CN112617862B - Method for judging coupling strength between signals based on multichannel neural signal analysis - Google Patents

Method for judging coupling strength between signals based on multichannel neural signal analysis Download PDF

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CN112617862B
CN112617862B CN202110042395.1A CN202110042395A CN112617862B CN 112617862 B CN112617862 B CN 112617862B CN 202110042395 A CN202110042395 A CN 202110042395A CN 112617862 B CN112617862 B CN 112617862B
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梁振虎
任娜
邵帅
金星
吴昀哲
李小俚
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Abstract

The invention discloses a method for judging coupling strength between signals based on multichannel neural signal analysis, which comprises the following steps: s1, selecting multi-channel electroencephalogram signals from an electroencephalogram database; step S2: preprocessing two time sequences of the selected signal; s3, calculating edge probability distribution functions of the two time sequences and entropies of the two time sequences, and calculating a joint entropy based on the joint probability function; s4, calculating the original sequencing cross mutual information of the two time sequences; s5, calculating an alternative time series and a PCMI of the alternative time series; and S6, calculating the pure PCMI. The method can estimate the real connection strength of the signals among different channels, and can effectively reduce the influence of signal artifacts generated by the mutual interference of the signals among different channels on the evaluation result of the signal strength among the channels.

Description

Method for judging coupling strength between signals based on multichannel neural signal analysis
Technical Field
The invention relates to the field of multichannel neural signal analysis, in particular to a method for judging coupling strength between signals based on multichannel neural signal analysis.
Background
Studies have shown that human bodies are accompanied by high levels of brain activity when they perform sensory, motor, cognitive, etc. events, and for humans, the high and low levels of signal coupling strength between different brain regions directly reflect the activity of their brains. At present, the change of the electroencephalogram characteristic index is compared through a symbolic analysis method, so that the quantitative evaluation of the change of the brain activity intensity is realized, and the method is considered to be a simple and effective method for solving the problems, such as ordering entropy, symbolic transfer entropy, weighted symbolic mutual information and the like, and is used for analyzing a nervous system and monitoring the brain state. However, the methods ignore the artifacts existing between the signals, and the influence of the artifacts is not eliminated in the calculation process, so that the calculation result is not accurate enough, and the method has deviation with the real information interaction between the neural signals.
Disclosure of Invention
In order to overcome the defects of the prior art, the patent provides a method for judging the coupling strength between signals based on multi-channel neural signal analysis, which combines a substitution analysis method with a symbolic dynamics method, makes up the defect that a single symbolic method cannot eliminate artifacts, can obtain the real coupling information between neural signals and the real connection strength between brain areas, can accurately extract the features of the neural signals, and more accurately analyzes the change of the information integration capability of the brain.
In order to achieve the purpose, the invention adopts the following technical scheme:
specifically, the invention provides a method for judging coupling strength between signals based on multichannel neural signal analysis, which comprises the following steps:
step S1: collecting multi-channel electroencephalogram signals in an electroencephalogram database;
step S2: preprocessing the acquired electroencephalogram signals, wherein the preprocessing comprises removing large-amplitude noise, removing 50Hz power frequency signals and quickly analyzing independent components to remove physiological noise components;
selecting a time sequence x after two channel preprocessingtAnd ytPerforming m-dimensional phase space reconstruction, and arranging the reconstructed time sequences in an increasing order;
removing the large-amplitude noise of the acquired signal in the step S2, removing a 50Hz power frequency signal by using a notch filter, performing down-sampling on the signal, and removing the physiological noise by using a rapid independent component analysis method;
selecting two time sequences of the preprocessed electroencephalogram signals, selecting an embedded dimension m lag tau, performing m-dimensional phase space reconstruction, and arranging the reconstructed time sequences according to an increasing sequence;
selectingTwo time series xtAnd ytN, constructing a vector X with an embedding dimension m and a lag τ using a phase space reconstruction methodt[xt,xt+τ,...,xt+mτ]And Yt[yt,yt+τ,...,yt+mτ](ii) a Where τ is a specific sample point, arranging the time series in increasing order, and using XtAnd YtRespectively as a sorted time series
Figure BDA0002896415740000021
And
Figure BDA0002896415740000022
the symbol of (1);
step S3: separately calculate xtAnd ytThe edge probability distribution function calculates entropy values of the two time sequences, and calculates joint entropy of the two time sequences based on the joint probability function;
in step S3, edge probability distribution functions of the two time series are calculated, entropy values of the two time series are calculated according to the distribution functions, and joint entropy of the two time series is calculated based on the joint probability function,
in particular to calculate xtAnd ytEdge Probability Distribution Function (PDF), denoted p (x) and p (y), respectively, xtAnd ytIs defined as:
Figure BDA0002896415740000023
Figure BDA0002896415740000024
computing joint entropy based on joint probability function H (X, Y)
Figure BDA0002896415740000025
Step S4: calculating the original PCMI of the two time series;
step S5: selecting one of the two time sequences to generate a substitute time sequence, and generating substitute data by adopting a random transformation method with an amplitude unchanged and a phase random;
step S6: and verifying whether the original PCMI is the pure PCMI or not, and verifying the outlier characteristic of the original PCMI by adopting the substitute sample set to obtain the value of the pure PCMI.
Preferably, the original PCMI is calculated in step S4 based on the entropy values of the two time series and the joint entropytAnd ytThe original PCMI of (a) is described as:
PCMI(X；Y)＝H(X)+H(Y)-H(X,Y)。
preferably, in step S5, one of the two time series is selected to generate the substitute time series, and the substitute data is generated by using a transformation method with a random phase and a constant amplitude, and the number of times of substitution is selected to be 20 times.
Preferably, it is verified in step S6 whether the original PCMI is a genuine PCMI and compared to the substitute PCMI for the presence of an outlier, if so, the original PCMI is a genuine PCMI, otherwise, the original PCMI is a false PCMI, assuming H 01 and the probability p <0.05, then GPCMI ═ PCMIoI.e., the original PCMI; otherwise GPCMI is 0 or is named PCMIspI.e., pseudo-PCMI. The calculation of GPCMI is described below:
Figure BDA0002896415740000031
compared with the prior art, the invention has the following advantages:
1. according to the electroencephalogram signal extraction method, the electroencephalogram signal can be judged and analyzed through the electroencephalogram signal extracted from the database, from the viewpoint of symbol analysis, the signal is described by adopting a symbol dynamics method, and a sequencing mode is extracted from a time sequence to obtain rich time sequence information;
2. the invention estimates the real connection strength between channels by using phase randomization, and can accurately analyze the information interaction capacity between the brain area and the brain area, thereby accurately analyzing the information integration capacity of the brain and the complexity of the brain network.
3. The method solves the problem that the traditional mutual information method generates a false mutual information value, and can analyze the real coupling strength between the neural signal channels.
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FIG. 1 is a flow chart of the method of the present invention;
FIG. 2a is a PCMI analysis of the MNMM model of the invention;
FIG. 2b is a boxed plot of PCMI curves and alternative PCMI indices at each coupling strength.
Detailed Description
Exemplary embodiments, features and aspects of the present invention will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
The invention is further described below with reference to the accompanying drawings:
as shown in fig. 1, the present invention comprises the steps of:
step S1: collecting multi-channel electroencephalogram signals in an electroencephalogram database; and updating and storing a plurality of electroencephalogram signals in real time in the electroencephalogram database.
Step S2: preprocessing the acquired electroencephalogram signals, wherein large-amplitude noise should be removed, 50Hz power frequency signals are removed, and physiological noise components are quickly and independently analyzed and removed;
selecting two time sequences xtAnd ytN, constructing a vector X with an embedding dimension m and a lag τ using a phase space reconstruction methodt[xt,xt+τ,...,xt+mτ]And Yt[yt,yt+τ,...,yt+mτ]. Where τ is a particular sample point. Arranging the time series in ascending order and using XtAnd YtRespectively as a sorted time series
Figure BDA0002896415740000041
And
Figure BDA0002896415740000042
is denoted by symbol (b).
Step S3: calculating xtAnd ytThe edge Probability Distribution Function (PDF), denoted as p (x) and p (y), respectively. x is the number oftAnd ytIs defined as:
Figure BDA0002896415740000043
Figure BDA0002896415740000044
computing joint entropy based on joint probability function H (X, Y)
Figure BDA0002896415740000045
Step S4: time series xtAnd ytThe original PCMI of (a) is described as:
PCMI(X；Y)＝H(X)+H(Y)-H(X,Y)
step S5: a time series of substitutions is calculated. To reduce computational complexity, only one time series, e.g. x, is usedtOr ytTo generate an alternative time series. The generated substitute data is called as
Figure BDA0002896415740000046
The number of substitutions is chosen to be 20, and we can then obtain 20 alternative PCMI values and call them PCMIs。
Step S6: the pure PCMI was calculated. The original PCMI is referred to as PCMIoWhich deviate significantly from the alternative PCMIsDistribution of (2), then PCMIoIs considered to be Genuine PCMI (GPCMI). Otherwise, PCMIoriginalIs known as a pseudo PCMI (referred to as PCMI)sp)。
In the calculation of the PCMI, the selection of parameters such as the embedding dimension m, the lag τ, and the time window length is important. If the embedding dimension is too small (m 1 or 2), there are very few states in the time series that are significantly different, and the scheme will not work well. On the other hand, in order to properly distinguish between stochastic dynamics and deterministic dynamics, the time length of the time series should be greater than m! M! . Based on the model and the actual electroencephalogram test, we selected the parameter of PCMI as m-4, the time window length as 10s, and the overlap rate as 75%.
Three PCMI indices are mentioned in the present invention. (1) The original PCMI is a value derived by the PCMI algorithm. (2) The substitute PCMI is a PCMI value derived from the substitute data. (3) The genuine PCMI is the PCMI value in the substitution check. The genuine PCMI may be zero (no significant difference from the distribution of the substitute PCMI indices derived from the substitute data) or equal to the original PCMI value (significant deviation from the distribution of the substitute PCMI indices). Thus, the GPCMI values are divided into two groups: non-zero (referred to as N-GPCMI) and zero.
GPCMI evaluates the performance of the coupling model. PCMI was evaluated for its performance in tracking coupling strength and the MNMM model was validated. The true association between two channels in the neural signal was detected by GPCMI, the performance of which was verified by MNMM and surrogate data analysis.
The coupling model analysis results are shown in fig. 2. The change in PCMI with coupling strength from 10 to 25 was analyzed in the present invention. Shown in FIG. 2a as (A) - (D): when the coupling coefficient CC is 10, 15, 20 and 25, two time series of simulated neural oscillations (left part in each sub-graph) and their corresponding normalized power spectra (right part in each sub-graph). FIG. 2b shows: box plots of the PCMI curves and the alternative PCMI indices (step size 0.2) in each coupling strength. The PCMI value increases linearly with coupling strength, and is higher than three-quarters of the median of the alternative PCMI when the coupling coefficient is greater than 12.2. Significant analysis showed that GPCMI can accurately track model coupling performance when the distribution of PCMI values differs significantly (p <0.05) from the distribution of surrogate PCMI values when CC is greater than 12.
On the basis of analyzing the MNMM coupling model, the conclusion is drawn that the coupling of the nonlinear system can be effectively evaluated by combining the PCMI and the amplitude-invariant phase random transformation method. The signal coupling strength between different channels in the brain area and the signal coupling strength between the brain areas are accurately measured, so that the information integration and mutual communication capacity between the brain areas is judged.
Finally, it should be noted that: the above-mentioned embodiments are only used for illustrating the technical solution of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (4)

1. A method for judging coupling strength between signals based on multichannel neural signal analysis is characterized by comprising the following steps: which comprises the following steps:
step S1: collecting multi-channel electroencephalogram signals in an electroencephalogram database;
step S2: preprocessing the acquired electroencephalogram signals, wherein the preprocessing comprises removing large-amplitude noise, removing 50Hz power frequency signals and quickly analyzing independent components to remove physiological noise components;
selecting a time sequence x after two channel preprocessingtAnd ytPerforming m-dimensional phase space reconstruction, and arranging the reconstructed time sequences in an increasing order;
removing the large-amplitude noise of the acquired signal in the step S2, removing a 50Hz power frequency signal by using a notch filter, performing down-sampling on the signal, and removing the physiological noise by using a rapid independent component analysis method;
selecting two time sequences of the preprocessed electroencephalogram signals, selecting an embedded dimension m lag tau, performing m-dimensional phase space reconstruction, and arranging the reconstructed time sequences according to an increasing sequence;
selecting two time sequences xtAnd ytN, constructing a vector X with an embedding dimension m and a lag τ using a phase space reconstruction methodt[xt,xt+τ,...,xt+mτ]And Yt[yt,yt+τ,...,yt+mτ](ii) a Where τ is a specific sample point, arranging the time series in increasing order, and using XtAnd YtRespectively as a sorted time series
Figure FDA0003192269640000011
And
Figure FDA0003192269640000012
the symbol of (1);
step S3: separately calculate xtAnd ytThe edge probability distribution function calculates entropy values of the two time sequences, and calculates joint entropy of the two time sequences based on the joint probability function;
in step S3, edge probability distribution functions of the two time series are calculated, entropy values of the two time series are calculated according to the distribution functions, and joint entropy of the two time series is calculated based on the joint probability function,
in particular to calculate xtAnd ytEdge probability distribution functions, denoted p (x) and p (y), xtAnd ytIs defined as:
Figure FDA0003192269640000013
Figure FDA0003192269640000014
computing joint entropy based on joint probability function H (X, Y)
Figure FDA0003192269640000015
Step S4: calculating the original PCMI of the two time series;
step S5: selecting one of the two time sequences to generate a substitute time sequence, generating 20 groups of substitute data by adopting an amplitude-invariant phase random transformation method, and calculating a substitute PCMI (PCMI) based on a substitute sample;
step S6: and verifying whether the original PCMI is the pure PCMI or not, and verifying the outlier characteristic of the original PCMI by adopting the substitute sample set to obtain the value of the pure PCMI.
2. The method for determining coupling strength between signals based on multi-channel neural signal analysis according to claim 1, wherein:
in step S4, the original PCMI is calculated from the entropy values of the two time series and the joint entropytAnd ytThe original PCMI of (a) is described as:
PCMI(X；Y)＝H(X)+H(Y)-H(X,Y)。
3. the method for determining coupling strength between signals based on multi-channel neural signal analysis according to claim 1, wherein:
in step S5, one of the two time series is selected to generate a substitution time series, and the number of substitution times is selected to be 20 times, using substitution data generated by a method in which the amplitude is constant and the phase is random.
4. The method for determining coupling strength between signals based on multi-channel neural signal analysis according to claim 1, wherein:
verifying whether the original PCMI is the pure PCMI or not in the step S6, comparing the original PCMI with the substitute PCMI to determine whether the outlier distribution exists, and if the outlier distribution exists, determining that the original PCMI is the pure PCMI, otherwise, determining that the original PCMI is the pseudo PCMI; suppose H01 and the probability p <0.05, then GPCMI ═ PCMIoI.e., the original PCMI; otherwise GPCMI is 0 or is named PCMIspI.e., the calculation of the pseudo-PCMI, GPCMI is described below:
Figure FDA0003192269640000021
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