CN110292376A - Remove method, apparatus, equipment and the storage medium of eye electricity artefact in EEG signals - Google Patents
Remove method, apparatus, equipment and the storage medium of eye electricity artefact in EEG signals Download PDFInfo
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Abstract
The invention discloses method, apparatus, equipment and the storage mediums of eye electricity artefact in a kind of removal EEG signals.This method comprises: the multichannel brain electric signal to acquisition carries out isolated component decomposition, multiple isolated components are obtained；Denoising is carried out to the multiple isolated component；The Sample Entropy of multiple isolated components after calculating denoising；According to the Sample Entropy of each isolated component, the electrically independent component of brain and the isolated component containing eye electricity artefact are determined；To the isolated component containing eye electricity artefact, eye electricity artefact is removed；The isolated component for removing eye electricity artefact and the electrically independent component of brain are reconstructed, the EEG signals of removal eye electricity artefact are obtained.The present invention eliminates the reliance on artificial by virtue of experience removal eye electricity artefact, with automatic identification eye electricity artefact and can remove, and denoising is first carried out after obtaining isolated component, and the EEG signals obtained from are purer, more accurate.
Description
Technical field
The present embodiments relate to eye electricity artefacts in EEG Processing technology more particularly to a kind of removal EEG signals
Method, apparatus, equipment and storage medium.
Background technique
EEG signals (Electroencephalogram, EEG) be it is a kind of using electrophysiological index record brain activity obtain
Method, in activity, the synchronous postsynaptic potential occurred of a large amount of neurons is formed brain after summation.It records brain activity
When electric wave variation, be overall reflection of the bioelectrical activity in cerebral cortex or scalp surface of cranial nerve cell.EEG signals
It is the autonomous potential activity for being generated by brain neurological motion and being present in always central nervous system, brain rich in is living
Dynamic information is the important means of brain research, physiological Study, clinical cerebral disease diagnosis.However, EEG signals have height non-flat
Stability, randomness and nonlinear feature, and weak output signal are using electro physiology system (port number is more) or portable brain electric
When acquisition equipment (few channel even single channel) is acquired EEG signals, be highly prone to eye electricity (Electrooculogram,
EOG), the interference of myoelectricity (Electromyography, EMG), electrocardio (Electrocardiography, EKG), these interference
Amplitude it is larger, have a great impact to the extractions of EEG signals, analysis and application.
In eye electricity, myoelectricity and cardiac electrical interference, eye electricity artefact is the largest interference component, and the influence to EEG signals is most
Greatly.The amplitude of the electric artefact of eye is very big, and maximum can reach 100mv, and EEG signals amplitude is very faint, general scalp EEG signals
Only 50 μ v or so, therefore will lead to collected EEG signals and generate obvious distortion, form eye electricity artefact.Also, eye electricity is pseudo-
The frequency band of mark also covers the frequency band of EEG signals, is difficult to remove it by the method for filtering.In an experiment subject blink,
Rotation of eyeball is all inevitable, therefore removal eye electricity artefact is just particularly important the interference of EEG signals.Existing skill
It needs manually by virtue of experience to identify eye electricity artefact in art, there is a problem that accuracy is low.In the process of acquisition EEG signals
In, EEG signals are also easy to the interference by other noises, this also brings centainly the eye electricity artefact in removal EEG signals
Difficulty.
Summary of the invention
In view of this, the embodiment of the present invention provide the method, apparatus of eye electricity artefact in a kind of removal EEG signals, equipment and
Storage medium, to obtain purer, accurate EEG signals.
In a first aspect, the embodiment of the invention provides a kind of method of eye electricity artefact in removal EEG signals, the method
Include:
Isolated component decomposition is carried out to the multichannel brain electric signal of acquisition, obtains multiple isolated components；
Denoising is carried out to the multiple isolated component；
The Sample Entropy of multiple isolated components after calculating denoising；
According to the Sample Entropy of each isolated component, the electrically independent component of brain and the isolated component containing eye electricity artefact are determined；
To the isolated component containing eye electricity artefact, eye electricity artefact is removed；
The isolated component for removing eye electricity artefact and the electrically independent component of brain are reconstructed, the brain electricity of removal eye electricity artefact is obtained
Signal.
Second aspect, the embodiment of the invention also provides a kind of device of eye electricity artefact in removal EEG signals, the dresses
It sets and includes:
Isolated component decomposing module obtains multiple for carrying out isolated component decomposition to the multichannel brain electric signal of acquisition
Isolated component；
Module is denoised, for carrying out denoising to the multiple isolated component；
Sample Entropy computing module, for calculating the Sample Entropy of multiple isolated components after denoising；
The electric artefact determining module of eye determines the electrically independent component of brain and contains for the Sample Entropy according to each isolated component
The isolated component of the electric artefact of eye；
The electric artefact of eye removes module, for removing eye electricity artefact to the isolated component containing eye electricity artefact；
Reconstructed module is removed for the isolated component and the electrically independent component of brain that remove eye electricity artefact to be reconstructed
The EEG signals of the electric artefact of eye.
The third aspect, the embodiment of the invention also provides a kind of equipment, the equipment includes:
One or more processors；
Storage device, for storing one or more programs,
When one or more of programs are executed by one or more of processors, so that one or more of processing
The method that device realizes eye electricity artefact in removal EEG signals described in any embodiment of the present invention.
Fourth aspect, the embodiment of the invention also provides a kind of computer readable storage mediums, are stored thereon with computer
Program realizes the side of eye electricity artefact in removal EEG signals described in any embodiment of the present invention when the program is executed by processor
Method.
The technical solution of the embodiment of the present invention is obtained by carrying out isolated component decomposition to the multichannel brain electric signal of acquisition
The Sample Entropy of multiple isolated components is calculated, according to every after carrying out denoising to multiple isolated component to multiple isolated components
The Sample Entropy of a isolated component determines the electrically independent component of brain and the isolated component containing eye electricity artefact, to containing eye electricity artefact
Isolated component removes eye electricity artefact, and the isolated component for removing eye electricity artefact and the electrically independent component of brain are reconstructed, removed
The EEG signals of the electric artefact of eye eliminate the reliance on artificial by virtue of experience removal eye electricity artefact, with automatic identification eye electricity artefact and can go
It removes, and first carries out denoising after obtaining isolated component, the EEG signals obtained from are purer, more accurate.
Detailed description of the invention
Fig. 1 is the flow chart of the method for eye electricity artefact in a kind of removal EEG signals of the offer of the embodiment of the present invention one；
Fig. 2 is the frequency response chart of the digital trap in the embodiment of the present invention；
Fig. 3 is to calculate multiple isolated components in the method for eye electricity artefact in removal EEG signals provided in an embodiment of the present invention
Sample Entropy flow chart；
Fig. 4 is the numerical value figure of the Sample Entropy of each isolated component in the embodiment of the present invention；
Fig. 5 is the waveform diagram of the isolated component containing eye electricity artefact in the embodiment of the present invention；
Fig. 6 is the electroencephalogram after the removal eye electricity artefact in the embodiment of the present invention；
Fig. 7 is the waveform diagram of the four-way EEG signals of the acquired original in the embodiment of the present invention；
Fig. 8 is the waveform diagram of the EEG signals reconstructed after the removal eye electricity artefact in the embodiment of the present invention；
Fig. 9 be the original EEG signals in the embodiment of the present invention and reconstruct EEG signals respectively with hanging down in eye electricity artefact
The result figure of the related coefficient of straight component；
Figure 10 is to carry out isolated component point in the method for eye electricity artefact in removal EEG signals provided in an embodiment of the present invention
The flow chart of solution；
Figure 11 is the flow chart of the method for eye electricity artefact in a kind of removal EEG signals provided by Embodiment 2 of the present invention；
Figure 12 is the frequency domain characteristic figure of the original EEG signals in the embodiment of the present invention；
Figure 13 is the frequency domain characteristic figure of the removal Hz noise in the embodiment of the present invention and its EEG signals after harmonic wave；
Figure 14 is that the embodiment of the present invention three provides a kind of structural schematic diagram for removing the device of eye electricity artefact in EEG signals；
Figure 15 is a kind of structural schematic diagram for equipment that the embodiment of the present invention four provides.
Specific embodiment
The present invention is described in further detail with reference to the accompanying drawings and examples.It is understood that this place is retouched
The specific embodiment stated is used only for explaining the present invention rather than limiting the invention.It also should be noted that in order to just
In description, only some but not all contents related to the present invention are shown in the drawings.
Embodiment one
Fig. 1 is the flow chart of the method for eye electricity artefact in a kind of removal EEG signals of the offer of the embodiment of the present invention one, this
Embodiment is applicable to carry out pretreated situation to EEG signals, and this method can be by eye electricity artefact in removal EEG signals
Device executes, which can be realized by software and/or hardware, can generally be integrated in the equipment such as computer or Medical Devices
In, this method specifically comprises the following steps:
Step 110, isolated component decomposition is carried out to the multichannel brain electric signal of acquisition, obtains multiple isolated components.
Wherein, multichannel brain electric signal can use prefrontal lobe multichannel collecting system acquisition and obtain, multichannel brain telecommunications
It number is at least two channel EEG signals, when multichannel brain electric signal is four-way EEG signals, prefrontal lobe multichannel collecting system
System is prefrontal lobe four-way acquisition system.When acquiring four-way EEG signals, four positions near prefrontal lobe can be chosen
Electrode is placed, the acquisition of EEG signals is carried out.The position for placing electrode is not limited solely to prefrontal lobe, as long as before being located proximate to
Frontal lobe is functionally ok with the related position of prefrontal lobe.
Using independent component analysis (Independent Component Analysis, ICA) to multichannel brain electric signal
Carry out isolated component decomposition.ICA refers to the technology that source signal is isolated from the linear hybrid signal of multiple source signals.It can make
Signal is decomposed with fastICA (Fast Independent Component Analysis) algorithm, its expression formula are as follows:
X (t)=As (t)
Wherein, x (t)=[x1(t),x2(t),…,xn(t)], x (t) ∈ Rn×N, x (t) is the observation vector of n-channel, 1≤t
≤ N, N indicate total number of sample points, s (t) ∈ Rm×NFor source signal, hybrid matrix A ∈ Rn×m, the target of independent component analysis is to ask
Separation matrix W is solved, recovers unknown source signal s (t) from observation vector x (t) by W, source signal s (t) is estimated using following formula
Meter obtains:
Y (t)=Wx (t)
Wherein, y (t) is the estimation of source signal s (t), y (t)=[y1(t),y2(t),…,ym(t)], y (t) ∈ Rm×N, y
It (t) is the m isolated component acquired, wherein yiIt (t) is i-th of isolated component, 1≤i≤m, i are integer, and t indicates to adopt for t-th
Sampling point, separation matrix W ∈ Rm×N。
Step 120, denoising is carried out to the multiple isolated component.
The mainly interference of Hz noise and its harmonic wave, mainly goes when carrying out denoising in the noise of EEG signals
Except the Hz noise and its harmonic wave in EEG signals.
Wherein, denoising is carried out to the multiple isolated component, it is optional to include:
Digital trap is designed by transform according to the property of Hz noise and each harmonic；
The multiple isolated component is filtered respectively using the digital trap, to remove the work in isolated component
Frequency interference and its harmonic wave.
When using digital trap removal Hz noise and its harmonic wave, it is desirable that do not change other frequency contents substantially, because
This, according to the frequency properties of Hz noise and its harmonic wave, the frequency response of digital trap and transmission function need satisfaction following
Condition:
Wherein, w0It is the frequency of each harmonic of Hz noise and Hz noise, m indicates that the harmonic wave of Hz noise is always secondary
Number.Gained is zero point,Gained is pole, due to p less than 1 but very close to 1, so zero
Point is very close with pole, when removing Hz noise and its harmonic wave, it is contemplated that actual conditions accomplish that triple-frequency harmonics can.
Due to p less than 1 but very close to 1, p=0.998 can be taken.
Frequency response chart can substantially be drawn by the zero point and pole of the transmission function of digital trap, as shown in Fig. 2,
There is minimum in zero point, frequency response, and in pole, maximum occurs in frequency response.Then it can be reversed design digital trap,
Z inverse transformation is carried out to transmission function, obtains digital trap h (t).
To obtain the isolated component after removal Hz noise and its harmonic wave according to the following formula:
W (t)=y (t) * h (t)
Wherein, w (t) is the isolated component removed after Hz noise and its harmonic wave.Remove Hz noise and its harmonic wave it
Afterwards, the isolated component obtained is just purer, so that subsequent obtained Sample Entropy is more accurate.
Step 130, the Sample Entropy of multiple isolated components after denoising is calculated.
For each isolated component after denoising, corresponding Sample Entropy is calculated separately.
Fig. 3 is to calculate multiple isolated components in the method for eye electricity artefact in removal EEG signals provided in an embodiment of the present invention
Sample Entropy flow chart, as shown in figure 3, calculate denoising after multiple isolated components Sample Entropy, it is optional to include:
Step 1301, the isolated component in multiple isolated components after choosing denoising, as current isolated component；
After selection obtains current isolated component, the number of the sampled point of current isolated component is determined, and be denoted as N, it is current only
Vertical component is denoted as wi(t), wherein t indicates that t-th of sampled point, 1≤t≤N, t are integer, and i indicates that current isolated component is i-th
A isolated component, 1≤i≤m, i are integer.
Step 1302, the first two-dimensional vector and the second two-dimensional vector are extracted from current isolated component；
According to the following formula, from current isolated component wi(t) b n dimensional vector n W is extracted inb(t) and Wb(t1), wherein b=2:
Wb(t)=[wi(t),wi(t+1)]
Wb(t1)=[wi(t1),wi(t1+1)]
Wherein, wi(t) and wiIt (t+1) is sampled value of the current isolated component in sampled point t and t+1 respectively, 1≤t≤N-1,
wi(t1) and wi(t1It+1) is current isolated component respectively in sampled point t1And t1+ 1 sampled value, 1≤t1≤ N-1, t ≠ t1, Wb
It (t) is the first two-dimensional vector, Wb(t1) it is the second two-dimensional vector.
Step 1303, the distance between first two-dimensional vector and second two-dimensional vector are calculated；
The first two-dimensional vector W is calculated according to the following formulab(t) and the second two-dimensional vector Wb(t1) the distance between:
Wherein, d [Wb(t),Wb(t1)] it is the first two-dimensional vector Wb(t) and the second two-dimensional vector Wb(t1The distance between),
wiIt (t+c) is sampled value of the current isolated component in sampled point t+c, wi(t1+ c) it is current isolated component in sampled point t1+ c's
Sampled value, c ∈ { 0,1 }.
Step 1304, for each of current isolated component sampled point, the first two-dimensional vector and the second two dimension are counted
The distance between vector is less than the number of statistical threshold, calculates the total of the number and the first two-dimensional vector and the second two-dimensional vector
Ratio apart from number, as the first ratio；
The standard deviation for calculating current isolated component, the value that standard deviation is obtained multiplied by preset threshold is as statistical threshold, i.e.,
Statistical threshold r=(0.1~0.25) std (wi(t)), std (wiIt (t)) is current isolated component wi(t) standard deviation presets threshold
Value can take a value in (0.1~0.25).For each of current isolated component sampled point, i.e., for each t value,
Count the distance between the first two-dimensional vector and the second two-dimensional vector d [Wb(t),Wb(t1)] it is less than the number of statistical threshold, it is denoted as
T, 1≤t≤N-1,1≤t1≤ N-1, t ≠ t1, t and t1It is integer, t1N-2 value can be taken, therefore, the first two-dimensional vector and
Total distance number between second two-dimensional vector is N-2, calculate according to the following formula T with it is total at a distance from number ratio, as the
One ratio:
Wherein, r indicates statistical threshold,Indicate T and it is total at a distance from number ratio, i.e. the first ratio, N-2 indicate always
Distance number.
Step 1305, the mean value for calculating first ratio, as the first mean value；
The first ratio is calculated according to the following formulaMean value, as the first mean value:
Wherein, Bb(r) it indicatesMean value, i.e. the first mean value.
Step 1306, the first trivector and the second trivector are extracted from current isolated component；
According to the following formula, from current isolated component wi(t) b is extracted in1N dimensional vector nWithWherein, b1=
3:
Wherein, wi(t)、wi(t+1)、wiIt (t+2) is sampled value of the current isolated component in sampled point t, t+1, t+2 respectively,
1≤t≤N-2, wi(t1)、wi(t1+1)、wi(t1It+2) is current isolated component respectively in sampled point t1、t1+1、t1+ 2 sampling
Value, 1≤t1≤ N-2, t ≠ t1,For the first trivector,For the second trivector.
Step 1307, the distance between first trivector and second trivector are calculated；
The first trivector is calculated according to the following formulaWith the second trivectorThe distance between:
Wherein, wiIt (t+e) is sampled value of the current isolated component in sampled point t+e, wi(t1+ e) it is that current isolated component exists
Sampled point t1The sampled value of+e, e ∈ { 0,1,2 }.
Step 1308, for each of current isolated component sampled point, the first trivector and the second three-dimensional are counted
The distance between vector is less than the number of the statistical threshold, calculates the number and the first trivector and the second trivector
The ratio of total distance number, as the second ratio；
For each of current isolated component sampled point, i.e., for each t value, count the first trivector and the
The distance between two trivectorsLess than the number of the statistical threshold, it is denoted as T1, 1≤t≤N-2,1
≤t1≤ N-2, t ≠ t1, t and t1It is integer, then t1N-3 value, therefore the first trivector and the second trivector can be taken
Between total distance number be N-3, calculate T according to the following formula1With it is total at a distance from number ratio, as the second ratio:
Wherein, r indicates statistical threshold,It is T1With it is total at a distance from number ratio, i.e. the second ratio, N-3 be it is total away from
From number.
Step 1309, the mean value for calculating second ratio, as the second mean value；
The second ratio is calculated according to the following formulaMean value, as the second mean value:
Wherein,Indicate the second ratioMean value, i.e. the second mean value.
Step 1310, according to first mean value and second mean value, the Sample Entropy of current isolated component is calculated；
First mean value and second mean value, the Sample Entropy of current isolated component is calculated according to following formula:
Wherein, sampEn is the Sample Entropy of current isolated component.
Step 1311, judge whether to obtain the Sample Entropy of all isolated components, if it is not, then repeating above-mentioned steps
1301- step 1310, if it is, terminating.
1301- step 1311 through the above steps has obtained the Sample Entropy of all isolated components, the sample being calculated in this way
This entropy is more accurate.
Step 140, according to the Sample Entropy of each isolated component, the electrically independent component of brain and the independence containing eye electricity artefact are determined
Component.
In the isolated component of removal noise, the numerical value of eye electricity artefact Sample Entropy is lower than the Sample Entropy of EEG signals, and
Since eye electricity artefact is generally present in two isolated components, i.e., eye electricity artefact has vertical component and horizontal component, therefore,
In the Sample Entropy of all isolated components, determine that the smallest two isolated components of Sample Entropy are the isolated component containing eye electricity artefact,
Other isolated components are the electrically independent component of brain.Fig. 4 is the numerical value figure of the Sample Entropy of each isolated component in the embodiment of the present invention,
In Fig. 4 by taking four-way EEG signals and 4 isolated components as an example, abscissa represents four isolated components in Fig. 4, such as Fig. 4 institute
Show, isolated component 1 and isolated component 2 are the smallest two isolated components of Sample Entropy, determine that the two isolated components are containing eye
The isolated component of electric artefact, and determine isolated component 3 and isolated component 4 for the electrically independent component of brain.It is recognized according to this method
The waveform diagram of isolated component containing eye electricity artefact as shown in figure 5, Fig. 5 by taking sampled point is 2000 points as an example, wherein 2 channels eye
Electric signal indicates the horizontal component of eye electricity artefact, and 6 channel electro-ocular signals indicate the vertical component of eye electricity artefact.
Step 150, to the isolated component containing eye electricity artefact, eye electricity artefact is removed.
Isolated component containing eye electricity artefact by filtering or is set to 0, so that eye electricity artefact is removed, electrically independent point of brain
It measures constant.Fig. 6 is the electroencephalogram removed after eye electricity artefact, and sampled point is 2000 points in Fig. 6.
Step 160, the isolated component for removing eye electricity artefact and the electrically independent component of brain are reconstructed, it is pseudo- obtains removal eye electricity
The EEG signals of mark.
The sequence of multiple isolated components is obtained according to when carrying out isolated component decomposition, by the only of corresponding removal eye electricity artefact
Multiple isolated components after vertical component and the electrically independent component composition removal eye electricity artefact of brain, to obtained multiple isolated component into
The reconstruct that row isolated component decomposes obtains the EEG signals of removal eye electricity artefact, i.e., pure EEG signals.To removal eye electricity
When multiple isolated components after artefact are reconstructed, it can be reconstructed according to the following formula:
Wherein,It is the multiple isolated components removed after eye electricity artefact,It is the brain of the removal eye electricity artefact obtained after reconstructing
Electric signal, W-1It is the pseudo inverse matrix of separation matrix W.
Fig. 7 is the waveform diagram of the four-way EEG signals of the acquired original in the embodiment of the present invention, and Fig. 8 is implementation of the present invention
The waveform diagram of the EEG signals reconstructed after removal eye electricity artefact in example.As shown in Figure 7 and Figure 8, the brain after eye electricity artefact is removed
Electric signal is purer.
EEG signals after can seeking original multichannel brain electric signal and reconstruct respectively with the eye electricity artefact that identifies
An isolated component related coefficient, by comparing both with the related coefficient of eye electricity artefact, evaluation removal eye electricity artefact
As a result, if the related coefficient of EEG signals and eye electricity artefact after reconstruct is less than the phase of original EEG signals and eye electricity artefact
Relationship number, it is determined that successfully remove eye electricity artefact.Fig. 9 is the brain telecommunications of the original EEG signals in the embodiment of the present invention and reconstruct
Number the result figure with the related coefficient of the vertical component in eye electricity artefact, Fig. 9 are using original EEG signals as four-way brain respectively
For electric signal, abscissa represents 4 channels, as shown in figure 9, dotted line indicates vertical with eye electricity artefact point of original EEG signals
The related coefficient of amount, solid line indicate the related coefficient of the EEG signals of reconstruct and the vertical component of eye electricity artefact, it can be seen that weight
The EEG signals of structure and the related coefficient of eye electricity artefact are less than the related coefficient of original EEG signals and eye electricity artefact, determine the use of
The method removal eye electricity artefact of eye electricity artefact is successful in removal EEG signals described in the embodiment of the present invention, and reconstruct
The related coefficient of EEG signals and eye electricity artefact illustrates that the EEG signals of reconstruct are uncorrelated to eye electricity artefact less than 0.5, reconstruct
No longer contain eye electricity artefact in EEG signals, it is purer.
The technical solution of the present embodiment is obtained more by carrying out isolated component decomposition to the multichannel brain electric signal of acquisition
A isolated component calculates the Sample Entropy of multiple isolated components after carrying out denoising to multiple isolated component, according to each only
The Sample Entropy of vertical component, determines the electrically independent component of brain and the isolated component containing eye electricity artefact, to the independence containing eye electricity artefact
Component removes eye electricity artefact, and the isolated component for removing eye electricity artefact and the electrically independent component of brain are reconstructed, and obtains removal eye electricity
The EEG signals of artefact eliminate the reliance on artificial by virtue of experience removal eye electricity artefact, with automatic identification eye electricity artefact and can remove, and
And denoising is first carried out after obtaining isolated component, the EEG signals obtained from are purer, more accurate, and save
Manpower and time, improve efficiency.
Based on the above technical solution, Figure 10 is eye electricity artefact in removal EEG signals provided in an embodiment of the present invention
Method in carry out the flow chart of isolated component decomposition independent point carried out to the multichannel brain electric signal of acquisition as shown in Figure 10
Amount is decomposed, and multiple isolated components are obtained, optional to include:
Step 111, average value processing and whitening processing are carried out to the multichannel brain electric signal of acquisition, after obtaining multiple processing
Observation vector；
The multichannel brain electric signal of acquisition can be expressed as observation vector x (t)=[x of multichannel1(t),x2(t),…,xn
(t)], x (t) ∈ Rn×N, 1≤t≤N, N indicate that total number of sample points carries out each of these observation vector x respectively first
Average value processing is removed, i.e. the mean value m=E { x } of observation vector x is individually subtracted in each element in observation vector x, makes it have zero
Value, the observation vector s after obtaining average value processing.Average value processing is gone to can simplify ICA algorithm, it is mixed after estimating away mean value
After closing matrix A, calculated separation signal is added to the mean value A of s-1m。
After carrying out average value processing to each observation vector, then whitening processing is carried out, i.e. linear transformation observation vector s
Keep its each ingredient uncorrelated and have unit variance, is i.e. albefaction is new vector s', and the covariance matrix of the new vector is equal to unit square
Battle array: E { s ' s 'T}=I.
When carrying out whitening processing, Eigenvalues Decomposition, i.e. E { ss are carried out to the data covariance of observation vector firstT}=
EDET, wherein E is E { ssTFeature vector orthogonal matrix, D is the diagonal matrix of characteristic value, D=diag (d1,…,dn), then
Vector after albefaction are as follows:
S '=ED-1/2ETs
Wherein, D-1/2=diag (d1 -1/2,…,dn -1/2).Similarly, albefaction hybrid matrix A obtains A ', then A ' is orthogonal.
Whitening operation can be reduced parameter to be estimated, and estimated mixing matrix A will estimate n2A parameter, and estimate orthogonal matrix
A ' only need to estimate n (n-1)/2 parameter, and this greatly reduces the computation complexities of ICA algorithm.
Step 112, according to each treated observation vector, corresponding separating vector is solved, and arrives at least two solving
When a separating vector, decorrelative transformation is carried out to the separating vector solved；
Separating vector is solved using FastICA algorithm.Observation vector s ' and orthogonal matrix A ' after albefaction obtained above
It is indicated respectively with z and B.
The single treatment process of algorithm is discussed first.Algorithm of the FastICA algorithm based on fixed point iteration structure, it is therefore an objective to make
wTZ has maximum non-Gaussian system, wherein w is a line of separation matrix W, using formula J (y) ∝ [E { G (y) }-E { G (v) }]2For mesh
Scalar functions define the derivative that g is non-quadratic function G, then function G1(u)=1/a1logcosh(a1U), G2(u)=- exp (- u2/
2) derivative are as follows:
g1(u)=tanh (a1U), g2(u)=uexp (- u2/2)
Wherein, 1≤a1≤ 2 be a suitable constant, usually selects a1=1.
E { (w can be passed throughTZ) optimum condition } obtains wTThe negentropy approximation of z.According to Kuhn-Tucker condition, in E
{(wTz)2}=| | w | |2Under=1 constraint condition, E { (wTZ) optimum condition } can be obtained by following formula:
E{zg(wTZ) }-β w=0
With the above-mentioned equation of Newton method solution, the definite equation left side is F, obtains its Jacobian matrix JF (w) are as follows:
JF (w)=E { zzTg′(wTz)}-βI
First item can be reduced to E { zz on the right of above formulaTg′(wTz)}≈E{zzT}E{g′(wTZ) }=E { g ' (wTZ) } I, Ya Ke
Become diagonal, reversible than matrix, therefore obtain inexact Newton iteration:
w+=w- [E { zg (wTz)}-βw]/[E{g′(wTz)}-β]
Above formula both sides are multiplied into together β-E { g ' (wTZ) }, it is further simplified as w+=E { zg (wTz)}-E{g′(wTz)}w。
To sum up, the citation form of a FastICA algorithm are as follows:
1) initialization vector w is (as generated random vector as vector w)
2) w is enabled+=E { zg (wTz)}-E{g′(wTz)}w；
3) w=w+/||w+||
4) judge whether w restrains, if do not restrained, return to 2).
Wherein, convergence refers to that in the same direction, i.e., their dot product is 1 to vector w twice for front and back.
FastICA algorithm can estimate a separating vector in separation matrix, in order to estimate in separation matrix
All separating vectors, need to carry out multiple FastICA algorithm and obtain separating vector w1,…,wn.These vectors are received in order to prevent
Hold back the output w needed in the same maximum value to after each iteration1 Tz,…,wn TZ decorrelation.Decorrelation includes Gram-
Schmidt-like decorrelation and matrix square root decorrelation.
Gram-Schmidt-like decorrelation is to estimate separating vector one by one.Estimating p separating vector
w1,…,wpLater, as estimation wp+1When first subtract previous prediction p vector projection wp+1wjwj, then j=1 ..., p are marked
Standardization wp+1, i.e.,
1) it enables
2) it enables
Matrix square root decorrelation can make all separating vectors all be equality, i.e.,
W=(WWT)-1/2W
Wherein, W=[w1,…,wn]T, inverse square root (WWT)-1/2It is obtained by Eigenvalues Decomposition, (WWT)-1/2=(FDFT
)-1/2=FD-1/2FT, it can be obtained by following iterative algorithm:
1) it enables
2) W=3/2W-1/2WW is enabledTW；
3) it repeats 2), until W restrains.
Step 113, the separating vector after decorrelative transformation is formed into separation matrix；
Since separating vector is the row vector in separation matrix, separating vector is formed into separation matrix in sequence.
Step 114, it according to the separation matrix, the observation vector and the mean value gone in average value processing, obtains more
A isolated component.
Y (t)=Wx (t)
According to the matrix M of separation matrix W, multiple treated observation vector z and corresponding mean value m composition, according to as follows
Formula obtains multiple isolated components:
Y=Wz+M
Wherein, multiple isolated components that y is, y ∈ Rm×N, the row vector of M is the corresponding mean value m arrangement of observation vector z
Made of, thus M ∈ Rm×N。
It is more accurate by isolated component obtained by the above method, and calculation amount is small.
Embodiment two
Figure 11 is the flow chart of the method for eye electricity artefact in a kind of removal EEG signals provided by Embodiment 2 of the present invention, this
Embodiment is optimized on the basis of the above embodiments, further comprises: Fourier transformation is carried out to multiple isolated components,
Obtain the amplitude spectrum and phase spectrum of the multiple isolated component.This method specifically comprises the following steps:
Step 210, isolated component decomposition is carried out to the multichannel brain electric signal of acquisition, obtains multiple isolated components.
Step 220, Fourier transformation is carried out to the multiple isolated component respectively, obtains the width of the multiple isolated component
Degree spectrum and phase spectrum.
Fast Fourier Transform (FFT) is carried out to multiple isolated components respectively, obtains the amplitude spectrum and phase of multiple isolated components
Spectrum, to carry out frequency-domain analysis.By frequency-domain analysis, the available frequency domain character to signal gets the frequency of signal concentration
Rate section is conducive to the feature that EEG signals are extracted in analysis；But also the available Hz noise into EEG signals and its each time
The case where harmonic wave, is convenient for subsequent removal Hz noise and its harmonic wave when there are Hz noise and its harmonic wave.
Step 230, denoising is carried out to the multiple isolated component.
Figure 12 is the frequency domain characteristic figure of the original EEG signals in the embodiment of the present invention, and abscissa represents frequency, ordinate
Amplitude is represented, as shown in figure 12, it can be seen that there are the Hz noises that frequency is 60Hz, can be by using with shown in Fig. 2
Frequency response chart digital trap removal Hz noise and its harmonic wave.Figure 13 is that the removal power frequency in the embodiment of the present invention is dry
Disturb and its harmonic wave after EEG signals frequency domain characteristic figure, abscissa represents frequency, and ordinate represents amplitude, as shown in figure 13,
The Hz noise of 60Hz has removed.
Step 240, the Sample Entropy of multiple isolated components after denoising is calculated.
Step 250, according to the Sample Entropy of each isolated component, the electrically independent component of brain and the independence containing eye electricity artefact are determined
Component.
Step 260, to the isolated component containing eye electricity artefact, eye electricity artefact is removed.
Step 270, the isolated component for removing eye electricity artefact and the electrically independent component of brain are reconstructed, it is pseudo- obtains removal eye electricity
The EEG signals of mark.
The technical solution of the present embodiment, on the basis of the above embodiments, by carrying out Fu respectively to multiple isolated components
In leaf transformation, obtain the amplitude spectrum and phase spectrum of multiple isolated components, frequency-domain analysis can be carried out to multiple isolated components, can be with
The frequency band for getting signal concentration is conducive to the feature that EEG signals are extracted in analysis, but also available to EEG signals
The case where middle Hz noise and its each harmonic.
Embodiment three
Figure 14 is that the embodiment of the present invention three provides a kind of structural schematic diagram for removing the device of eye electricity artefact in EEG signals,
The method that the device can execute eye electricity artefact in the removal EEG signals that any embodiment of the present invention provides, the device can be by
Software and/or hardware is realized, generally can be integrated in the equipment such as computer or Medical Devices.As shown in figure 14, the present embodiment
The device of eye electricity artefact includes: isolated component decomposing module 310, denoising module 320, Sample Entropy in the removal EEG signals
Computing module 330, eye electricity artefact determining module 340, eye electricity artefact removal module 350 and reconstructed module 360.
Wherein, isolated component decomposing module 310, for carrying out isolated component decomposition to the multichannel brain electric signal of acquisition,
Obtain multiple isolated components；
Module 320 is denoised, for carrying out denoising to the multiple isolated component；
Sample Entropy computing module 330, for calculating the Sample Entropy of multiple isolated components after denoising；
The electric artefact determining module 340 of eye, for the Sample Entropy according to each isolated component, determines the electrically independent component of brain and contains
There is the isolated component of eye electricity artefact；
The electric artefact of eye removes module 350, for removing eye electricity artefact to the isolated component containing eye electricity artefact；
Reconstructed module 360 is gone for the isolated component and the electrically independent component of brain that remove eye electricity artefact to be reconstructed
Except the EEG signals of eye electricity artefact.
Optionally, further includes:
Fourier transformation module obtains the multiple for carrying out Fourier transformation respectively to the multiple isolated component
The amplitude spectrum and phase spectrum of isolated component.
Optionally, the isolated component decomposing module includes:
Pretreatment unit obtains more for carrying out average value processing and whitening processing to the multichannel brain electric signal of acquisition
A treated observation vector；
Separating vector solves unit, for according to each treated observation vector, solving corresponding separating vector, and
When solving at least two separating vectors, decorrelative transformation is carried out to the separating vector solved；
Separation matrix determination unit, for the separating vector after decorrelative transformation to be formed separation matrix；
Isolated component determination unit, for according to the separation matrix, the observation vector and described going in average value processing
Mean value, obtain multiple isolated components.
Optionally, the denoising module includes:
The design of notch unit is designed number and is fallen into for the property according to Hz noise and each harmonic by transform
Wave device；
Hz noise removal unit, for being filtered respectively to the multiple isolated component using the digital trap
Wave, to remove the Hz noise and its harmonic wave in isolated component.
Optionally, the Sample Entropy computing module is specifically used for:
An isolated component in multiple isolated components after choosing denoising, as current isolated component；
The first two-dimensional vector and the second two-dimensional vector are extracted from current isolated component；
Calculate the distance between first two-dimensional vector and second two-dimensional vector；
For each of current isolated component sampled point, count between the first two-dimensional vector and the second two-dimensional vector
Distance is less than the number of statistical threshold, calculate the number and the first two-dimensional vector and the second two-dimensional vector it is total at a distance from number ratio
Value, as the first ratio；
The mean value for calculating first ratio, as the first mean value；
The first trivector and the second trivector are extracted from current isolated component；
Calculate the distance between first trivector and second trivector；
For each of current isolated component sampled point, count between the first trivector and the second trivector
Distance is less than the number of the statistical threshold, calculate the number and the first trivector and the second trivector it is total at a distance from number
Ratio, as the second ratio；
The mean value for calculating second ratio, as the second mean value；
According to first mean value and second mean value, the Sample Entropy of current isolated component is calculated；
Above-mentioned steps are repeated, until obtaining the Sample Entropy of all isolated components.
Removal brain provided by any embodiment of the invention can be performed in the device of eye electricity artefact in above-mentioned removal EEG signals
The method of eye electricity artefact in electric signal, has the corresponding functional module of execution method and beneficial effect.Not in the present embodiment in detail
The technical detail described to the greatest extent, reference can be made to the method for removing eye electricity artefact in EEG signals that any embodiment of that present invention provides.
Example IV
Figure 15 is a kind of structural schematic diagram for equipment that the embodiment of the present invention four provides, and as shown in figure 15, which includes
Processor 410, memory 420, input unit 430 and output device 440；The quantity of processor 410 can be one in equipment
Or it is multiple, in Figure 15 by taking a processor 410 as an example；Processor 410, memory 420, input unit 430 in equipment and defeated
Device 440 can be connected by bus or other modes out, in Figure 15 for being connected by bus.
Memory 420 is used as a kind of computer readable storage medium, can be used for storing software program, journey can be performed in computer
Sequence and module, such as the corresponding program instruction/module of method of eye electricity artefact in the removal EEG signals in the embodiment of the present invention
(for example, isolated component decomposing module 310, denoising module 320, Sample Entropy in removal EEG signals in the device of eye electricity artefact
Computing module 330, eye electricity artefact determining module 340, eye electricity artefact removal module 350 and reconstructed module 360).Processor 410 is logical
Cross the operation software program, instruction and the module that are stored in memory 420, thereby executing equipment various function application and
The method of eye electricity artefact in above-mentioned removal EEG signals is realized in data processing.
Memory 420 can mainly include storing program area and storage data area, wherein storing program area can store operation system
Application program needed for system, at least one function；Storage data area, which can be stored, uses created data etc. according to equipment.This
Outside, memory 420 may include high-speed random access memory, can also include nonvolatile memory, for example, at least one
Disk memory, flush memory device or other non-volatile solid state memory parts.In some instances, memory 420 can be into one
Step includes the memory remotely located relative to processor 410, these remote memories can pass through network connection to equipment.On
The example for stating network includes but is not limited to internet, intranet, local area network, mobile radio communication and combinations thereof.
Input unit 430 can be used for receiving the number or character information of input, and generate with the user setting of equipment with
And the related key signals input of function control.Output device 440 may include that display screen etc. shows equipment.
Embodiment five
The embodiment of the present invention five also provides a kind of storage medium comprising computer executable instructions, and the computer can be held
Row instruction by computer processor when being executed for executing a kind of method for removing eye electricity artefact in EEG signals, this method packet
It includes:
Isolated component decomposition is carried out to the multichannel brain electric signal of acquisition, obtains multiple isolated components；
Denoising is carried out to the multiple isolated component；
The Sample Entropy of multiple isolated components after calculating denoising；
According to the Sample Entropy of each isolated component, the electrically independent component of brain and the isolated component containing eye electricity artefact are determined；
To the isolated component containing eye electricity artefact, eye electricity artefact is removed；
The isolated component for removing eye electricity artefact and the electrically independent component of brain are reconstructed, the brain electricity of removal eye electricity artefact is obtained
Signal.
Certainly, a kind of storage medium comprising computer executable instructions, computer provided by the embodiment of the present invention
Removal brain electricity provided by any embodiment of the invention can also be performed in the method operation that executable instruction is not limited to the described above
Relevant operation in signal in the method for eye electricity artefact.
By the description above with respect to embodiment, it is apparent to those skilled in the art that, the present invention
It can be realized by software and required common hardware, naturally it is also possible to which by hardware realization, but in many cases, the former is more
Good embodiment.Based on this understanding, technical solution of the present invention substantially in other words contributes to the prior art
Part can be embodied in the form of software products, which can store in computer readable storage medium
In, floppy disk, read-only memory (Read-Only Memory, ROM), random access memory (Random such as computer
Access Memory, RAM), flash memory (FLASH), hard disk or CD etc., including some instructions are with so that a computer is set
Standby (can be personal computer, server or the network equipment etc.) executes method described in each embodiment of the present invention.
It is worth noting that, included is each in above-mentioned removal EEG signals in the embodiment of the device of eye electricity artefact
Unit and module are only divided according to the functional logic, but are not limited to the above division, as long as can be realized corresponding
Function；In addition, the specific name of each functional unit is also only for convenience of distinguishing each other, it is not intended to restrict the invention
Protection scope.
Note that the above is only a better embodiment of the present invention and the applied technical principle.It will be appreciated by those skilled in the art that
The invention is not limited to the specific embodiments described herein, be able to carry out for a person skilled in the art it is various it is apparent variation,
It readjusts and substitutes without departing from protection scope of the present invention.Therefore, although being carried out by above embodiments to the present invention
It is described in further detail, but the present invention is not limited to the above embodiments only, without departing from the inventive concept, also
It may include more other equivalent embodiments, and the scope of the invention is determined by the scope of the appended claims.
Claims (10)
1. a kind of method of eye electricity artefact in removal EEG signals, which is characterized in that the described method includes:
Isolated component decomposition is carried out to the multichannel brain electric signal of acquisition, obtains multiple isolated components；
Denoising is carried out to the multiple isolated component；
The Sample Entropy of multiple isolated components after calculating denoising；
According to the Sample Entropy of each isolated component, the electrically independent component of brain and the isolated component containing eye electricity artefact are determined；
To the isolated component containing eye electricity artefact, eye electricity artefact is removed；
The isolated component for removing eye electricity artefact and the electrically independent component of brain are reconstructed, the brain telecommunications of removal eye electricity artefact is obtained
Number.
2. the method according to claim 1, wherein further include:
Fourier transformation is carried out to the multiple isolated component respectively, obtains the amplitude spectrum and phase of the multiple isolated component
Spectrum.
3. the method according to claim 1, wherein the multichannel brain electric signal to acquisition carries out isolated component point
Solution, obtains multiple isolated components, comprising:
Average value processing and whitening processing are carried out to the multichannel brain electric signal of acquisition, obtain multiple treated observation vectors；
According to each treated observation vector, corresponding separating vector is solved, and when solving at least two separating vectors,
Decorrelative transformation is carried out to the separating vector solved；
Separating vector after decorrelative transformation is formed into separation matrix；
According to the separation matrix, the observation vector and the mean value gone in average value processing, multiple isolated components are obtained.
4. the method according to claim 1, wherein carrying out denoising to the multiple isolated component, comprising:
Digital trap is designed by transform according to the property of Hz noise and each harmonic；
The multiple isolated component is filtered respectively using the digital trap, it is dry to remove the power frequency in isolated component
It disturbs and its harmonic wave.
5. being wrapped the method according to claim 1, wherein calculating the Sample Entropy of multiple isolated components after denoising
It includes:
An isolated component in multiple isolated components after choosing denoising, as current isolated component；
The first two-dimensional vector and the second two-dimensional vector are extracted from current isolated component；
Calculate the distance between first two-dimensional vector and second two-dimensional vector；
For each of current isolated component sampled point, the distance between the first two-dimensional vector and the second two-dimensional vector are counted
Less than the number of statistical threshold, calculate the number and the first two-dimensional vector and the second two-dimensional vector it is total at a distance from number ratio,
As the first ratio；
The mean value for calculating first ratio, as the first mean value；
The first trivector and the second trivector are extracted from current isolated component；
Calculate the distance between first trivector and second trivector；
For each of current isolated component sampled point, the distance between the first trivector and the second trivector are counted
Less than the number of the statistical threshold, calculate the number and the first trivector and the second trivector it is total at a distance from number ratio
Value, as the second ratio；
The mean value for calculating second ratio, as the second mean value；
According to first mean value and second mean value, the Sample Entropy of current isolated component is calculated；
Above-mentioned steps are repeated, until obtaining the Sample Entropy of all isolated components.
6. the device of eye electricity artefact in a kind of removal EEG signals, which is characterized in that described device includes:
Isolated component decomposing module obtains multiple independences for carrying out isolated component decomposition to the multichannel brain electric signal of acquisition
Component；
Module is denoised, for carrying out denoising to the multiple isolated component；
Sample Entropy computing module, for calculating the Sample Entropy of multiple isolated components after denoising；
The electric artefact determining module of eye determines the electrically independent component of brain and containing eye electricity for the Sample Entropy according to each isolated component
The isolated component of artefact；
The electric artefact of eye removes module, for removing eye electricity artefact to the isolated component containing eye electricity artefact；
Reconstructed module obtains removal eye electricity for the isolated component and the electrically independent component of brain that remove eye electricity artefact to be reconstructed
The EEG signals of artefact.
7. device according to claim 6, which is characterized in that further include:
Fourier transformation module obtains the multiple independence for carrying out Fourier transformation respectively to the multiple isolated component
The amplitude spectrum and phase spectrum of component.
8. device according to claim 6, which is characterized in that the isolated component decomposing module includes:
Pretreatment unit obtains multiple places for carrying out average value processing and whitening processing to the multichannel brain electric signal of acquisition
Observation vector after reason；
Separating vector solves unit, for solving corresponding separating vector, and solving according to each treated observation vector
When at least two separating vectors, decorrelative transformation is carried out to the separating vector solved；
Separation matrix determination unit, for the separating vector after decorrelative transformation to be formed separation matrix；
Isolated component determination unit, for according to the separation matrix, the observation vector and it is described go it is equal in average value processing
Value, obtains multiple isolated components.
9. a kind of equipment, which is characterized in that the equipment includes:
One or more processors；
Storage device, for storing one or more programs,
When one or more of programs are executed by one or more of processors, so that one or more of processors are real
Now such as the method for eye electricity artefact in removal EEG signals as claimed in any one of claims 1 to 5.
10. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the program is by processor
The method such as eye electricity artefact in removal EEG signals as claimed in any one of claims 1 to 5 is realized when execution.
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