CN115317000A - Multilayer brain network model construction method based on same frequency and cross-frequency information interaction - Google Patents
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Abstract
The invention provides a multilayer brain network model construction method based on co-frequency and cross-frequency information interaction, which comprises the steps of effective extraction of EEG signal multilayer frequency bands, calculation of function connection indexes among the multilayer frequency bands, construction and visualization of a multilayer brain network model. Firstly, synchronously acquiring a multi-channel electroencephalogram signal capable of reflecting a brain functional state, defining an electrode position as a network node, and performing frequency band division on a preprocessed signal based on wavelet packet decomposition; secondly, calculating the edges of the multi-layer brain network based on the phase locking values, and further constructing an adjacent matrix in the same frequency band and a network connection matrix between cross-frequency bands of the multi-channel electroencephalogram signals; thirdly, a multilayer brain network model is built by taking the characteristic frequency band as a layering basis, and the network characteristics in the layers and among the layers are visually presented and calculated by further combining a graph theory method. The invention comprehensively analyzes the synchronous relation between the same frequency band and different frequency bands of the multi-channel electroencephalogram signals, and is beneficial to deeply understanding the network structure and topological characteristics of the brain movement function state.
Description
Technical Field
The invention relates to the field of neural rehabilitation engineering and motor mechanism research, in particular to a multilayer brain network model construction method based on same frequency and cross-frequency information interaction.
Background
The brain is the highest part of the human nervous system, complex brain network analysis based on a graph theory analysis technology is one of the main methods for researching information interaction of brain functional regions at present, and the brain network analysis is widely applied to research of diseases such as nervous or mental systems. The cerebral apoplexy is a disease of brain tissue damage caused by cerebral vessel rupture or blockage, in recent years, the incidence of the cerebral apoplexy in China is increased year by year and shows the trend of the younger population, more than 80 percent of cerebral apoplexy patients are accompanied with motor dysfunction, and the change of the functional connection and the topological structure of a brain network in the cerebral apoplexy rehabilitation process is researched to be helpful for understanding the cerebral motion control process and the motor function recovery mechanism.
Research has shown that stroke not only causes changes in the brain tissue structure but also further affects the functional connection and structure of the brain network, and the brain network of a patient changes in a series of ways along with the rehabilitation process. However, the current brain network research mainly focuses on single-level functional network connection or single-frequency band analysis, and has a single angle. As is known, the brain is a highly complex system, the information transmission process of the brain is also complex, and not only co-frequency coupling but also cross-frequency coupling exists, so that an analysis method for constructing a multi-layer frequency band brain network model is provided, and the functional characteristics of the brain network are analyzed from the angles of co-frequency bands and cross-frequency bands.
Disclosure of Invention
In order to solve the technical problems mentioned in the background art, the invention provides a multilayer brain network model construction method based on same-frequency and cross-frequency information interaction.
In order to achieve the technical purpose, the technical scheme of the invention is as follows:
a multilayer brain network model construction method based on co-frequency and cross-frequency information interaction comprises the following steps:
1. after preprocessing a plurality of channels of electroencephalogram signals which are synchronously acquired, carrying out frequency band division on the multi-channel electroencephalogram signals by adopting a wavelet packet method, and effectively acquiring multi-channel electroencephalogram characteristic frequency bands of theta waves, alpha waves, beta waves and gamma waves;
2. constructing a multi-layer frequency band network model formed by an adjacent matrix in the same frequency band and a network connection matrix among cross frequency bands of a multi-channel electroencephalogram characteristic frequency band based on a phase-locked value (PLV) method;
3. and taking the multi-channel electroencephalogram characteristic frequency band as a layering basis, and performing visual presentation and calculation on the characteristics of the networks in and between layers by combining a graph theory method based on a multi-layer frequency band brain network model.
The method is further improved in that: the first step is specifically as follows: preprocessing a plurality of channel electroencephalogram signals acquired synchronously and decomposing wavelet packets to obtain a plurality of sub-frequency bands in an effective frequency range; the decomposed sub-bands are further synthesized into theta wave (4-8 Hz), alpha wave (8-12 Hz), beta wave (12-32 Hz) and gamma wave (32-80 Hz) frequency bands, and therefore the characteristic frequency band of the multichannel electroencephalogram is effectively obtained.
The method is further improved in that: the specific method of the second step is as follows:
the time sequences of the brain electrical signals of the frequency bands of theta wave, alpha wave, beta wave and gamma wave are taken as research objects for explanation, and the time sequences of the multichannel brain electrical signals are as follows:
where n is the number of time points, ch 1 ,Ch 2 ,…,Ch m Respectively correspond to a plurality of different channels of electroencephalogramsM is more than or equal to 2 and less than or equal to M, and M is the total number of channels in the time sequence of the signals.
a) Obtaining the instantaneous phase of each channel of electroencephalogram signals through Hilbert transform:
wherein A is 1 (t, n) is Ch m Instantaneous amplitude, P, of the channel EEG signal 1 (t, n) is Ch m Instantaneous phase of the channel electroencephalogram signal; wherein a (t, n) is Ch m The time sequence of the channel brain electrical signals,
the specific steps for the Hilbert transform are as follows:
where P represents the integral considered to be the Cauchy principal value; in the same way, the method for preparing the composite material,
can be defined as Ch m+1 Instantaneous phase of the channel electroencephalogram signal;
b) Further calculate the average value PLV of the phase index over time t, i.e.:
where Φ (t, n) is Ch m And Ch m+1 Instantaneous phase difference between channels, N being the total number of trials; similarly, the instantaneous phase difference between every two channels is respectively calculated for the multi-channel electroencephalogram signals.
If stable phase difference exists between the calculation channels, the PLV value is close to 1, namely phase synchronization; if no phase synchronization PLV is present, the PLV value approaches 0.
c) Calculating significant PLV values using a proxy data method based on random phase transformations to construct weightsAn adjacency matrix, a function connection matrix is constructed by calculating the PLV value between the preprocessed multichannel same-frequency-band electroencephalogram signals and the PLV between the proxy data electroencephalogram signals and is normalized, and the in-layer connection matrixes of four frequency bands of theta wave, alpha wave, beta wave and gamma wave are respectively marked as M iso_t ，M iso_a ，M iso_b ，M iso_g (ii) a Similarly, an interlayer connection matrix M between cross-frequency bands is calculated cross_ta ，M cross_tb ，M cross_tg ，M cross_ab ，M cross_ag ，M cross_bg 。
The method is further improved in that: in the third step, a multi-channel electroencephalogram characteristic frequency band is taken as a layering basis, a multi-layer frequency band network model is constructed based on the in-layer adjacent matrix and the interlayer connection matrix calculated in the second step, and finally, the in-layer and interlayer network characteristics are visually presented and calculated by combining a graph theory method, so that information interaction and topological networks of a plurality of functional brain intervals of the brain are explored, and the network structure and the topological characteristics of the brain movement function state can be deeply understood.
Due to the adoption of the technical scheme, the invention has the technical progress that: the multi-channel electroencephalogram signals are analyzed based on the model, so that the coupling characteristics of the signals in a plurality of characteristic frequency bands and among cross-frequency bands can be simultaneously embodied, the information interaction in the electroencephalogram characteristic frequency bands can embody the brain control working mode, and the physiological mechanism of diseases such as neurodegeneration and the like can be researched; the coupling characteristics among different frequency bands can reflect information interaction of brain electrical signals in cross-band oscillation, and the functional connection of brain execution cognition, memory and other activities is facilitated to be understood. Therefore, the information interaction between different brain areas with different functions of the brain can be more comprehensively embodied through the model, and the network structure and the topological characteristics of the movement function state of the brain can be deeply understood.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a diagram of the positions of 34-channel electroencephalogram electrodes;
FIG. 3 is a schematic diagram of a model of a multi-layer band network according to the present invention;
FIG. 4 is a graph of the results of the multi-layer band brain network for theta and gamma bands of the subject under the task state.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to explain the key steps of the technical solution in detail, the technical solution in the embodiment of the present invention is described in detail below with reference to the accompanying drawings in the embodiment of the present invention:
the brain, one of the highest-level information processing systems of human beings, includes numerous cells, neurons or a complex structure network formed by connecting a plurality of brain regions with each other, and realizes various functions by the mutual cooperation of the brain regions. In recent years, the interest of many researchers is obtained by the analysis of complex brain networks based on graph theory analysis technology, and meanwhile, the application in the field of brain science is also an important branch of the analysis of complex brain networks. Previous research mainly focuses on network analysis of co-frequency-band brain electrical signals, but because the brain is a highly complex system, information transmission is extremely complex, a nonlinear relationship exists besides a traditional linear transmission relationship, and cross-frequency-band coupling is one of more typical analysis angles. Therefore, the invention constructs a multilayer brain network model based on same-frequency and cross-frequency information interaction, can embody the brain network characteristics of the same frequency band and has a cross-frequency connection relation, and is beneficial to more comprehensively analyzing the information processing process of the brain.
Theta, alpha, beta and gamma in this application represent theta, alpha, beta and gamma, respectively.
The number of channels for acquiring the channel electroencephalogram signals includes but is not limited to 34 channels, and the model is suitable for any multi-channel electroencephalogram signals. The following example is illustrated using only 34 channels of electroencephalogram signals.
The following are specific embodiments of the present invention:
FIG. 1 shows a schematic flow chart of the present invention:
step 1, adopting a 64-channel eego TM The sports system collects multi-channel electroencephalogram signals, and the sampling frequency is 1000Hz.
An electroencephalogram signal acquisition process: the electroencephalogram electrode adopts an international standard 10-20 electrode placement standard, and 34 channels of electroencephalogram signals of a tested subject in a rest state and an action task are collected, wherein the positions of the 34 channels of electroencephalogram electrodes are shown in figure 2.
Step 2, preprocessing the acquired electroencephalogram signals based on MATLAB analysis software comprises: removing physiological artifacts such as baseline drift, electro-oculogram and myoelectricity and power frequency interference, and resampling the signal to 1024Hz; based on the frequency band characteristics of the electroencephalogram signals, preprocessing the electroencephalogram signals of 34 selected channels, and then decomposing the wavelet packets of 7 layers to obtain a plurality of sub-frequency bands with the resolution of 4Hz within an effective frequency range (0-4 Hz,4-8Hz,8-12Hz, \8230;, 508-512 Hz); the decomposed sub-bands are further synthesized into theta (4-8 Hz), alpha (8-12 Hz), beta (12-32 Hz) and gamma (32-80 Hz) frequency bands, and then the multichannel electroencephalogram characteristic frequency bands are obtained.
where n is the number of time points, ch 1 ,Ch 2 ,…,Ch 34 Are respectively provided withCorresponding to 34 different channels of electroencephalogram signals.
a) Obtaining the instantaneous phase of each channel of electroencephalogram signals through Hilbert transform:
wherein A is 1 (t, n) is Ch 1 Instantaneous amplitude, P, of the channel EEG signal 1 (t, n) is Ch 1 Instantaneous phase of the channel electroencephalogram signal; wherein a (t, n) is Ch 1 The time sequence of the channel brain electrical signals,
the Hilbert transform process is specifically as follows:
where P represents the integral considered to be the cauchy main value. In the same way, the method for preparing the composite material,
can be defined as Ch 2 Instantaneous phase of the channel brain electrical signal.
b) Further calculate the average value PLV of the phase index over time t, i.e.:
where Φ (t, n) is Ch 1 And Ch 2 Instantaneous phase difference between channels, N being the total number of trials; similarly, instantaneous phase differences between every two channels are respectively calculated for 34 channels of electroencephalogram signals. If stable phase difference exists between the calculation channels, the PLV value is close to 1, namely phase synchronization; if no phase synchronization PLV is present, the PLV value approaches 0.
c) Calculating the significant PLV value by adopting a proxy data method based on random phase transformation to construct a weighted adjacency matrix, and calculating multi-pass after preprocessingConstructing a functional connection matrix by PLV values between the electroencephalogram signals of the same frequency band and PLV' between the electroencephalogram signals of the proxy data and carrying out normalization processing, and respectively recording the in-layer connection matrices of the four frequency bands of theta, alpha, beta and gamma as M iso_t ，M iso_a ，M iso_b ，M iso_g 。
Similarly, calculating the phase coupling relation of the electroencephalogram channel corresponding to the PLV in different frequency bands, calculating the PLV of the preprocessed electroencephalogram channel corresponding to the electroencephalogram channel in different frequency bands and the PLV 'of the proxy data electroencephalogram signal, constructing the interlayer connection of the multilayer frequency band brain network by taking the PLV' as a threshold value, and performing normalization processing to obtain an interlayer connection matrix M between cross-frequency bands cross_ta ，M cross_tb ，M cross_tg ，M cross_ab ，M cross_ag ，M cross_bg 。
And 4, constructing a multilayer frequency band network model based on the in-layer adjacent matrix and the interlayer connection matrix calculated in the step 3 by taking the characteristic frequency band as a layering basis, and finally, performing visual presentation and calculation on the in-layer and interlayer network characteristics by combining a graph theory method. Further, information interaction and topological networks among multiple functional brain regions of the brain are explored, and the network structure and topological characteristics of the movement function state of the brain can be deeply understood.
Based on the process, a multilayer frequency band brain network based on PLV is constructed in a rest state and a task state, namely the information interaction condition of the brain in different states is quantitatively depicted.
In order to verify the feasibility and the effectiveness of the multilayer brain network model construction method based on the same-frequency and cross-frequency information interaction, 4 healthy right-handed aged subjects (average age, 65.5 +/-7.59) are recruited to carry out a linkage task experiment. The detailed protocol was approved by the ethical review committee of Yanshan university. According to the analysis process for constructing the multilayer frequency band brain network, the multilayer frequency band brain network is constructed on the collected multichannel electroencephalogram signals.
According to the electroencephalogram acquisition and analysis process, the brain function connection network attribute of a healthy subject in different task states is further explored. Fig. 4 shows the phase coupling relationship and the cross-band theta-gamma phase coupling relationship of two frequency bands theta and gamma in the respective frequency band ranges when 4 healthy elderly are tested to perform an action task state, wherein 1 refers to the in-layer connection of the theta frequency band, 2 refers to the cross-band theta-gamma interlayer connection, and 3 refers to the in-layer connection of the gamma frequency band.
The principle and the implementation mode of the invention are explained by applying a specific example, and the description of the embodiment is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (4)
1. A multilayer brain network model construction method based on same frequency and cross-frequency information interaction is characterized by comprising the following steps:
1. after preprocessing a plurality of channel electroencephalogram signals which are synchronously acquired, carrying out frequency band division on the multi-channel electroencephalogram signals by adopting a wavelet packet method, and effectively acquiring multi-channel electroencephalogram characteristic frequency bands of theta waves, alpha waves, beta waves and gamma waves;
2. constructing a multi-layer frequency band network model formed by an adjacent matrix in the same frequency band and a network connection matrix among cross frequency bands of a multi-channel electroencephalogram characteristic frequency band based on a phase-locked value (PLV) method;
3. and taking the multi-channel electroencephalogram characteristic frequency band as a layering basis, and performing visual presentation and calculation on the characteristics of the networks in and between layers by combining a graph theory method based on a multi-layer frequency band brain network model.
2. The method for constructing the multi-layer brain network model based on the interaction of the same frequency and cross-frequency information according to claim 1, wherein the first step specifically comprises: preprocessing a plurality of channel electroencephalogram signals acquired synchronously and decomposing wavelet packets to obtain a plurality of sub-frequency bands in an effective frequency range; the decomposed sub-bands are further synthesized into theta wave (4-8 Hz), alpha wave (8-12 Hz), beta wave (12-32 Hz) and gamma wave (32-80 Hz) frequency bands, and therefore the characteristic frequency band of the multichannel electroencephalogram is effectively obtained.
3. The method for constructing the multilayer brain network model based on the interaction of the same frequency and cross-frequency information according to claim 1, wherein the specific method in the second step is as follows:
the time sequence of the brain electrical signals of the frequency bands of theta wave, alpha wave, beta wave and gamma wave is taken as a research object for explanation, and the time sequence of the multichannel brain electrical signals is as follows:
where n is the number of time points, ch 1 ,Ch 2 ,…,Ch m Respectively corresponding to time sequences of a plurality of different channels of electroencephalogram signals, wherein M is more than or equal to 2 and less than or equal to M, and M is the total number of channels;
a) Obtaining the instantaneous phase of each channel electroencephalogram signal through Hilbert transform:
wherein A is 1 (t, n) is Ch m Instantaneous amplitude, P, of the channel EEG signal 1 (t, n) is Ch m Instantaneous phase of the channel electroencephalogram signal; wherein a (t, n) is Ch m The time sequence of the channel brain electrical signals,
the specific steps for the Hilbert transform are as follows:
where P represents the integral considered to be the Cauchy principal value; in the same way, the method has the advantages of,
can be defined as Ch m+1 Instantaneous phase of the channel electroencephalogram signal;
b) Further calculate the average value PLV of the phase index over time t, i.e.:
where Φ (t, n) is Ch m And Ch m+1 Instantaneous phase difference between channels, N being the total number of trials; similarly, instantaneous phase differences between every two channels are respectively calculated for the multi-channel electroencephalogram signals;
if stable phase difference exists between the calculation channels, the PLV value is close to 1, namely phase synchronization; if no phase synchronization PLV value is close to 0;
c) Calculating a remarkable PLV value by adopting a proxy data method based on random phase transformation to construct a weighted adjacency matrix, calculating a PLV value between preprocessed multi-channel same-frequency-band electroencephalogram signals and a PLV between proxy data electroencephalogram signals to construct a functional connection matrix, and normalizing, wherein in-layer connection matrixes of four frequency bands of theta waves, alpha waves, beta waves and gamma waves are respectively marked as M iso_t ，M iso_a ，M iso_b ，M iso_g (ii) a Similarly, an interlayer connection matrix M between the cross frequency bands is calculated cross_ta ，M cross_tb ，M cross_tg ，M cross_ab ，M cross_ag ，M cross_bg 。
4. The method for constructing the multilayer brain network model based on the same frequency and cross-frequency information interaction according to claim 1, characterized in that in the third step, a multilayer frequency band network model constructed based on the intra-layer adjacency matrix and the inter-layer connection matrix calculated in the second step is constructed by taking a multi-channel electroencephalogram characteristic frequency band as a layering basis, and finally, the intra-layer and inter-layer network characteristics are visually presented and calculated by combining a graph theory method, so that the information interaction and topological network among a plurality of functional brain regions of the brain are explored, and the network structure and the topological characteristic of the brain movement function state can be deeply understood.
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