit index based on the initial brain network components, and the target detection result is generated based on the goodness-of-fit index. Specifically, each channel in the initial brain network components is converted into a brain imaging coordinate system (MNI standard coordinate system). Brain regions related to the interactive task are selected based on prior experience, denoted as target vector r. The target vector r stores the indices of each channel related to the brain region of interest, while other irrelevant channels are denoted as... Then the mixing matrix A g Standardization generates a standardized hybrid matrix ZA g Finally, the goodness-of-fit index is calculated based on the target vector and the standardized mixture matrix. Here, ic represents the component number. Since a larger goodness-of-fit index Gof(ic) indicates that the component is more similar to the spatial template, the target detection result is generated based on the goodness-of-fit index.

[0067] S52: Calculate the Pearson correlation coefficient between the time series of neural activity and the time series of the components based on the initial brain network components using a time series approach for neural activity, and generate the target detection result based on the Pearson correlation coefficient.

[0068] Please see Figure 6 , Figure 6 A specific implementation of step S52 is shown below:

[0069] S521: The neural signals of each interaction group are obtained by performing convolution calculations based on the initial brain network components using a design matrix and hemodynamic response function.

[0070] S522: Calculate the Pearson correlation coefficient between the time series of neural activity and the time series of components based on the neural signals and the initial brain network components.

[0071] S523: Generate the target detection result based on the Pearson correlation coefficient.

[0072] In this embodiment, a time-series approach to neural activity is used to calculate the Pearson correlation coefficient between the time series of neural activity and the time series of components based on initial brain network components, and the target detection result is generated based on the Pearson correlation coefficient. Specifically, the time series T of neural activity and the time series S of components are calculated. g The Pearson correlation coefficient between them is calculated as follows:

[0073] R(ic) = corr(T,S) g (ic,:));

[0074] Here, T is obtained by concatenating the neural signals of each interaction group over time. The neural signals of each interaction group are typically obtained by convolving the design matrix and the hemodynamic response function (HRF). Similarly, since a larger Pearson correlation coefficient R(ic) indicates a greater similarity between the two pairs of data in that interaction group, the target detection result is generated based on the Pearson correlation coefficient R(ic).

[0075] In this embodiment, multiple sets of subject data are acquired, and a matrix corresponding to each set of subject data is constructed to obtain multiple initial matrices. These initial matrices are then integrated in spatial and temporal dimensions to obtain a target matrix. Independent component analysis is performed on the target matrix to obtain a mixed matrix and a source component matrix. Initial brain network components for each interaction group are calculated based on the mixed matrix and the source component matrix. Target component information is screened from the initial brain network components using a spatial template approach or a time-series approach of neural activity, and a target detection result is generated based on the target component information. This embodiment of the invention can distinguish different brain networks involved in interbrain interaction, revealing the pattern characteristics of different brain networks during interbrain interaction, which is beneficial for improving the detection accuracy of multi-person brain synchronization.

[0076] Furthermore, this application's embodiments overcome the limitations of other methods. Instead of artificially dividing the brain into multiple voxels or channels and then calculating the synchronization magnitude of multiple brains on each pair of voxels or channels, it divides the "super brain" during social interaction into several different "super brain networks" and assesses the activity state of these brain networks as a whole. This application's embodiments alleviate the problem of multiple tests. It is not necessary to perform statistical significance tests on the synchronization of each pair of channels or voxels.

[0077] Please refer to Figure 7 As a response to the above Figure 1 The implementation of the method shown in this application provides an embodiment of a device for detecting synchronization among multiple brains, which is similar to... Figure 1 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0078] like Figure 7 As shown, the multi-person brain synchronization detection device of this embodiment includes: a test data acquisition unit 61, a target matrix generation unit 62, an independent component analysis unit 63, a brain network component calculation unit 64, and a target detection result generation unit 65, wherein:

[0079] The test data acquisition unit 61 is used to acquire multiple sets of test data and construct a matrix corresponding to each set of test data to obtain multiple initial matrices;

[0080] The target matrix generation unit 62 is used to integrate the initial matrix in spatial and temporal dimensions to obtain the target matrix;

[0081] Independent component analysis unit 63 is used to perform independent component analysis on the target matrix to obtain a mixture matrix and a source component matrix;

[0082] Brain network component calculation unit 64 is used to calculate the initial brain network components of each interaction group based on the mixing matrix and the source component matrix;

[0083] The target detection result generation unit 65 is used to select target component information from the initial brain network components using a spatial template method or a time series method of neural activity, and generate target detection results based on the target component information.

[0084] Furthermore, the target matrix generation unit 62 includes:

[0085] The spatial dimension integration unit is used to integrate the initial matrix of the same group of interactors in the spatial dimension to obtain the data matrix of the same group of interactors;

[0086] The time-dimension connection unit is used to connect all the data matrices in the time dimension to obtain the target matrix.

[0087] Furthermore, the independent component analysis unit 63 includes:

[0088] The matrix decomposition unit is used to perform independent component analysis on the target matrix using a preset independent component analysis algorithm, so as to decompose the target matrix into the mixture matrix and the source component matrix, wherein the mixture matrix represents the proportion of each component in each channel, and the source component matrix is ​​all the separated components, and each column of the source component matrix represents a different source component.

[0089] Furthermore, the brain network component calculation unit 64 includes:

[0090] The first calculation unit is used to input the mixing matrix and the initial matrix in the corresponding interaction group into the first preset formula for calculation to obtain the target source component matrix;

[0091] The second calculation unit is used to input the target source component matrix and the initial matrix in the corresponding interaction group into the second preset formula for calculation to obtain the target mixing matrix;

[0092] A brain network component generation unit is used to merge the target source component matrix and the target mixing matrix of the same interaction group as the initial brain network component of the interaction group.

[0093] Furthermore, the target detection result generation unit 65 includes:

[0094] The first detection unit is used to calculate a goodness-of-fit index based on the initial brain network components using a spatial template, and to generate the target detection result based on the goodness-of-fit index; or,

[0095] The second detection unit is used to calculate the Pearson correlation coefficient between the time series of neural activity and the time series of the components based on the initial brain network components using a time series approach of neural activity, and to generate the target detection result based on the Pearson correlation coefficient.

[0096] Furthermore, the first detection unit includes:

[0097] A brain imaging coordinate system transformation unit is used to convert each channel in the initial brain network component into a brain imaging coordinate system, and select brain regions related to the interactive task based on the brain imaging coordinate system as target vectors, wherein the target vectors store the sequence numbers of each channel related to the brain region of interest.

[0098] A matrix normalization unit is used to normalize the hybrid matrix to generate a normalized hybrid matrix;

[0099] The goodness-of-fit index calculation unit is used to calculate the goodness-of-fit index based on the target vector and the standardized mixing matrix, and to generate the target detection result based on the goodness-of-fit index.

[0100] Furthermore, the second detection unit includes:

[0101] The neural signal computation unit is used to perform convolution calculations based on the initial brain network components by designing matrices and hemodynamic response functions to obtain the neural signals of each interaction group.

[0102] The Pearson correlation coefficient calculation unit is used to calculate the Pearson correlation coefficient between the time series of neural activity and the time series of components based on the neural signal and the initial brain network components.

[0103] The detection result generation unit is used to generate the target detection result based on the Pearson correlation coefficient.

[0104] In this embodiment, multiple sets of subject data are acquired, and a matrix corresponding to each set of subject data is constructed to obtain multiple initial matrices. These initial matrices are then integrated in spatial and temporal dimensions to obtain a target matrix. Independent component analysis is performed on the target matrix to obtain a mixed matrix and a source component matrix. Initial brain network components for each interaction group are calculated based on the mixed matrix and the source component matrix. Target component information is screened from the initial brain network components using a spatial template approach or a time-series approach of neural activity, and a target detection result is generated based on the target component information. This embodiment of the invention can distinguish different brain networks involved in interbrain interaction, revealing the pattern characteristics of different brain networks during interbrain interaction, which is beneficial for improving the detection accuracy of multi-person brain synchronization.

[0105] To address the aforementioned technical problems, embodiments of this application also provide a computer device. Please refer to [link / reference needed] for details. Figure 8 , Figure 8 This is a basic structural block diagram of the computer device in this embodiment.

[0106] Computer device 7 includes a memory 71, a processor 72, and a network interface 73 that are interconnected via a system bus. It should be noted that only a computer device 7 with these three components (memory 71, processor 72, and network interface 73) is shown in the figure; however, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0107] Computer devices can include desktop computers, laptops, handheld computers, and cloud servers. These devices allow for human-computer interaction with users through methods such as keyboards, mice, remote controls, touchpads, or voice-activated devices.

[0108] The memory 71 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 71 may be an internal storage unit of the computer device 7, such as the hard disk or memory of the computer device 7. In other embodiments, the memory 71 may also be an external storage device of the computer device 7, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 7. Of course, the memory 71 may also include both internal storage units and external storage devices of the computer device 7. In this embodiment, the memory 71 is typically used to store the operating system and various application software installed on the computer device 7, such as the program code of a method for detecting synchronization between multiple brains. In addition, the memory 71 may also be used to temporarily store various types of data that have been output or will be output.

[0109] In some embodiments, processor 72 may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. This processor 72 is typically used to control the overall operation of computer device 7. In this embodiment, processor 72 is used to run program code stored in memory 71 or process data, for example, to run the program code of the above-described method for detecting multi-brain synchronization, to implement various embodiments of the method for detecting multi-brain synchronization.

[0110] The network interface 73 may include a wireless network interface or a wired network interface, which is typically used to establish a communication connection between the computer device 7 and other electronic devices.

[0111] This application also provides another embodiment, namely, a computer-readable storage medium storing a computer program that can be executed by at least one processor to cause the at least one processor to perform the steps of the method for detecting synchronization among multiple brains as described above.

[0112] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods of the various embodiments of this application.

[0113] Obviously, the embodiments described above are only some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the patent scope of this application. This application can be implemented in many different forms; rather, the purpose of providing these embodiments is to provide a more thorough and comprehensive understanding of the disclosure of this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this application.

Claims

1. A method for detecting synchronization among multiple brains, characterized in that, include: Multiple sets of subject data were obtained, and a matrix corresponding to each set of subject data was constructed to obtain multiple initial matrices; The initial matrix is ​​integrated in both spatial and temporal dimensions to obtain the target matrix; Independent component analysis is performed on the target matrix to obtain the mixture matrix and the source component matrix; The initial brain network components for each interaction group are calculated based on the mixture matrix and the source component matrix. Using spatial templates or time-series methods of neural activity, target component information is screened from the initial brain network components, and target detection results are generated based on the target component information.

2. The method for detecting synchronization among multiple brains according to claim 1, characterized in that, The step of integrating the initial matrix in spatial and temporal dimensions to obtain the target matrix includes: The initial matrices of the same group of interactors are integrated in the spatial dimension to obtain the data matrix of the same group of interactors; The target matrix is ​​obtained by concatenating all the data matrices along the time dimension.

3. The method for detecting synchronization among multiple brains according to claim 1, characterized in that, The step of performing independent component analysis on the target matrix to obtain the mixture matrix and the source component matrix includes: The target matrix is ​​subjected to independent component analysis using a preset algorithm to decompose it into a mixture matrix and a source component matrix. The mixture matrix represents the proportion of each component in each channel, and the source component matrix contains all the separated components. Each column in the source component matrix represents a different source component.

4. The method for detecting synchronization among multiple brains according to claim 1, characterized in that, The calculation of the initial brain network components for each interaction group based on the mixture matrix and the source component matrix includes: The mixing matrix and the initial matrix in the corresponding interaction group are input into the first preset formula for calculation to obtain the target source component matrix; The target source component matrix and the initial matrix in the corresponding interaction group are input into the second preset formula for calculation to obtain the target mixing matrix; The target source component matrix and the target mixing matrix of the same interaction group are merged to form the initial brain network component of the interaction group.

5. The method for detecting synchronization among multiple brains according to any one of claims 1 to 4, characterized in that, The process of using spatial templates or time-series methods of neural activity to screen target component information from the initial brain network components and generating target detection results based on the target component information includes: A goodness-of-fit index is calculated based on the initial brain network components using a spatial template approach, and the target detection result is generated based on the goodness-of-fit index; or, The Pearson correlation coefficient between the time series of neural activity and the time series of components is calculated based on the initial brain network components using a time series approach, and the target detection result is generated based on the Pearson correlation coefficient.

6. The method for detecting synchronization among multiple brains according to claim 5, characterized in that, The step of calculating a goodness-of-fit index based on the initial brain network components using a spatial template method, and generating the target detection result based on the goodness-of-fit index, includes: Each channel in the initial brain network component is converted into a brain imaging coordinate system, and brain regions related to the interactive task are selected based on the brain imaging coordinate system as target vectors, wherein the target vectors store the sequence numbers of each channel related to the brain region of interest. The mixture matrix is ​​standardized to generate a standardized mixture matrix; The goodness-of-fit index is calculated based on the target vector and the standardized mixing matrix, and the target detection result is generated based on the goodness-of-fit index.

7. The method for detecting synchronization among multiple brains according to claim 5, characterized in that, The method of calculating the Pearson correlation coefficient between the time series of neural activity and the time series of components based on the initial brain network components using a time series approach, and generating the target detection result based on the Pearson correlation coefficient, includes: The neural signals of each interaction group are obtained by convolution calculation based on the initial brain network components using designed matrices and hemodynamic response functions. The Pearson correlation coefficient between the time series of neural activity and the time series of components is calculated based on the neural signals and the initial brain network components. The target detection result is generated based on the Pearson correlation coefficient.

8. A device for detecting synchronization among multiple brains, characterized in that, include: The test data acquisition unit is used to acquire multiple sets of test data and construct a matrix corresponding to each set of test data to obtain multiple initial matrices; The target matrix generation unit is used to integrate the initial matrix in spatial and temporal dimensions to obtain the target matrix; An independent component analysis unit is used to perform independent component analysis on the target matrix to obtain a mixture matrix and a source component matrix; A brain network component calculation unit is used to calculate the initial brain network components of each interaction group based on the mixing matrix and the source component matrix; The target detection result generation unit is used to filter target component information from the initial brain network components using a spatial template method or a time series method of neural activity, and generate target detection results based on the target component information.

9. A computer device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method for detecting synchronization among multiple brains as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method for detecting synchronization among multiple brains as described in any one of claims 1 to 7.

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