CN111227828A - Method for establishing brain function network - Google Patents

Method for establishing brain function network Download PDF

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CN111227828A
CN111227828A CN202010093985.2A CN202010093985A CN111227828A CN 111227828 A CN111227828 A CN 111227828A CN 202010093985 A CN202010093985 A CN 202010093985A CN 111227828 A CN111227828 A CN 111227828A
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time
function network
brain function
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许学添
陈晓明
李玲俐
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Guangdong Justice Police Vocational College
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Abstract

The invention relates to a method for establishing a brain function network, which comprises the following steps of S1: calculating the power GFP of the whole brain area; s2: acquiring the peak time of GFP and recording as a time sequence t; s3: calculating the phase locking value between each two paths of lead electroencephalogram signals within a time range with each moment in the time sequence t as the central time; s4: reflecting the relation between nodes according to the phase locking value index between each two paths of lead electroencephalogram signals so as to establish a brain function network. Compared with the prior art, the method for establishing the brain function network can establish the brain function network accurately describing the state and the connectivity of the brain.

Description

Method for establishing brain function network
Technical Field
The invention belongs to the field of electroencephalogram signal analysis, and particularly relates to a method for establishing a brain function network.
Background
Electroencephalograms (EEG) are patterns obtained by recording spontaneous biopotentials of the brain from the scalp by means of a precise electronic instrument in an enlarged manner, and are spontaneous and rhythmic electrical activities of brain cell groups recorded by means of electrodes. The electroencephalogram signal has higher time precision and can dynamically observe the state change of the brain, so the electroencephalogram signal is an important tool for diagnosing different neurological disorders and diseases. The interaction and the mutual association between the neurons of the brain make the brain become a complex kinetic system. The brain is considered as a functional network, and the time correlation among nerve clusters of different areas of the brain is researched, so that the exploration of the relation among the different areas of the brain is a method of a brain dynamic system. At present, a method for establishing a brain function network generally establishes a functional network according to a pearson correlation coefficient of an EEG signal acquired between different electrodes, or establishes a functional network according to a phase-locked value of the EEG signal between different electrodes, so that the discharge characteristics of the EEG signal at different moments are ignored, and the established brain function network cannot better reflect the real state of the brain.
Disclosure of Invention
The invention aims to provide a method for establishing a brain function network, which establishes a connection relation of nodes through phase locking values among different lead nodes and is realized through the following technical scheme:
a method for establishing a brain function network, wherein electroencephalogram signals are collected by a multi-channel lead electrode, comprises the following steps: :
s1: the power of the whole brain domain GFP is calculated,
Figure BDA0002384631230000011
wherein K is the total number of conducting electrodes of the leads and represents K nodes, i is the ith lead, and Vi(t) EEG signal for the ith lead, Vmean(t) is the average value of K lead signals at t moment;
s2: the peak time of GFP is obtained and is recorded as the time series t ═ t1,t2,t3...tn]；
S3: at a time sequence t ═ t1,t2,t3...tn]At each time ti(i∈[1,n]) Calculating the phase locking value between each two paths of lead electroencephalogram signals within a time range of the central time;
s4: reflecting the relation between nodes according to the phase locking value index between each two paths of lead electroencephalogram signals so as to establish a brain function network.
Compared with the prior art, the method can establish the brain function network accurately describing the brain state and the connectivity.
Further, the phase-locked value index of the step S4 is a time series [ t1,t2,t3...tn]At each time ti(i∈[1,n]) The mean value of the corresponding phase-locked values, or both, in a time series t1,t2,t3...tn]The maximum value of the phase-locked value corresponding to each time instant.
Further, in step S4, a weighted brain function network graph is directly established according to the phase-locked value indicator, and the weight values between the nodes are the phase-locked value indicators.
Further, in step S4, the phase-locked value indicator is compared with a threshold, and if the phase-locked value indicator is greater than or equal to the threshold, it is determined that there is a connection relationship between nodes, or if the phase-locked value indicator is smaller than the threshold, it is determined that there is no connection relationship between nodes, so as to establish an unauthorized brain function network diagram.
Further, the calculation formula of the phase-locked value PLV is:
Figure BDA0002384631230000021
wherein
Figure BDA0002384631230000022
Is the phase relation value of two-way lead EEG signal, N is time sequence [ t1,t2,t3...tn]At each time ti(i∈[1,n]) The number of sampling points in a time range of the central time.
Further, the phase relation value of the two-way lead electroencephalogram signals
Figure BDA0002384631230000023
May be calculated by time-frequency analysis of the signal.
In order that the invention may be more clearly understood, specific embodiments thereof will be described hereinafter with reference to the accompanying drawings.
Drawings
Fig. 1 is a flow chart of a method for establishing a brain function network according to the present invention.
Fig. 2 is a diagram of a global brain domain power peak timing.
Fig. 3 is a brain function network creation representation.
Fig. 4 is a diagram of a brain function network.
Detailed Description
The invention provides a method for establishing a brain function network, which can effectively establish the brain function network.
Since electroencephalogram information is generally acquired by multi-lead electrodes, the correlation between different electrodes can be established, and thus a brain function network can be established. Defining the measured area of each lead electrode as a node in the graph, abstracting the multipath EEG signal into a graph G consisting of a set of points and a set of edges: g ═ V, E, where V is the set of nodes corresponding to the lead nodes for EEG brain number acquisition and E is the connected edge.
Phase synchronization of different frequency bands of electroencephalograms has been proven to be a possible mechanism for explaining neuron integration, and the phase synchronization relationship of two electroencephalograms is represented by a phase-locked value (PLV). Global Field Power (GFP) represents the electric field strength on the brain at each instant and is therefore typically used to measure Global brain response to events or to characterize rapid changes in brain activity, with the peak position of the GFP curve representing the instant of strongest field strength and highest topographic signal-to-noise ratio. According to the method, the phase-locked value is calculated by using the time of the peak position of the GFP curve, and the process of cerebral neuron discharge can be better reflected. Referring to fig. 1, the method for establishing a brain function network according to the present invention includes the following steps:
s1: the power of the whole brain domain GFP is calculated,
Figure BDA0002384631230000031
wherein K is the total number of conducting electrodes of the leads and represents K nodes, i is the ith lead, and Vi(t) EEG signal for the ith lead, VmeanAnd (t) is the average value of K lead signals at the t moment.
S2: the peak time of GFP is obtained and is recorded as the time series t ═ t1,t2,t3...tn]。
As shown in FIG. 2, [ t ]1,t2,t3...tn]The moment of alignment is the peak moment of the GFP curve.
S3: in the time series [ t ]1,t2,t3...tn]At each time ti(i∈[1,n]) And calculating the phase locking value between each two paths of lead electroencephalogram signals within a time range of the central time.
The phase-locked value PLV is calculated by the formula:
Figure BDA0002384631230000032
wherein
Figure BDA0002384631230000033
Is the phase relation value of two-way lead EEG signal, N is time sequence [ t1,t2,t3...tn]At each time ti(i∈[1,n]) The number of sampling points in a time range of the central time. And the phase relation value of two-way lead EEG signal
Figure BDA0002384631230000034
The calculation can be performed by time-frequency analysis of the signal, including short-time fourier transform, wavelet transform, hilbert-yellow transform, and Cohen-like time-frequency distribution.
S4: reflecting the relation between nodes according to the phase locking value index between each two paths of lead electroencephalogram signals so as to establish a brain function network.
The time series t is calculated in step S31,t2,t3...tn]Each time ti(i∈[1,n]) The average value of the multiple phase-locked values or the maximum value thereof is taken as the phase-locked value index between each two paths of lead electroencephalogram signals to describe the relation between two nodes, so that a weighted brain function network diagram can be established, and the weight between the nodes is the phase-locked value index; and an unauthorized brain function network graph can be established by comparing the phase-locked value index with a threshold value, judging that the nodes have a connection relation if the phase-locked value index is greater than or equal to the threshold value, or judging that the nodes have no connection relation if the phase-locked value index is less than the threshold value, thereby establishing the unauthorized brain function network graph.
For the brain functional network diagram G, the unweighted adjacency matrix of the diagram is W, and the element omega thereof isijThe calculation is as follows:
Figure BDA0002384631230000035
if the phase lock value PLV between EEG signals of any two nodes i and jijIf the value is larger than or equal to the threshold value delta, the nodes i and j of the brain area are considered to have a connection relation, and the element omega corresponding to the adjacent matrix W is adjacent toij1, otherwise, ωij0 indicates that there is a connection relationship between nodes i and j in the brain region.
The whole process of establishing the brain function network diagram is shown in fig. 3, the phase-locked value of each two paths of electroencephalogram signals is calculated to obtain an adjacency matrix with weight, and then the phase-locked value is compared with a threshold value to establish a non-weight adjacency matrix of the brain network connection diagram. As shown in fig. 4, a functional brain network is established for 128-lead electroencephalogram signals.
The process and specific application of the method for establishing a brain function network of the present invention are described below.
Firstly, acquiring electroencephalogram signals to be analyzed, calculating the whole brain domain power of each path of lead nodes, then recording phase-locked values at moments near the power peak value of the whole brain domain, then taking the average value of the phase-locked values as a phase-locked value index, and finally judging the connection relation of the nodes according to the phase-locked value index to establish a brain function network.
Compared with the prior art, the method for establishing the brain function network establishes the brain function network through the phase locking value at the moment near the full brain domain power peak value, can establish the brain function network accurately describing the brain state and the connectivity, and can provide a brand new method for the intelligent judgment, the early warning and the treatment of the brain nervous system related diseases by applying the method to the electroencephalogram signal analysis of the brain nervous system related diseases.
The present invention is not limited to the above-described embodiments, and various modifications and variations of the present invention are intended to be included within the scope of the claims and the equivalent technology of the present invention if they do not depart from the spirit and scope of the present invention.

Claims (6)

1. A method for establishing a brain function network, wherein electroencephalogram signals are collected by a plurality of lead electrodes, and the method is characterized in that: the method comprises the following steps:
s1: the power of the whole brain domain GFP is calculated,
Figure FDA0002384631220000011
wherein K is the total number of conducting electrodes of the leads and represents K nodes, i is the ith lead, and Vi(t) EEG signal for the ith lead, Vmean(t) is the average value of K lead signals at t moment;
s2: the peak time of GFP is obtained and is recorded as the time series t ═ t1,t2,t3...tn]；
S3: at a time sequence t ═ t1,t2,t3...tn]At each time ti(i∈[1,n]) Calculating the phase locking value between each two paths of lead electroencephalogram signals within a time range of the central time;
s4: reflecting the relation between nodes according to the phase locking value index between each two paths of lead electroencephalogram signals so as to establish a brain function network.
2. The method for establishing a brain function network according to claim 1, wherein: the phase-locked value index of the step S4 is a time series [ t1,t2,t3...tn]At each time ti(i∈[1,n]) The mean value of the corresponding phase-locked values, or both, in a time series t1,t2,t3...tn]The maximum value of the phase-locked value corresponding to each time instant.
3. The method for establishing a brain function network according to claim 2, wherein: and step S4, directly creating a weighted brain function network graph according to the phase-locked value index, where the weight values between the nodes are the phase-locked value index.
4. The method for establishing a brain function network according to claim 2, wherein: the step S4 is to compare the phase-locked value indicator with a threshold, and if the phase-locked value indicator is greater than or equal to the threshold, determine that there is a connection relationship between nodes, or if the phase-locked value indicator is smaller than the threshold, determine that there is no connection relationship between nodes, thereby establishing an unauthorized brain function network diagram.
5. The method for establishing a brain function network according to claims 3-4, wherein: the calculation formula of the phase-locked value PLV is as follows:
Figure FDA0002384631220000012
wherein
Figure FDA0002384631220000013
Is the phase relation value of two-way lead EEG signal, N is time sequence [ t1,t2,t3...tn]At each time ti(i∈[1,n]) The number of sampling points in a time range of the central time.
6. The method for establishing a brain function network according to claim 5, wherein: phase relation value of the two-path lead electroencephalogram signal
Figure FDA0002384631220000021
May be calculated by time-frequency analysis of the signal.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111671399A (en) * 2020-06-18 2020-09-18 清华大学 Method and device for measuring noise perception intensity and electronic equipment

Citations (5)

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CN104510468A (en) * 2014-12-30 2015-04-15 中国科学院深圳先进技术研究院 Character extraction method and device of electroencephalogram
US20180199848A1 (en) * 2015-07-10 2018-07-19 Universite De Rennes I Method of constructing a data structure representative of a dynamic reorganization of a plurality of brain networks, corresponding device and program
CN108577835A (en) * 2018-05-17 2018-09-28 太原理工大学 A kind of brain function network establishing method based on micro- state
US20190239794A1 (en) * 2016-10-07 2019-08-08 University Of Tsukuba Electroencephalogram detecting device and program
US20190321638A1 (en) * 2015-11-18 2019-10-24 David J. Mogul Method and apparatus for preventing or terminating epileptic seizures

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104510468A (en) * 2014-12-30 2015-04-15 中国科学院深圳先进技术研究院 Character extraction method and device of electroencephalogram
US20180199848A1 (en) * 2015-07-10 2018-07-19 Universite De Rennes I Method of constructing a data structure representative of a dynamic reorganization of a plurality of brain networks, corresponding device and program
US20190321638A1 (en) * 2015-11-18 2019-10-24 David J. Mogul Method and apparatus for preventing or terminating epileptic seizures
US20190239794A1 (en) * 2016-10-07 2019-08-08 University Of Tsukuba Electroencephalogram detecting device and program
CN108577835A (en) * 2018-05-17 2018-09-28 太原理工大学 A kind of brain function network establishing method based on micro- state

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111671399A (en) * 2020-06-18 2020-09-18 清华大学 Method and device for measuring noise perception intensity and electronic equipment

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