CN106073702B - Based on the more time-frequency scale diencephalon myoelectricity coupling analytical methods of small echo-transfer entropy - Google Patents
Based on the more time-frequency scale diencephalon myoelectricity coupling analytical methods of small echo-transfer entropy Download PDFInfo
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- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
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- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
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- A61B5/369—Electroencephalography [EEG]
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- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
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Abstract
The invention discloses a kind of based on the more time-frequency scale diencephalon myoelectricity coupling analytical methods of small echo-transfer entropy, the method includes brain electromyography signal synchronous acquisition part and signal processing, brain electromyography signal synchronous acquisition part includes eeg signal acquisition and electromyographic signal collection；Signal processing includes small echo-transfer entropy analysis method of Signal Pretreatment and brain myoelectricity.The present invention has applicability, admissibility, has important application value in medical science of recovery therapy field.
Description
Technical field
The present invention relates to neural rehabilitation project and locomotory mechanism research fields, and in particular to one kind is based on small echo-transfer entropy
More time-frequency scale diencephalon myoelectricity coupling analytical methods.
Background technique
Brain electricity (electroencephalogram, EEG) and myoelectricity (electromyographic, EMG) signal wraps respectively
The functional response information that information and muscle are intended to brain control is controlled containing somatic movement, the multi-scale coupling between brain electromyography signal
The multi-level cortex of message reflection-muscle function coupling (Functional corticomuscular coupling, FCMC)
Link information.Currently, brain myoelectricity synchronous characteristic research is based primarily upon coherence analysis, obtains the driving of brain motor mindedness and transported with muscle
Functional cohesion feature between dynamic response, but traditional coherent analysis can not embody coupling direction character.To be best understood from
Function between cerebral cortex and respective muscle interacts and information transmission characteristic, and it is same that Granger Causality analysis is applied to brain myoelectricity
Step research finds that there are two-way (downlink EEG → EMG, uplink EMG → EEG) couplings to contact between brain myoelectricity.But due to brain myoelectricity it
Between Function Coupling between coupling model is unknown and brain electromyography signal there is nonlinear causal relationship, the Glan based on set model
Outstanding causality analysis method cannot effectively describe brain myoelectricity Non-linear coupling feature.Transfer entropy has independent of set model and reality
The characteristics of existing quantitative analysis of nonlinear, it can effectively estimate the Function Coupling intensity and information transfer side between cortex-muscle
To.Therefore, transmitting entropy model is for the Function Coupling intensity and information direction of transfer feature, announcement between estimation cortex-muscle
Motion control and response mechanism in motion process between cortex and muscle have feasibility.2015, author of the present invention " had been based on
The brain electromyography signal coupling analysis of multiple dimensioned transfer entropy " in it is proposed that multiple dimensioned transfer entropy method, and not based on this method research
With brain electromyography signal coupling feature in time scale.But with the increase of coarse scale, sequence length reduces, may make
Entropy estimate inaccuracy.The it is proposed of mobile equalization overcomes this drawback, so that the length of time series of each scale keeps phase
Together.But the above research still has several drawbacks: coarse is only when carrying out brain electricity and electromyography signal with mobile equalization method
Between it is sized, can not depict brain electricity and myoelectricity time-frequency domain characteristic and different time-frequency scale diencephalon electromyography signal it is non-thread
Property coupling and information transmitting.
Summary of the invention
The purpose of the present invention is to provide one kind it can be found that non-linear dependencies between cortex muscle, further investigation brain skin
Layer muscle between coupling and information transfer characteristic based on the more time-frequency scale diencephalon myoelectricity coupling analysis of small echo-transfer entropy
Method.
The step of the method for the invention, is as follows:
Step 1, Neuroscan equipment synchronous acquisition EEG signals and electromyography signal are led in use 64；
Step 2, collected EEG signals and electromyography signal are gone respectively using Neuroscan device data processing software
Except baseline drift, spilling, eye movement and Hz noise；
Step 3, spectral decomposition, analysis are carried out to brain electricity and electromyography signal using Daubechies class db4 wavelet basis function
Synchronizing characteristics between EEG signals and electromyography signal difference time-frequency scale, Non-linear coupling and information transmitting between quantitative description brain flesh
Feature；
Step 4, motor function analysis is carried out between Non-linear coupling brain flesh and information transfer characteristic.
Further, in step 1, electrode for encephalograms uses world 10-20 system standard, using the mastoid process of ears as ginseng
It examines, the EEG signals of corresponding movement is recorded from 32 top guide skin brain wave acquisition equipment；Electromyography signal is acquired using Synamp2 equipment,
Electrode is placed at belly of muscle position along muscle fibre direction.
Further,
The specific method is as follows for the step 3:
Based on measured data pretreated in step 2 building EEG signals x (t) and two groups of time sequences of electromyography signal y (t)
Column；Wavelet transformation is carried out to EEG signals x (t), wavelet transformation is applied among partition of the scale；
Wavelet structure function, formula are as follows first:
Ψj,k(t)=2-j/2Ψ(2-jt-k) (1)
In formula, Ψ (t) is morther wavelet；Translational movement of the k for Ψ (t) ordinate direction, the number of plies of j expression signal, j, k ∈ Z,
Z is set of integers；Scale parameter is 2j, translation parameters 2jk；T is time index；
Then 7 layers of spectral decomposition are carried out to EEG signals x (t), obtains wavelet conversion coefficient
Wavelet coefficient Cj,kAccording to the sequence arrangement of frequency range from high to low, extract the 3rd, 4,5,6,7 layer coefficients reconstruct
Gamma (32~64Hz), beta (16~32Hz), alpha (8~16Hz), theta (4~8Hz) and delta (1~4Hz) frequency
The signal of section:
In formula,For the quality factor of filter；
Reconstruction signal x1(t)、x2(t)、x3(t)、x4(t) and x5(t) respectively correspond brain Electricity Functional frequency band delta, theta,
The signal of alpha, beta and gamma；
Above-mentioned identical wavelet transform procedure is carried out for electromyography signal y (t), obtains ym(t) (m=1,2,3,4,5), point
The signal of myoelectricity delta, theta, alpha, beta and gamma frequency range is not corresponded to；
Based on transfer entropy calculation method, small echo-transfer entropy WTE that x (t) arrives y (t) is constructedx→y, formula is as follows:
In formula, u is predicted time；Joint probability of the p () between variable；Respectively indicate brain electricity and myoelectricity
The delay vector of delta, theta, alpha, beta, gamma component；ForForecasting sequence；
WTEx→yThen indicate the x of EEG signals EEGi(t) y of the component to electromyography signal EMGm(t) transfer entropy between component
Value；Similarly signal y (t) arrives small echo-transfer entropy WTE of x (t)y→xExpression formula are as follows:
In formula,ForForecasting sequence；WTEy→xIndicate the y of EMGm(t) to component to the x of EEGi(t) between component
Transmit entropy；It is bigger to transmit entropy, illustrates that the coupling of cortex muscle is stronger between this frequency range；Vice versa.
The present invention has the beneficial effect that compared with prior art: the method for the present invention analyzes brain myoelectricity using small echo-transfer entropy
Signal message transmission characteristic, it is nonlinear between quantitative description EEG signals and electromyography signal frequency range to couple and information transfer characteristic,
Help to explore the functional cohesion between cerebral cortex and muscle, study movement controls feedback mechanism and dyskinesia pathology machine
System establishes the rehabilitation state evaluation index based on brain electromyography signal, constructs healing robot motion state and patient physiological condition
Evaluation mechanism can obtain considerable Social benefit and economic benefit.
Detailed description of the invention
Fig. 1 is the structure diagram of Neuroscan equipment in the present invention.
Fig. 2 is the work flow diagram of the method for the present invention.
Fig. 3 is time-frequency result figure after the wavelet decomposition of the channel subject C4 EEG signals.
Fig. 4 is time-frequency result figure after the wavelet decomposition of electromyography signal at subject's musculus flexor digitorum sublimis.
Fig. 5 is that brain electromyography signal small echo-transfer entropy of subject analyzes result figure.
Drawing reference numeral: 1- electrode for encephalograms, 2- electrode cap, 3- brain myoelectricity Acquisition Instrument, 4- myoelectricity conducting wire, 5- electromyographic electrode.
Specific embodiment
EEG signals and electromyography signal are very faint, have the characteristics that non-linear, non-stationary and frequency domain characteristic is prominent.?
In motion process, the interactive controlling mechanism between nervous system and muscle can pass through the synchronous coupling analysis body of brain electromyography signal
It is existing.Wavelet decomposition can extract the specific time-frequency data segment of brain electromyography signal, and transfer entropy can portray it is non-thread between signal
Property coupling and information transfer characteristic, the present invention by research brain myoelectricity between small echo-transfer entropy analysis, obtain different motion shape
Information transfering relation between cerebral cortex and muscle under state, and then the physiological mechanism that study movement dysfunction generates.
Embodiment 1:
As shown in Fig. 2, method and step is as follows:
Step 1, Neuroscan equipment synchronous acquisition EEG signals and electromyography signal are led in use 64.
The structure of Neuroscan equipment is as shown in Figure 1, by electrode for encephalograms, electrode cap, brain myoelectricity Acquisition Instrument, myoelectricity lead
Line, electromyographic electrode connection composition.
Eeg signal acquisition: electrode for encephalograms adopt international standards 10-20 electrode place standard, pass through electrode cap 2 realize brain
Electrode 1 is contacted with scalp.The experiment of brain electromyography signal synchronous acquisition is carried out under the output movement of hand static grip.M1, M2 are led
Mastoid process is hit exactly overhead as reference electrode, ground electrode arrangement after connection is connected respectively to left and right ear, is adopted from 32 top guide skin brain electricity
Collect the EEG signals of record corresponding movement in the selection area C3, C4 and CPZ in equipment.
Electromyographic signal collection: musculus flexor digitorum sublimis (flexor digitorum is acquired using Synamp2 equipment
Superficialis, FDS) at electromyography signal, be tested the skin surface at position with alcohol wipe first, remove skin surface
Then electromyographic electrode 5 is pasted at belly of muscle position by grease and scurf along muscle fibre direction, and myoelectricity conducting wire 4 is appropriate
It is fixed to reduce the interference that conducting wire shakes in motion process to the greatest extent.
Step 2, collected EEG signals and electromyography signal are gone respectively using Neuroscan device data processing software
Except baseline drift, spilling, eye movement and Hz noise；
Step 3, spectral decomposition is carried out to brain electricity and electromyography signal using Daubechies class db4 wavelet basis function, be based on
Pretreated measured data building EEG signals x (t) and two groups of time serieses of electromyography signal y (t) in step 2.With brain telecommunications
For number x (t), wavelet transformation is applied among partition of the scale.Wavelet structure function, formula are as follows first:
Ψj,k(t)=2-j/2Ψ(2-jt-k) (1)
In formula, Ψ (t) is morther wavelet；Translational movement of the k for Ψ (t) ordinate direction, the number of plies of j expression signal, j, k ∈ Z,
Z is set of integers；Scale parameter is 2j, translation parameters 2jk；T is time index.
Then 7 layers of spectral decomposition are carried out to EEG signals x (t), obtains wavelet conversion coefficient
The wavelet coefficient C obtained based on above formulaj,kAccording to frequency range from high to low sequence arrangement, extract the 3rd, 4,5,
6,7 layer coefficients reconstruct gamma (32~64Hz), beta (16~32Hz), alpha (8~16Hz), theta (4~8Hz) and
The signal of delta (1~4Hz) frequency range:
In formula,For the quality factor of filter；
Reconstruction signal x1(t)、x2(t)、x3(t)、x4(t) and x5(t) respectively correspond brain Electricity Functional frequency band delta, theta,
The signal of alpha, beta and gamma.
Above-mentioned identical wavelet transform procedure is carried out for electromyography signal y (t), obtains ym(t) (m=1,2,3,4,5), point
The signal of myoelectricity delta, theta, alpha, beta and gamma frequency range is not corresponded to.
Based on transfer entropy calculation method, small echo-transfer entropy WTE that x (t) arrives y (t) is constructedx→y, formula is as follows:
In formula, u is predicted time；Joint probability of the p () between variable；Respectively indicate brain electricity and myoelectricity
The delay vector of delta, theta, alpha, beta, gamma component；ForForecasting sequence；
WTEx→yThen indicate the x of EEG signals EEGi(t) y of the component to electromyography signal EMGm(t) transfer entropy between component
Value.Similarly signal y (t) arrives small echo-transfer entropy WTE of x (t)y→xExpression formula are as follows:
In formula,ForForecasting sequence；WTEy→xIndicate the y of EMGm(t) to component to the x of EEGi(t) between component
Transmit entropy；It is bigger to transmit entropy, illustrates that the coupling of cortex muscle is stronger between this frequency range；Vice versa.
It based on These parameters, calculates under the output movement of hand static grip, on difference coupling direction, between different time-frequency scales
WTE value, can non-linear synchronous coupling between quantitative description EEG signals EEG and the more time-frequency scales of electromyography signal EMG it is special
Sign.
For the feasibility and validity for verifying brain electromyography signal small echo-transfer entropy analysis method of the present invention, 8 are raised
The subject of name health carries out the output experiment of hand static grip, and subject's relevant information is as shown in table 1.According to of the present invention
Brain myoelectricity acquisition and analytic process, synchronous acquisition subject's constant force output movement under brain electromyography signal, analyzed and ground
Study carefully the coupling in subject motion's motion process between cortex muscle and information transmission mechanism.
Myoelectricity at this experiment acquisition left hand musculus flexor digitorum sublimis (flexor digitorum superficialis, FDS)
Signal and the channel opposite side C4 EEG signals, and calculate WTE value.
Fig. 3 and Fig. 4 is respectively subject's brain electricity, time-frequency domain knot of the electromyography signal after wavelet decomposition on each time-frequency scale
Fruit (left side is time-domain diagram, and right side is frequency domain figure), it can be seen that brain electricity and electromyography signal can obtain after wavelet decomposition
To the signal of delta, theta, alpha, beta and gamma frequency range.
Average value after the small echo that Fig. 5 is coupled between 8 subject's brain fleshes-transfer entropy analysis.There it can be seen that quiet
The stiffness of coupling of beta frequency range is the most significant between cortex muscle during the output of state grip, and each time-frequency on different coupling directions
There is also differences for stiffness of coupling between scale, provide theoretical study method to probe into neuromuscular function coupling mechanism.
1 subject's relevant information of table.
Claims (1)
1. a kind of based on the more time-frequency scale diencephalon myoelectricity coupling analytical methods of small echo-transfer entropy, which is characterized in that the method
The step of it is as follows:
Step 1, Neuroscan equipment synchronous acquisition EEG signals and electromyography signal are led in use 64；
Step 2, base is removed respectively to collected EEG signals and electromyography signal using Neuroscan device data processing software
Line drift, spilling, eye movement and Hz noise, for pretreated measured data building EEG signals x (t) and electromyography signal y
(t) two groups of time serieses；
Step 3, spectral decomposition is carried out to brain electricity and electromyography signal using Daubechies class db4 wavelet basis function, analyzes brain
Synchronizing characteristics between electric signal and electromyography signal difference time-frequency scale, Non-linear coupling and information transmitting are special between quantitative description brain flesh
Sign；Specific steps include:
1. EEG signals x (t) carries out wavelet transformation, wavelet transformation is applied among partition of the scale；Wavelet structure function first,
Formula is as follows:
In formula, Ψ (t) is morther wavelet；K is the translational movement of Ψ (t) ordinate direction, and j indicates the number of plies of signal, and j, k ∈ Z, Z are
Set of integers；Scale parameter is 2j, translation parameters 2jk；T is time index；
2. then carrying out 7 layers of spectral decomposition to EEG signals x (t), wavelet conversion coefficient is obtained
Wavelet coefficient Cj,kAccording to the sequence arrangement of frequency range from high to low, extract the 3rd, 4,5,6,7 layer coefficients reconstruct
The letter of gamma:32~64Hz, beta:16~32Hz, alpha:8~16Hz, theta:4~8Hz and delta:1~4Hz frequency range
Number:
In formula,For the quality factor of filter；
3. reconstruction signal x1(t)、x2(t)、x3(t)、x4(t) and x5(t) respectively correspond brain Electricity Functional frequency band delta, theta,
The signal of alpha, beta and gamma；Above-mentioned identical wavelet transform procedure is carried out for electromyography signal y (t), obtains ym(t),
M=1,2,3,4,5, respectively correspond the signal of myoelectricity delta, theta, alpha, beta and gamma frequency range；
4. being based on transfer entropy calculation method, construction x (t) arrives small echo-transfer entropy WTE of y (t)x→y, formula is as follows:
In formula, u is predicted time；Joint probability of the p () between variable；Respectively indicate brain electricity and myoelectricity delta,
The delay vector of theta, alpha, beta, gamma component；ForForecasting sequence；
WTEx→yThen indicate the x of EEG signals EEGi(t) y of the component to electromyography signal EMGm(t) the transmitting entropy between component；Similarly
Signal y (t) arrives small echo-transfer entropy WTE of x (t)y→xExpression formula are as follows:
In formula,ForForecasting sequence；WTEy→xIndicate the y of EMGm(t) to component to the x of EEGi(t) transfer entropy between component
Value；It is bigger to transmit entropy, illustrates that the coupling of cortex muscle is stronger between this frequency range；It is smaller to transmit entropy, illustrates in this frequency range mesothelium
Layer muscle coupling is weaker；
Step 4, Non-linear coupling the brain flesh under motion state and information transfer characteristic are analyzed.
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