CN117357132A - Task execution method and device based on multi-layer brain network node participation coefficient - Google Patents
Task execution method and device based on multi-layer brain network node participation coefficient Download PDFInfo
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Abstract
The specification discloses a task execution method and device based on multi-layer brain network node participation coefficients. The task execution method comprises the following steps: firstly, brain signal data are acquired so as to obtain brain signal sequences corresponding to all nodes in each preset time period according to the brain signals of all nodes in the time period. For each node, according to the brain signal sequence of the node in each time period and the brain signal sequences of other nodes in each time period, determining the node connectivity of the node with other nodes in the same time period and the node connectivity of the node with other nodes in different time periods, further determining the multi-participation coefficient of each node in brain activity, wherein the multi-participation coefficient corresponding to the node is used for representing the connectivity condition of the brain region corresponding to the node with other brain regions in the brain activity, and then executing a target task according to the multi-participation coefficient corresponding to each node.
Description
Technical Field
The present disclosure relates to the field of neuroscience and computer technology, and in particular, to a task execution method and apparatus based on multi-layer brain network node participation coefficients.
Background
With the rapid development of neuroscience, research into the brain is also becoming more intensive, and multi-layer brain networks have been developed for better research and analysis of brain functions and structures. Wherein the multi-layer brain network divides the brain into a plurality of brain regions, and the brain regions interact with each other through connection among neurons. Each brain region is abstracted into a node, and the communication process between different brain regions of the brain corresponds to the communication process between the nodes, so that the brain can be regarded as a complex network.
At present, the node participation coefficient of each node can be obtained according to the brain signal data of each node collected in the brain activity to measure the connection condition of each node and other nodes, so that the brain is researched and analyzed. However, the node participation coefficient obtained at present is not accurate enough, and the connection condition between each brain region in the brain cannot be analyzed correctly.
Based on the above, how to improve the accuracy of the node participation coefficients in the multi-layer brain network so as to perform more accurate analysis on the connection condition between each brain region in the brain is a problem to be solved.
Disclosure of Invention
The specification provides a task execution method and device based on multi-layer brain network node participation coefficients, so as to partially solve the problems existing in the prior art.
The technical scheme adopted in the specification is as follows:
the present specification provides a task execution method based on multi-layer brain network node participation coefficients, wherein a plurality of brain areas are divided in the brain of a user, each brain area corresponds to different nodes in a preset brain network diagram, and the task execution method comprises the following steps:
acquiring brain signal data to obtain brain signal sequences corresponding to nodes in each time period according to brain signals of the nodes in each time period for each preset time period, wherein at least partial time periods in each time period are overlapped in part;
for each node, determining node connectivity of the node with other nodes in the same time period according to the brain signal sequence of the node in each time period and the brain signal sequences of other nodes in each time period, wherein the node connectivity is used as a first connectivity corresponding to the node, and determining node connectivity of the node with other nodes in different time periods, and the node connectivity is used as a second connectivity corresponding to the node;
determining a multi-participation coefficient of each node in the brain activity according to the first connectivity and the second connectivity corresponding to each node, wherein the multi-participation coefficient corresponding to each node is used for representing the communication condition of the brain region corresponding to the node with other brain regions in the brain activity;
And executing the target task according to the multi-participation coefficient corresponding to each node.
Optionally, acquiring brain signal data to obtain, for each preset time period, a brain signal sequence corresponding to each node in the time period according to brain signals of each node in the time period, where the brain signal sequence specifically includes:
and acquiring brain signal data, so as to sample the brain signal data of each node in each preset time period according to a time window corresponding to the preset time period, so as to obtain a brain signal sequence corresponding to each node in the time period.
Optionally, determining the node connectivity of the node with other nodes in the same time period as the first connectivity corresponding to the node specifically includes:
for each node, determining the association degree between the brain signal sequence of the node and the brain signal sequences of other nodes in the same time period as a first association degree corresponding to the node;
and determining an intra-layer adjacency matrix according to the first association degree corresponding to each node, wherein for each matrix value contained in the intra-layer adjacency matrix, the matrix value is used for representing the first connectivity between two nodes corresponding to the matrix value in the same time period.
Optionally, determining the node connectivity of the node with other nodes in different time periods as the second connectivity corresponding to the node specifically includes:
for each node, determining the association degree between the brain signal sequence of the node and the brain signal sequences of other nodes in different time periods, and taking the association degree as a second association degree corresponding to the node;
and determining an interlayer adjacency matrix according to the second association degree corresponding to each node, wherein the matrix value is used for representing the second connectivity between two nodes corresponding to the matrix value in different time periods for each matrix value contained in the interlayer adjacency matrix.
Optionally, determining the multi-participation coefficient of each node in the brain activity according to the first connectivity and the second connectivity corresponding to each node specifically includes:
and aiming at each node, obtaining a multi-participation coefficient of the node in brain activity according to the determined total number of links of the node and other nodes, the first connectivity corresponding to the node and the second connectivity corresponding to the node, wherein the total number of links refers to the sum of links formed when the node is linked with other nodes.
Optionally, the determined total number of links between the node and other nodes specifically includes:
For each node, determining the association degree between the brain signal sequence of the node and the brain signal sequences of other nodes in the same time period as a first association degree corresponding to the node, and determining the association degree between the brain signal sequence of the node and the brain signal sequences of other nodes in different time periods as a second association degree corresponding to the node;
for each other node, if the first association degree between the node and the other node is determined to be greater than a preset threshold value or the second association degree between the node and the other node is determined to be greater than a preset threshold value, determining that a link exists between the node and the other node;
and determining the total number of links according to the links which are determined to exist between the node and each other node.
The specification provides a task execution device based on multi-layer brain network node participation coefficients, comprising:
the acquisition module is used for acquiring brain signal data so as to obtain brain signal sequences corresponding to all nodes in each time period according to the brain signals of all nodes in each time period for each preset time period, wherein at least part of time periods in each time period have partial time overlapping;
A determining module, configured to determine, for each node, a node connectivity of the node with other nodes in the same time period according to the brain signal sequence of the node in each time period and the brain signal sequences of other nodes in each time period, as a first connectivity corresponding to the node, determining node connectivity of the node with other nodes in different time periods, and as a second connectivity corresponding to the node;
the calculation module is used for determining a multi-participation coefficient of each node in the brain activity according to the first connectivity and the second connectivity corresponding to each node, and for each node, the multi-participation coefficient corresponding to the node is used for representing the communication condition of the brain region corresponding to the node with other brain regions in the brain activity;
and the execution module is used for executing the target task according to the multi-participation coefficient corresponding to each node.
Optionally, the determining module is specifically configured to determine, for each node, a degree of association between the brain signal sequence of the node and the brain signal sequences of other nodes in the same time period, as a first degree of association corresponding to the node; and determining an intra-layer adjacency matrix according to the first association degree corresponding to each node, wherein for each matrix value contained in the intra-layer adjacency matrix, the matrix value is used for representing the first connectivity between two nodes corresponding to the matrix value in the same time period.
The present specification provides a computer readable storage medium storing a computer program which when executed by a processor implements the above-described method of task execution based on multi-layer brain network node participation coefficients.
The present specification provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the above method of task execution based on coefficients of participation of a multi-layer brain network node when executing the program.
The above-mentioned at least one technical scheme that this specification adopted can reach following beneficial effect:
in the task execution method based on the multi-layer brain network node participation coefficient provided by the specification, brain signal data is acquired first so as to obtain brain signal sequences corresponding to all nodes in each preset time period according to the brain signals of all nodes in the time period. For each node, according to the brain signal sequence of the node in each time period and the brain signal sequences of other nodes in each time period, determining the node connectivity of the node with other nodes in the same time period and the node connectivity of the node with other nodes in different time periods, further determining the multi-participation coefficient of each node in brain activity, wherein the multi-participation coefficient corresponding to the node is used for representing the connectivity condition of the brain region corresponding to the node with other brain regions in the brain activity, and then executing a target task according to the multi-participation coefficient corresponding to each node.
According to the task execution method based on the multi-layer brain network node participation coefficients, according to the brain signal sequence of the node in each time period and the brain signal sequences of other nodes in each time period, the node connectivity of the node with other nodes in the same time period and the node connectivity of the node with other nodes in different time periods are determined, and then the multi-participation coefficients of each node in brain activities are determined.
Drawings
The accompanying drawings, which are included to provide a further understanding of the specification, illustrate and explain the exemplary embodiments of the present specification and their description, are not intended to limit the specification unduly. In the drawings:
fig. 1 is a schematic flow chart of a task execution method based on a multi-layer brain network node participation coefficient provided in the present specification;
Fig. 2 is a schematic diagram of a process for obtaining brain signal sequences corresponding to each node in a preset time period according to the present disclosure;
fig. 3 is a schematic diagram of a task execution device based on a multi-layer brain network node participation coefficient provided in the present specification;
fig. 4 is a schematic view of an electronic device corresponding to fig. 1 provided in the present specification.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the present specification more apparent, the technical solutions of the present specification will be clearly and completely described below with reference to specific embodiments of the present specification and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present specification. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are intended to be within the scope of the present disclosure.
The following describes in detail the technical solutions provided by the embodiments of the present specification with reference to the accompanying drawings.
Fig. 1 is a flow chart of a task execution method based on a multi-layer brain network node participation coefficient provided in the present specification, including the following steps:
s101: and acquiring brain signal data to obtain brain signal sequences corresponding to all nodes in each time period according to the brain signals of all nodes in the time period for each preset time period, wherein at least part of time periods in each time period have partial time overlapping.
In a practical scenario, the brain is a complex system consisting of multiple regions, and a multi-layer brain network has been developed for better study and analysis of the brain's function and structure, as well as the connection of the regions in brain activity. Wherein the multi-layer brain network divides the brain into a plurality of brain regions, and the brain regions interact with each other through connection among neurons. Each brain region is abstracted into a node, and the communication process between different brain regions of the brain corresponds to the communication process between the nodes, so that the brain can be regarded as a complex network.
The Multi-participation coefficient (Multi-Participation Coefficient, MPC) is a concept in the theory of a complex network, and in this specification, the connection condition of each node with other nodes in layers and among layers is measured by calculating the Multi-participation coefficient of each node in a Multi-layer brain network, wherein the connection condition of each node in layers corresponds to the connection condition of each node with other nodes in the same time period, and the connection condition of each node in layers corresponds to the connection condition of each node with other nodes in different time periods. The research on the multi-participation degree of each node of the multi-layer brain network is of great significance for revealing the property of the brain network.
However, the current method of calculating the multi-participation coefficients does not obtain accurate results, and cannot make a correct judgment on the connection condition of each brain region in brain activity, and further cannot comprehensively evaluate the connection condition of each node with other nodes in layers and between layers.
Based on this, the present disclosure provides a task execution method based on multi-layer brain network node participation coefficients, so as to obtain more accurate multi-participation coefficients to accurately analyze the connection condition between brain regions, the present disclosure needs to obtain brain signal data first, so as to sample, for each preset time period, the brain signal data of each node in the time period according to a time window corresponding to the preset time period, so as to obtain a brain signal sequence corresponding to each node in the time period. And for each node, determining the node connectivity of the node with other nodes in the same time period as a first connectivity according to the brain signal sequence of the node in each time period and the brain signal sequences of other nodes in each time period, and determining the node connectivity of the node with other nodes in different time periods as a second connectivity. And further, according to the determined total number of links between the node and other nodes, the first connectivity corresponding to the node and the second connectivity corresponding to the node, obtaining a multi-participation coefficient of the node in brain activity, wherein the multi-participation coefficient corresponding to the node is used for representing the communication condition of a brain region corresponding to the node in brain activity and other brain regions, and then executing a target task according to the multi-participation coefficient corresponding to each node.
Specifically, the manner in which the server acquires brain signal data may be magnetic resonance imaging (Magnetic Resonance Imaging, MRI), functional magnetic resonance imaging (functional Magnetic Resonance Imaging, fMRI), or the like. In this specification, fMRI is used to obtain brain signal data.
fMRI is a technique for studying brain functional activities using magnetic resonance techniques. When brain neurons are active, local blood flow changes, thereby causing changes in blood oxygen levels. fMRI can reflect the activity of different areas of the brain by detecting the change of blood oxygen level, and further image the brain. The server obtains brain signal data based on data on blood oxygen level changes required to construct a brain image.
Further, in processing brain signal data, each brain region can be considered as a node, and the node set is denoted as v= {,/>,…/>}, wherein->Represents the i-th node in the multi-layer brain network, and n= |v| represents the total number of nodes contained in each multi-layer brain network. In the present specification, brain signal data is obtained through fMRI, so that for each preset time period, according to a time window corresponding to the preset time period, brain signal data of each node in the time period is sampled, and finally, a brain signal sequence corresponding to each node in the time period is obtained, as shown in fig. 2.
Fig. 2 is a schematic diagram of a process for obtaining brain signal sequences corresponding to each node in a preset time period according to the present disclosure.
The horizontal axis of the coordinate axis in fig. 2 represents time variation, the vertical axis represents variation of blood oxygen level in blood, and a plurality of curves shown in fig. 2 correspond to a plurality of nodes. In this specification, brain signal data of each node is sampled based on a window sliding mechanism. Specifically, the server sets the appropriate window size and step size according to the total length of the brain signal. In order to ensure that the finally obtained multi-participation coefficient is more accurate, a part of data of the whole brain signal data obtained by default is a noise part, when the server samples the brain signal data, the server can firstly remove the noise part from the head of the whole brain signal data, then take a signal sequence with the same length as the window size, mark the signal sequence as a first window (namely a first box in fig. 2), after the first window is taken, move the window backwards by a step distance, mark the window as a second window (namely a second box in fig. 2), and sequentially take k windows according to the step size and the size of the window so as to obtain the brain signal sequence corresponding to each node in k time periods.
S102: for each node, according to the brain signal sequence of the node in each time period and the brain signal sequences of other nodes in each time period, determining the node connectivity of the node with other nodes in the same time period as the first connectivity corresponding to the node, and determining the node connectivity of the node with other nodes in different time periods as the second connectivity corresponding to the node.
In this specification, for each node, the node may have a connection with other nodes in the same time period or different time periods, so it is necessary to calculate the first connectivity between the node and other nodes in the same time period and the second connectivity between the node and other nodes in different time periods, so that the resulting multi-participation coefficient is more accurate.
Specifically, for a first connectivity between each node and other nodes in the same time period, determining, for each node, a degree of association between a brain signal sequence of the node and brain signal sequences of other nodes in the same time period as a first degree of association corresponding to the node, and determining an intra-layer adjacency matrix according to the first degree of association corresponding to each node, wherein, for each matrix value contained in the intra-layer adjacency matrix, the matrix value is used for representing the first connectivity between two nodes corresponding to the matrix value in the same time period.
That is, in the present specification, forFor node (++>) The server may calculate the same time window m (/ -for)>) The next two nodes->And->Pearson coefficients of inter-brain signal sequences (+.>And is also provided with) To obtain +.>The degree of association between the brain signal sequence of a node and the brain signal sequences of other nodes is taken as a first degree of association corresponding to the node. And further obtaining an intra-layer adjacency matrix according to the first association degree of each node. Wherein for each matrix value +.>For indicating->Node and->First connectivity between nodes at an mth layer. It should be noted that the mth layer is understood to be below the mth time window, then +.>Can be indicated as->Node and->A first connectivity between nodes under an mth time window.
And for the second connectivity of each node with other nodes in different time periods, determining the association degree between the brain signal sequence of the node and the brain signal sequences of other nodes in different time periods as the second association degree corresponding to the node. And determining an interlayer adjacent matrix according to the second association degree corresponding to each node, wherein the matrix value is used for representing the second connectivity between two nodes corresponding to the matrix value in different time periods for each matrix value contained in the interlayer adjacent matrix.
That is, in the present specification, forFor node (++>) The server may calculate different time windows m (=>) And n ()>And->) The next two nodes->And->Pearson coefficients of inter-brain signal sequences (+.>And->) To obtain +.>The degree of association between the brain signal sequence of a node and the brain signal sequences of other nodes is taken as a second degree of association corresponding to the node. And further obtaining an interlayer adjacency matrix according to the second association degree of each node. Wherein for each matrix value +.>For indicating->Node and->Between nodes at the mth layer and the nth (>) A second degree of connectivity of the layers. It should be noted that the mth layer is understood to be under the mth time window, and the nth layer is understood to be under the nth time window, then +.>Can refer to +.>Node +.>And a second degree of connectivity between the nodes.
Further, the server composes the intra-layer adjacency matrix and the inter-layer adjacency matrix into a multi-layer adjacency matrix W, wherein the multi-layer adjacency matrix is used for representing connection conditions of nodes in the multi-layer brain network in the intra-layer and the inter-layer.
In addition, the server determines the degree of correlation between the brain signal sequences of each node and the brain signal sequences of other nodes in the same time period or different time periods, which is to say, when the degree of communication between two nodes is higher, the waveforms of the brain signal sequences corresponding to the two nodes are more similar, and the degree of similarity between the brain signal sequences of the two nodes is higher. Therefore, based on the above principle, the above-mentioned method for obtaining the correlation between brain signal sequences of two nodes by calculating pearson coefficients of the brain signal sequences of two nodes in this specification is just one way, and there are various methods for actually calculating the correlation according to the similarity, for example: cosine distance, euclidean distance, KL hash, etc., are not particularly limited herein.
S103: and determining a multi-participation coefficient of each node in the brain activity according to the first connectivity and the second connectivity corresponding to each node, wherein the multi-participation coefficient corresponding to each node is used for representing the communication condition of the brain region corresponding to the node with other brain regions in the brain activity.
In this specification, for each node, the server needs to obtain a multi-participation coefficient of the node in brain activity according to the total number of links between the node and other nodes, the first connectivity corresponding to the node, and the second connectivity corresponding to the node.
Specifically, for each node, the total number of links refers to the sum of links formed when the node has a link with other nodes. The server may determine whether the first degree of association between the node and the other node is greater than a preset threshold, or determine whether the second degree of association between the node and the other node is greater than a preset threshold, and determine the total number of links according to the determined links between the node and each other node when at least one of the first degree of association and the second degree of association between the node and the other node is greater than the preset threshold, which indicates that a link exists between the node and the other node.
The server may also determine whether a link exists between the node and the other node according to whether the first association degree and the second association degree between the node and the other node are greater than a preset threshold. For example, the server may determine, according to the pearson coefficients of the brain signal sequences of the two nodes, that the matrix values in the intra-layer adjacency matrix and the inter-layer adjacency matrix are both between 0 and 1, that is, any one matrix value is used to represent a first connectivity between the two nodes corresponding to the matrix value in the same period or a second connectivity in a different period, where the weaker the first connectivity or the second connectivity tends to be 0, the stronger the connectivity between the two nodes tends to be 1.
Thus, when determining whether a link exists between the node and the other node, assuming that the preset threshold is 0, it can be considered that as long as the matrix value is greater than 0, it is representative that a link exists between the node and the other node. Therefore, when there is a connection between two nodes, the corresponding matrix value is expressed as 1 in the intra-layer adjacent matrix and the inter-layer adjacent matrix, and when there is no connection, it is expressed as 0, and the intra-layer adjacent matrix and the inter-layer adjacent matrix are processed And, setting the value of matrix value greater than 0 in the intra-layer adjacent matrix and the inter-layer adjacent matrix to be 1 and invariable equal to 0, converting the multi-layer adjacent matrix W into a multi-layer adjacent matrix only representing whether links exist between nodes or not。
It should be noted that, in practical application, the first connectivity and the second connectivity between some nodes tend to be 0, so that, in order to avoid that the connections with small connectivity are also counted into the total number of links, and further the accuracy of the multi-participation coefficient is reduced, for the multi-layer adjacency matrix W, the preset threshold value may also be a number between 0 and 1, so that the connections with small connectivity can be screened out, and only the links with strong connection capability are reserved.
Further, the calculation is performed in the same time periodTotal number of links when a node has links with other nodes:
wherein,indicating +.>The total number of links the node is connected to other nodes may specifically refer to +.>The total number of links in the layer of nodes and other nodes is the total number of links in the layer of nodes and other nodes.
Similarly, the calculation is performed in different time periodsTotal number of links when a node has links with other nodes:
wherein,representing +.>The total number of links the node is connected to other nodes may specifically refer to +. >There is a total number of links between the layers with other nodes under the nth time window (nth layer).
Then, it is determined thatThe formula for the total number of links of a node with other nodes can be expressed as:
wherein,representation->The total number of links between a node and other nodes within and between layers,for indicating->The node has a total number of links with other nodes in the layer,for indicating->The node is in interlayer with other nodesThere is a total number of links for the link.
The server is according toThe first connectivity corresponding to the node can be obtained +.>Intra-layer connectivity of a node with other nodes, where intra-layer connectivity means +.>The sum of the first connectivity of the node with other nodes is formulated as:
wherein,is indicated in the m-th layer->Intra-layer connectivity of a node with other nodes.
Similarly, the server is based onThe second connectivity corresponding to the node can be obtained by +.>Interlayer connectivity between a node and other nodes, where interlayer connectivity refers to +.>The sum of the second connectivity of the node with other nodes is formulated as:
wherein,expressed in the m-th layer- >Inter-layer connectivity of a node with other nodes in the nth layer. In particular, when->When (I)>Can also mean +.>Intra-layer connectivity of a node with other nodes.
Further according toTotal number of links of node and other nodes, in the same time +.>Intra-layer connectivity of a node with other nodes and +.>Interlayer connectivity between the node and other nodes to obtain +.>The multi-participation coefficient of a node in brain activity can be formulated as:
wherein, first calculateNodes are +.>Intra-layer of nodes with other nodesConnectivity or +.>The ratio of the inter-layer connectivity of the node with other nodes in the total number of links, then +.>And adding square results of the duty ratio of all intra-layer connectivity and inter-layer connectivity of the node in the total link to obtain the multi-participation coefficient.
It should be noted that, when the multiple participation coefficient tends to be 1, the node is more uniformly connected between the layers, that is, the node is well connected with other nodes in the same time period and different time periods. Conversely, when the multiple participation coefficient tends to 0, it is indicated that the node is unevenly connected within and between layers, that is, the node is poorly connected with other nodes of the same period of time and different periods of time.
S104: and executing the target task according to the multi-participation coefficient corresponding to each node.
The server obtains the final multi-participation coefficient corresponding to each node so as to continue to execute the target task. The target task can be to analyze the connection condition of each node in brain activity more accurately so as to obtain a more perfect evaluation result aiming at the connection condition of the node; the target task may also be to obtain multiple participation coefficients corresponding to each node, and then display information on the connections between brain regions corresponding to each node in the brain activity.
Of course, the multi-participation coefficient has reference significance for searching central nodes (key brain areas) and the like widely connected with other nodes in the brain. Further, whether or not the central node is abnormal may be used to determine the progress of Alzheimer's disease, and to prevent or treat Alzheimer's disease, etc. And when the multi-participation coefficients of the normal person and the compulsive patient are compared, the multi-participation coefficient of each node of the normal person is higher than that of the compulsive patient because the brain area connection condition of the compulsive patient is abnormal.
In the specification, the server obtains the multi-participation coefficient of each node in the brain activity according to the total number of links between each node and other nodes, the first connectivity corresponding to each node and the second connectivity corresponding to each node, so that the accuracy of the multi-participation coefficient of the node is greatly improved, the relation between brain areas in the brain activity is more comprehensively displayed, and the server can better execute the target task according to the accurate multi-participation coefficient.
The above is a task execution method based on the multi-layer brain network node participation coefficient implemented by one or more of the above embodiments, and based on the same thought, the present disclosure further provides a corresponding task execution device based on the multi-layer brain network node participation coefficient, as shown in fig. 3.
Fig. 3 is a schematic diagram of a task execution device based on a multi-layer brain network node participation coefficient provided in the present specification, including:
the acquiring module 301 is configured to acquire brain signal data, so as to obtain, for each preset time period, a brain signal sequence corresponding to each node in the time period according to brain signals of each node in the time period, where at least part of time periods in each time period have partial time overlapping;
A determining module 302, configured to determine, for each node, a node connectivity between the node and other nodes in the same time period according to the brain signal sequence of the node in each time period and the brain signal sequences of other nodes in each time period, as a first connectivity corresponding to the node, and determine a node connectivity between the node and other nodes in different time periods, as a second connectivity corresponding to the node;
the calculating module 303 is configured to determine, according to the first connectivity and the second connectivity corresponding to each node, a multiple participation coefficient of each node in brain activity, where, for each node, the multiple participation coefficient corresponding to the node is used to represent a connection condition of a brain region corresponding to the node with other brain regions in brain activity;
and the execution module 304 is configured to execute the target task according to the multiple participation coefficients corresponding to each node.
Optionally, the acquiring module 301 is specifically configured to acquire brain signal data, so as to sample, for each preset time period, brain signal data of each node in the time period according to a time window corresponding to the preset time period, so as to obtain a brain signal sequence corresponding to each node in the time period.
Optionally, the determining module 302 is specifically configured to determine, for each node, a degree of association between the brain signal sequence of the node and the brain signal sequences of other nodes in the same time period, as a first degree of association corresponding to the node; and determining an intra-layer adjacency matrix according to the first association degree corresponding to each node, wherein for each matrix value contained in the intra-layer adjacency matrix, the matrix value is used for representing the first connectivity between two nodes corresponding to the matrix value in the same time period.
Optionally, the determining module 302 is specifically configured to determine, for each node, a degree of association between the brain signal sequence of the node and the brain signal sequences of other nodes in different time periods, as a second degree of association corresponding to the node; and determining an interlayer adjacency matrix according to the second association degree corresponding to each node, wherein the matrix value is used for representing the second connectivity between two nodes corresponding to the matrix value in different time periods for each matrix value contained in the interlayer adjacency matrix.
Optionally, the calculating module 303 is specifically configured to obtain, for each node, a multiple participation coefficient of the node in brain activity according to the determined total number of links between the node and other nodes, the first connectivity corresponding to the node, and the second connectivity corresponding to the node, where the total number of links refers to a sum of links formed when the node is linked with other nodes.
Optionally, the calculating module 303 is specifically configured to determine, for each node, a degree of association between the brain signal sequence of the node and the brain signal sequences of other nodes in the same time period as a first degree of association corresponding to the node, and determine, for each node, a degree of association between the brain signal sequence of the node and the brain signal sequences of other nodes in different time periods as a second degree of association corresponding to the node; for each other node, if the first association degree between the node and the other node is determined to be greater than a preset threshold value or the second association degree between the node and the other node is determined to be greater than a preset threshold value, determining that a link exists between the node and the other node; and determining the total number of links according to the links which are determined to exist between the node and each other node.
The present specification also provides a computer readable storage medium storing a computer program, where the computer program is configured to perform a task execution method based on the participation coefficients of the multi-layer brain network node provided in fig. 1.
The present specification also provides a schematic structural diagram of an electronic device corresponding to fig. 1 shown in fig. 4. At the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile storage, as described in fig. 4, although other hardware required by other services may be included. The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to realize the task execution method based on the multi-layer brain network node participation coefficient described in the above figure 1. Of course, other implementations, such as logic devices or combinations of hardware and software, are not excluded from the present description, that is, the execution subject of the following processing flows is not limited to each logic unit, but may be hardware or logic devices.
Improvements to one technology can clearly distinguish between improvements in hardware (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) and software (improvements to the process flow). However, with the development of technology, many improvements of the current method flows can be regarded as direct improvements of hardware circuit structures. Designers almost always obtain corresponding hardware circuit structures by programming improved method flows into hardware circuits. Therefore, an improvement of a method flow cannot be said to be realized by a hardware entity module. For example, a programmable logic device (Programmable Logic Device, PLD) (e.g., field programmable gate array (Field Programmable Gate Array, FPGA)) is an integrated circuit whose logic function is determined by the programming of the device by a user. A designer programs to "integrate" a digital system onto a PLD without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Moreover, nowadays, instead of manually manufacturing integrated circuit chips, such programming is mostly implemented by using "logic compiler" software, which is similar to the software compiler used in program development and writing, and the original code before the compiling is also written in a specific programming language, which is called hardware description language (Hardware Description Language, HDL), but not just one of the hdds, but a plurality of kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), lava, lola, myHDL, PALASM, RHDL (Ruby Hardware Description Language), etc., VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog are currently most commonly used. It will also be apparent to those skilled in the art that a hardware circuit implementing the logic method flow can be readily obtained by merely slightly programming the method flow into an integrated circuit using several of the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable logic controllers, and embedded microcontrollers, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, atmel AT91SAM, microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller may thus be regarded as a kind of hardware component, and means for performing various functions included therein may also be regarded as structures within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functions of each element may be implemented in one or more software and/or hardware elements when implemented in the present specification.
It will be appreciated by those skilled in the art that embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the present specification may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present description can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present description is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the specification. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
It will be appreciated by those skilled in the art that embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the present specification may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present description can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.
The foregoing is merely exemplary of the present disclosure and is not intended to limit the disclosure. Various modifications and alterations to this specification will become apparent to those skilled in the art. Any modifications, equivalent substitutions, improvements, or the like, which are within the spirit and principles of the present description, are intended to be included within the scope of the claims of the present description.
Claims (10)
1. A task execution method based on multi-layer brain network node participation coefficients is characterized in that a plurality of brain areas are divided in the brain of a user, each brain area corresponds to different nodes in a preset brain network diagram, and the task execution method comprises the following steps:
acquiring brain signal data to obtain brain signal sequences corresponding to nodes in each time period according to brain signals of the nodes in each time period for each preset time period, wherein at least partial time periods in each time period are overlapped in part;
for each node, determining node connectivity of the node with other nodes in the same time period according to the brain signal sequence of the node in each time period and the brain signal sequences of other nodes in each time period, wherein the node connectivity is used as a first connectivity corresponding to the node, and determining node connectivity of the node with other nodes in different time periods, and the node connectivity is used as a second connectivity corresponding to the node;
determining a multi-participation coefficient of each node in the brain activity according to the first connectivity and the second connectivity corresponding to each node, wherein the multi-participation coefficient corresponding to each node is used for representing the communication condition of the brain region corresponding to the node with other brain regions in the brain activity;
And executing the target task according to the multi-participation coefficient corresponding to each node.
2. The method of claim 1, wherein obtaining brain signal data to obtain, for each preset time period, a brain signal sequence corresponding to each node in the time period according to brain signals of each node in the time period, includes:
and acquiring brain signal data, so as to sample the brain signal data of each node in each preset time period according to a time window corresponding to the preset time period, so as to obtain a brain signal sequence corresponding to each node in the time period.
3. The method of claim 1, wherein determining the node connectivity of the node with other nodes in the same time period as the first connectivity corresponding to the node specifically includes:
for each node, determining the association degree between the brain signal sequence of the node and the brain signal sequences of other nodes in the same time period as a first association degree corresponding to the node;
and determining an intra-layer adjacency matrix according to the first association degree corresponding to each node, wherein for each matrix value contained in the intra-layer adjacency matrix, the matrix value is used for representing the first connectivity between two nodes corresponding to the matrix value in the same time period.
4. The method of claim 1, wherein determining the node connectivity of the node with other nodes in different time periods as the second connectivity corresponding to the node specifically comprises:
for each node, determining the association degree between the brain signal sequence of the node and the brain signal sequences of other nodes in different time periods, and taking the association degree as a second association degree corresponding to the node;
and determining an interlayer adjacency matrix according to the second association degree corresponding to each node, wherein the matrix value is used for representing the second connectivity between two nodes corresponding to the matrix value in different time periods for each matrix value contained in the interlayer adjacency matrix.
5. The method of claim 1, wherein determining the multi-participation factor of each node in brain activity based on the first connectivity and the second connectivity corresponding to each node, comprises:
and aiming at each node, obtaining a multi-participation coefficient of the node in brain activity according to the determined total number of links of the node and other nodes, the first connectivity corresponding to the node and the second connectivity corresponding to the node, wherein the total number of links refers to the sum of links formed when the node is linked with other nodes.
6. The method of claim 5, wherein the determining the total number of links between the node and other nodes specifically comprises:
for each node, determining the association degree between the brain signal sequence of the node and the brain signal sequences of other nodes in the same time period as a first association degree corresponding to the node, and determining the association degree between the brain signal sequence of the node and the brain signal sequences of other nodes in different time periods as a second association degree corresponding to the node;
for each other node, if the first association degree between the node and the other node is determined to be greater than a preset threshold value or the second association degree between the node and the other node is determined to be greater than a preset threshold value, determining that a link exists between the node and the other node;
and determining the total number of links according to the links which are determined to exist between the node and each other node.
7. A task execution device based on multi-layer brain network node participation coefficients, comprising:
the acquisition module is used for acquiring brain signal data so as to obtain brain signal sequences corresponding to all nodes in each time period according to the brain signals of all nodes in each time period for each preset time period, wherein at least part of time periods in each time period have partial time overlapping;
A determining module, configured to determine, for each node, a node connectivity of the node with other nodes in the same time period according to the brain signal sequence of the node in each time period and the brain signal sequences of other nodes in each time period, as a first connectivity corresponding to the node, determining node connectivity of the node with other nodes in different time periods, and as a second connectivity corresponding to the node;
the calculation module is used for determining a multi-participation coefficient of each node in the brain activity according to the first connectivity and the second connectivity corresponding to each node, and for each node, the multi-participation coefficient corresponding to the node is used for representing the communication condition of the brain region corresponding to the node with other brain regions in the brain activity;
and the execution module is used for executing the target task according to the multi-participation coefficient corresponding to each node.
8. The apparatus of claim 7, wherein determining a node connectivity of the node with other nodes in the same time period is used as a first connectivity corresponding to the node, and the determining module is specifically configured to determine, for each node, a degree of association between a brain signal sequence of the node and brain signal sequences of other nodes in the same time period as a first degree of association corresponding to the node; and determining an intra-layer adjacency matrix according to the first association degree corresponding to each node, wherein for each matrix value contained in the intra-layer adjacency matrix, the matrix value is used for representing the first connectivity between two nodes corresponding to the matrix value in the same time period.
9. A computer readable storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the method of any of the preceding claims 1-6.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of the preceding claims 1-6 when executing the program.
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