CN109144259A - It is a kind of based on it is multiple dimensioned arrangement transfer entropy brain area between synchronized relation analysis method - Google Patents
It is a kind of based on it is multiple dimensioned arrangement transfer entropy brain area between synchronized relation analysis method Download PDFInfo
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Abstract
The invention discloses synchronized relation analysis methods between a kind of brain area based on multiple dimensioned arrangement transfer entropy；The present invention acquires 6 Healthy Peoples and 5 patients with cerebral apoplexy 32 channel EEG signals when different grips export first, during research grip output, the synchronized relation of brain motor area and sensory area, using multiple dimensioned arrangement transfer entropy algorithm, to the lead C3 of brain motor area and motor sensory area, the EEG signals in multiple channels such as C4, CP5, CP6 synchronize coupling analysis；When determining the output of right-hand man's grip, the synchronized relation of brain motor area and sensory area.Multiple dimensioned arrangement transfer entropy method proposed in this paper can portray the synchronous characteristic to interact between different time-frequency scales between brain area and Information Transmission Properties, the synchronizing characteristics between brain area under multiple dimensioned multiband is embodied, further to explore the rehabilitation of human motion control system and dyskinesia for theoretical foundation.
Description
Technical field
The invention belongs to field of signal processing, are related to a kind of synchronism analysis method of EEG signals, in particular to a kind of
Analysis method applied to synchronous regime between brain area.
Background technique
When EEG signals (Electroencephalogram, EEG) result from the neural cell group activity of cerebral function area
Potential change is a kind of objective expression of electrical brain activity, has important value to the deep pathogenic mechanism for understanding brain.
It is mutually coordinated between each brain area of brain, integrate information, the orderly function of brain is realized, based between EEG research Different brain region
Synchronous coupled relation has become the hot spot of people's research.
Based on EEG signal measurement brain synchronization index be broadly divided into two classes: i.e. linear synchronous measure with it is non-linear
Synchronization metric.Linear synchronous measurement can only reflect the linear correlation between system, rather than linear synchronous Measure Indexes can be anti-
Mirror complicated between different zones, nonlinear degree of coupling in brain.Common non-linear synchronized algorithm includes that phase is same
Step, mutual information, coherent analysis etc..Wherein mutual information is one of the most important information theory for calculating dependence, but mutually
To the more demanding of data length when information calculates, and EEG limited length in practice, the change of its state is estimated from signal
The probability-distribution function of amount is not accurate enough.In order to overcome this limitation, arrangement mutual trust is proposed according to pattern of rows and columns of signal
Breath method analyzes the coherence of Healthy People and epileptic's EEG signal.In order to characterize the bidirectional couple feature of signal, Glan
Outstanding causality analysis is applied to the synchronous research of brain myoelectricity.It can not be to non-linear, multiple dimensioned life for Granger causality
Reason system is effectively analyzed, and thanks to equality people using the coupled relation between multiple dimensioned transfer entropy analysis brain myoelectricity, cortex muscle function
It is the most significant that β frequency range can be coupled in.
Although transfer entropy has the advantages of non-linear, multiscale analysis, but transfer entropy is suitable for longer signal, EEG
The limited length of signal, the probability-distribution function that its state variable is estimated from signal is not accurate enough, from the arrangement mould of signal
Available accurate Distribution estimation in formula, for this problem, pattern of rows and columns of binding signal is in probability distribution
Estimation and transfer entropy is multiple dimensioned, advantage of nonlinear analysis, proposes multiple dimensioned arrangement transfer entropy (Multiscale Permut
Ation Transfer Entropy, MPTE), it is quantitative to coupling analysis between health volunteer and patients with cerebral apoplexy progress brain area
Coupling feature and Information Transmission Properties between brain area in the case where multiband is multiple dimensioned is described, further to explore human motion control
The function evaluation methods of system mechanism and athletic rehabilitation provide foundation.
Summary of the invention
In view of the deficiencies of the prior art, the present invention proposes a kind of brain interval synchronizations based on multiple dimensioned arrangement transfer entropy
Relationship analysis method.
Although transfer entropy has the advantages of non-linear, multiscale analysis, be widely used in the synchronous coupled relation of physiological system
Analysis, but transfer entropy be suitable for longer signal, its shape can not be accurately estimated from the EEG signal of limited length
The probability-distribution function of state variable, and the available accurate Distribution estimation from pattern of rows and columns of signal, for
The probability distribution of this problem, pattern of rows and columns of binding signal is estimated and transfer entropy is multiple dimensioned, advantage of nonlinear analysis, mentions
Gone out multiple dimensioned arrangement transfer entropy, analyze Healthy People and patients with cerebral apoplexy when grip exports brain motor area and sensory area it
Between coupled relation.
Synchronized relation analysis method between a kind of brain area based on multiple dimensioned arrangement transfer entropy of the present invention, this method is specifically wrapped
Include following steps:
(1) synchronous acquisition 32 channel EEG signals in the output of different grips；
(2) channel C3, C4, CP5, CP6 EEG signals obtained in selecting step (1), carry out wavelet threshold to it and disappear
It makes an uproar processing, filters out the influence of position and other noises；
(3) uses bandpass filter, extracts the different frequency range of EEG signal, respectively θ: 4-8hz, α: 8-14hz, β:
15-35hz,γ:35-50hz；
(4) uses multiple dimensioned arrangement transfer entropy algorithm, carries out to the EEG signal for the different frequency range that step (3) are extracted same
Step analysis；
Multiple dimensioned arrangement transfer entropy algorithm is implemented as follows:
A. time series symbolism
Symbol division is carried out to original series according to the numerical value feature of original series, the time series X for being n for lengtht=
{x1,x2,x3... .. } symbolism formula it is as follows:
S is the quantity of symbol in formula (2)；
B. phase space reconfiguration
To XSCarry out phase space reconfiguration:
Wherein m is Embedded dimensions, and τ is delay factor；
C. the probability distribution of pattern of rows and columns
Time reproducing sequence is arranged by ascending order:
Obtain πj={ j1,j2... .. } indicate each original location index of element in reconstitution time sequence, sequence shares m！
Kind arrangement is possible, counts times N that each sequence occursπj, the probability of appearance are as follows:
D. multiple dimensioned arrangement transfer entropy is obtained
Finally according to formula (6), multiple dimensioned arrangement transfer entropy is obtained
If Xt,YtThe time series for being n for two length, respectively since the history of t-1 to t-p is denoted as
I(X；Y-|X-) indicate X and Y-In X-Under the conditions of mutual information；Then YtTo XtMultiple dimensioned arrangement transfer entropy be defined as X and Y-In X-
Under mutual information；
The definition of the stiffness of coupling of e.EEG signal different frequency range
For the power of the synchronized relation between the multiple dimensioned lower EEG signals of quantitative description multiband, multiple dimensioned arrangement is utilized
Transfer entropy combines the conspicuousness coherent area evaluation method in synchronous research, characterizes stiffness of coupling, coupling with conspicuousness coherent area
Close intensity can quantitative description EEG signals synchronized relation power；Scale is the conspicuousness coherent area of p is defined as:
In formula (7), Ap is conspicuousness correlation area, and Ap is bigger, shows that the synchronized relation between brain electricity is stronger；Δ f indicates frequency
Rate resolution ratio, MPTEp(f) indicate that scale is p, in the multiple dimensioned arrangement transfer entropy of f frequency range.The present invention and it is existing it is many with
EEG coupling analytical method is compared between moving relevant brain area, is had a characteristic that
Although transfer entropy has the advantages of non-linear, multiscale analysis, be widely used in the synchronous coupled relation of physiological system
Analysis, but transfer entropy be suitable for longer signal, its shape can not be accurately estimated from the EEG signal of limited length
The probability-distribution function of state variable, and the available accurate Distribution estimation from pattern of rows and columns of signal, for
The probability distribution of this problem, pattern of rows and columns of binding signal is estimated and transfer entropy is multiple dimensioned, advantage of nonlinear analysis, mentions
Multiple dimensioned arrangement transfer entropy is gone out, multiple dimensioned arrangement transfer entropy is it can be concluded that synchronized relation between more accurate brain area.
Detailed description of the invention
Fig. 1 is eeg signal acquisition scene photo.
Fig. 2 is brain wave acquisition channel position figure.
Stiffness of coupling of the motor area to sensory area when Fig. 3 (a) is the output of health volunteer's left hand 5kg grip；
Stiffness of coupling of the motor area to sensory area when Fig. 3 (b) is the output of health volunteer's left hand 10kg grip.
Stiffness of coupling of the motor area to sensory area when Fig. 3 (c) is the output of health volunteer's left hand 20kg grip.
Stiffness of coupling of the sensory area to motor area when Fig. 3 (d) is the output of health volunteer's left hand 5kg grip.
Stiffness of coupling of the sensory area to motor area when Fig. 3 (e) is the output of health volunteer's left hand 10kg grip.
Stiffness of coupling of the sensory area to motor area when Fig. 3 (f) is the output of health volunteer's left hand 20kg grip.
Stiffness of coupling of the motor area to sensory area when Fig. 4 (a) is the output of health volunteer's left hand 5kg grip；
Stiffness of coupling of the motor area to sensory area when Fig. 4 (b) is the output of health volunteer's left hand 10kg grip.
Stiffness of coupling of the motor area to sensory area when Fig. 4 (c) is the output of health volunteer's left hand 20kg grip.
Stiffness of coupling of the sensory area to motor area when Fig. 4 (d) is the output of health volunteer's left hand 5kg grip.
Stiffness of coupling of the sensory area to motor area when Fig. 4 (e) is the output of health volunteer's left hand 10kg grip.
Stiffness of coupling of the sensory area to motor area when Fig. 4 (f) is the output of health volunteer's left hand 20kg grip.
Comparison of the motor area to sensory area stiffness of coupling when Fig. 5 (a) is the output of health volunteer right-hand man's grip.
Fig. 5 (b) be health volunteer right-hand man's grip export when sensory area to motor area stiffness of coupling comparison.
Ratio of the motor area to sensory area stiffness of coupling when Fig. 6 (a) is patients with cerebral apoplexy and the output of Healthy People left hand grip
Compared with.
Ratio of the sensory area to motor area stiffness of coupling when Fig. 6 (b) is patients with cerebral apoplexy and the output of Healthy People left hand grip
Compared with.
Ratio of the motor area to sensory area stiffness of coupling when Fig. 6 (c) is patients with cerebral apoplexy and the output of Healthy People right hand grip
Compared with.
Ratio of the sensory area to motor area stiffness of coupling when Fig. 6 (d) is patients with cerebral apoplexy and the output of Healthy People right hand grip
Compared with.
Specific embodiment
Elaborate with reference to the accompanying drawing to the embodiment of the present invention: the present embodiment is being with technical solution of the present invention
Under the premise of implemented, the detailed implementation method and specific operation process are given.
As shown in Figure 1, the present embodiment includes the following steps:
Step 1, when synchronous acquisition difference grip exports, it is as follows that 32 channel EEG signals specifically acquire operation:
11 subjects are selected herein, wherein having 6 health volunteers, 5 patients with cerebral apoplexy, subject's concrete condition
As shown in table 1.All subjects are familiar with experiment flow and have obtained sufficient rest before the experiments.Quietly testing
Interior, subject sit up straight on Wooden chair, gripe dynamometer are placed in subject's palm of the hand, front display screen refers to for issuing
Show signal, carries out the output experiment of 5kg, 10kg and 20kg grip respectively.Subject is after receiving signal, immediately by grip
Meter is held from zero graduation to target scale, is persistently exported grip, is kept gripe dynamometer 5 seconds at target scale；Then grip is unclamped
Meter is rested 20 seconds, then carries out next group of grip experiment, carries out 10 groups of experiments respectively under the conditions of identical grip.
1 subject's details of table
In Guangdong Province, industrial injury recovery centre carries out the acquisition experiment of EEG signals, tests collection site as shown in Fig. 1.Number
According to acquisition it is used be 128 to lead BrainAmp DC eeg collection system (Brain Products GmbH, Germany).It is synchronous
Acquire above-mentioned experimental paradigm lower 32 channels brain electricity number (FP1, FP2, F7, F8, F4, F3, FZ, FC5, FC1, FC2, FC6,
T7, C3, CZ, C4, T8, CP5, CP1, CP2, CP6, TP9, P7, P3, PZ, P4, P8, TP10, PO9, O1, OZ, O2, PO10),
The location drawing in each channel is as shown in Figure 2.Electrode for encephalograms uses world 10-20 system standard, is with reference to electricity with the mastoid process of ears
Position, has recorded 32 channel EEG signals.Before electrode placement, scalp has been cleaned up, sample frequency 1000Hz.To grind
During studying carefully grip output, the coupled relation of the motor area EEG and sensory area, to the lead of brain motor area and motor sensory area
The EEG signals in the channel C3, C4, CP5, CP6 are analyzed.
Step 2, the C3, C4, CP5 obtained in selecting step one, the channel CP6 EEG signals carry out wavelet threshold to it
Denoising filters out electrocardio, the influence of position and other noises.
Step 3 extracts the different rhythm and pace of moving things (θ: 4-8hz, α: 8-14hz, β: the 15- of EEG signal using bandpass filter
35hz,γ:35-50hz)
Step 4 synchronizes analysis to the EEG signal of different frequency range using multiple dimensioned arrangement transfer entropy algorithm.
(1) Synchronization Analysis between brain area under multiband is multiple dimensioned
When studying six health volunteer's grip output, each frequency range (θ: 4-8hz, α: 8-14hz, β: 15- of EEG
35hz, γ: 35-50hz) stiffness of coupling under different scale.By Fig. 3 (a)-(f), Fig. 4 (a)-(f) is as can be seen that brain area
Between have bidirectional couple, right-hand man's difference grip output under, the stiffness of coupling of EEG signals β frequency range is most strong, EEG signals
Between synchronism it is the most significant in β frequency range.There is this feature in all subjects.Existing research shows motor cortex β frequency
The synchronous concussion of section maintains the stable state output of movement, and brain motor cortex and muscle are the most significant in the synchronized relation of β frequency range,
This is most significantly consistent with synchronous between motor area and sensory area in β frequency range.Under different scale, such as figure Fig. 3 (a)-
(f), 2 in Fig. 4 (a)-(f), 6,10 low scales, the synchronized relation of each frequency range are all fainter；And under higher scale, such as
18 in figure, 22,26,30, the synchronized relation of each frequency range is remarkably reinforced.This is because when scale is lower, it is big in meeting lossing signal
The detailed information of amount can not capture multidate information therein well；However scale is too big, is influenced excessively, to aggravate by details
The influence of noise reduces the robustness of algorithm, increases algorithm complexity.Compare for the ease of further analyzing, chooses one
A suitable scale, the selection principle of scale are can neither to lose the too many information of EEG signals, ensure higher robust again
Property and lower time complexity, Binding experiment is as a result, subsequent analysis mesoscale is selected as 18.
(2) Synchronization Analysis when right-hand man acts between brain area
Further analyze the difference of synchronism between brain area when right-hand man's grip exports.To the most significant β frequency range coupling of synchronism
Intensity is compared.Mean value and standard deviation such as Fig. 5 of six health volunteer (S1-S6) opposite side brain areas in β frequency range stiffness of coupling
(a), shown in Fig. 5 (b).It was found that the stiffness of coupling right hand of allocinesi area and sensory area is more stronger than left hand when grip exports, but
Stiffness of coupling is not as the increase of grip generates the change of tendency.Show opposite side (left side) brain when right hand grip output
It is more significant when motor area concussion movement synchronous with sensory area is compared with left hand grip.In view of the strong hand of 6 health volunteers
It is the right hand, is known by opposite side Controlling principle, left side brain area has obtained more exercises and exploitation in daily life.This with
Experimental result is consistent.The opposite side brain area of the right hand has stronger information exchange energy compared with the opposite side brain area of left hand when grip exports
Power.
(3) Synchronization Analysis between Healthy People and the brain area of patients with cerebral apoplexy
Stiffness of coupling between health volunteer and patients with cerebral apoplexy brain area is compared.Due to patients with cerebral apoplexy grip
20kg can not be arrived, the EEG signals when output of 5kg, 10kg grip are only acquired in experiment.6 Healthy Peoples and 5 cerebral apoplexies are suffered from
Shown in mean variance such as Fig. 6 (a)-(d) of the stiffness of coupling of person's EEG signals β frequency range.It can be found that when grip exports, in β
Frequency range, the stiffness of coupling between Healthy People brain area are stronger compared with patient.Show Healthy People grip output when motor area and sensory area it is same
Step property is better than patients with cerebral apoplexy.To find out its cause, the cerebral cortex of patients with cerebral apoplexy is due to by damage, making it move function
Different degrees of decline can be generated.Information exchange between the brain area of patients with cerebral apoplexy is weaker than Healthy People, this is also likely to be brain soldier
One of the reason of middle patient motion hypofunction.
Stiffness of coupling when finally exporting respectively to health volunteer with cerebral apoplexy patient right-hand man's difference grip does independence
Sample T is examined, its significant difference is analyzed, from table 2 it can be seen that health volunteer and patients with cerebral apoplexy right-hand man's grip are defeated
When out, the P value of stiffness of coupling is respectively less than 0.05, and there are significant differences.
The significance analysis of motor area and sensory area stiffness of coupling when 2 patients with cerebral apoplexy of table and Healthy People grip export
Claims (1)
1. synchronized relation analysis method between a kind of brain area based on multiple dimensioned arrangement transfer entropy, which is characterized in that this method is specific
The following steps are included:
(1) synchronous acquisition 32 channel EEG signals in the output of different grips；
(2) channel C3, C4, CP5, CP6 EEG signals obtained in selecting step (1), carry out at wavelet threshold denoising it
Reason, filters out the influence of position and other noises；
(3) uses bandpass filter, extracts the different frequency range of EEG signal, respectively θ: 4-8hz, α: 8-14hz, β: 15-
35hz,γ:35-50hz；
(4) uses multiple dimensioned arrangement transfer entropy algorithm, synchronizes point to the EEG signal of the different frequency range of step (3) extraction
Analysis；
Multiple dimensioned arrangement transfer entropy algorithm is implemented as follows:
A. time series symbolism
Symbol division is carried out to original series according to the numerical value feature of original series, the time series X for being n for lengtht={ x1,
x2,x3... .. } symbolism formula it is as follows:
S is the quantity of symbol in formula (2)；
B. phase space reconfiguration
To XSCarry out phase space reconfiguration:
Wherein m is Embedded dimensions, and τ is delay factor；Indicate time series X, whole states from time t to time S；
C. the probability distribution of pattern of rows and columns
Time reproducing sequence is arranged by ascending order:
Obtain πj={ j1,j2... .. } indicate each original location index of element in reconstitution time sequence, sequence shares m！Kind row
Column are possible, count times N that each sequence occursπj, the probability of appearance are as follows:
D. multiple dimensioned arrangement transfer entropy is obtained
Finally according to formula (6), multiple dimensioned arrangement transfer entropy is obtained
If Xt,YtThe time series for being n for two length, respectively since the history of t-1 to t-p is denoted as I
(X；Y-|X-) indicate X and Y-In X-Under the conditions of mutual information；Then YtTo XtMultiple dimensioned arrangement transfer entropy be defined as X and Y-In X-Under
Mutual information；
The definition of the stiffness of coupling of e.EEG signal different frequency range
For the power of the synchronized relation between the multiple dimensioned lower EEG signals of quantitative description multiband, multiple dimensioned arrangement transfer entropy is utilized
In conjunction with the conspicuousness coherent area evaluation method in synchronous research, stiffness of coupling, stiffness of coupling are characterized with conspicuousness coherent area
Can quantitative description EEG signals synchronized relation power；Scale is the conspicuousness coherent area of p is defined as:
In formula (7), Ap is conspicuousness correlation area, and Ap is bigger, shows that the synchronized relation between brain electricity is stronger；Δ f indicates frequency point
Resolution, MPTEp(f) indicate that scale is p, in the multiple dimensioned arrangement transfer entropy of f frequency range.
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| CN110367974A (en) * | 2019-07-10 | 2019-10-25 | 南京邮电大学 | Research method based on the coupling of variation mode decomposition-transfer entropy brain myoelectricity |
| CN111067514A (en) * | 2020-01-08 | 2020-04-28 | 燕山大学 | Multi-channel electroencephalogram coupling analysis method based on multi-scale multivariable transfer entropy |
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| CN112617862A (en) * | 2020-01-15 | 2021-04-09 | 燕山大学 | Method for judging coupling strength between signals based on multichannel neural signal analysis |
| CN111227830A (en) * | 2020-02-14 | 2020-06-05 | 燕山大学 | Electroencephalogram and electromyographic coupling analysis method based on complex improved multi-scale transfer entropy |
| CN111708978A (en) * | 2020-07-23 | 2020-09-25 | 杭州电子科技大学 | Multi-scale time-frequency inter-muscle coupling analysis method |
| CN111708978B (en) * | 2020-07-23 | 2023-05-02 | 杭州电子科技大学 | Multi-scale time-frequency inter-muscle coupling analysis method |
| CN112244870A (en) * | 2020-09-24 | 2021-01-22 | 杭州电子科技大学 | Epilepsy electroencephalogram bidirectional coupling analysis method based on symbolic permutation transfer entropy |
| CN112244880A (en) * | 2020-09-24 | 2021-01-22 | 杭州电子科技大学 | Emotion-induced electroencephalogram signal analysis method based on variable-scale symbol compensation transfer entropy |
| CN112244870B (en) * | 2020-09-24 | 2022-02-22 | 杭州电子科技大学 | Epilepsy electroencephalogram bidirectional coupling analysis method based on symbolic permutation transfer entropy |
| CN114259242A (en) * | 2021-12-27 | 2022-04-01 | 杭州电子科技大学 | Functional cortical muscle coupling method based on multi-time scale transfer spectrum entropy |
| CN114259242B (en) * | 2021-12-27 | 2024-04-12 | 杭州电子科技大学 | Functional cortical muscle coupling method based on multi-time scale transfer spectrum entropy |
| CN116035597A (en) * | 2023-02-03 | 2023-05-02 | 首都医科大学宣武医院 | Electroencephalogram signal coupling analysis method, device and system |
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