CN106859640A - A kind of EEG measuring device and method based on independent component analysis - Google Patents
A kind of EEG measuring device and method based on independent component analysis Download PDFInfo
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- A61B5/316—Modalities, i.e. specific diagnostic methods
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Abstract
The invention discloses a kind of EEG measuring device based on independent component analysis, for the reference electrode tied up the elastic band on forehead, be placed on ear and the earth electrode being placed on ear；The elastic band is provided with several metal electrodes；The present invention need to only place the metal electrode of several smooth in appearance in forehead, the brain electricity composition and eye electricity composition in tracer signal, the analysis and assessment finally slept with isolated brain electricity and eye electricity are separated using independent component analysis and adaptive-filtering software algorithm.This electrode optimization method is avoided places a large amount of electrodes in all multiposition of brain, has been inherently eliminated influence of the electrode placement to patient's ortho；The prioritization scheme that the invention is proposed concentrates on forehead all electrodes, it is entirely avoided influence of the hair to electrode contact, so as to improve the signal quality of sleep monitor and the stability of result.
Description
Technical field
The present invention relates to medical testing field, and in particular to a kind of EEG measuring device and side based on independent component analysis
Method.
Background technology
The common drawback of existing sleep monitor EEG measuring is needed in multiple positions such as the occipital lobe of brain, top and frontal lobe
Electrode is laid, the presence meeting contact of the impeded electrode with scalp of a large amount of hairs, places to electrode and bring greatly on these positions
Challenge.Therefore traditional sleep monitor brain electricity wet electrode generally needs to use conductive paste, to reduce hair to electrode contact
Influence, reduces the contact resistance between electrode and skin.This wet electrode it is maximum the disadvantage is that, conductive paste within a certain period of time
Easily kill, this is obviously unfavorable for the long term monitoring of electrocardio and EEG signals.The use of conductive paste would generally give the skin tape of patient
Carry out excitant and produce uncomfortable sensation, occipital lobe lays metal electrode and can bring strong influence to the ortho of patient.
Generally also need to cut the hair of electrode riding position, the acceptance of patient is generally relatively low.Meanwhile, the placement of wet electrode
Needing the medical personnel of specialty is carried out, and causes medical personnel's workload larger, and the inspection fee of patient is higher, and there is patient
Skin is to conductive paste or the risk of electrode fixation adhesive tape allergy.
Although dry electrode can avoid the use of conductive paste, the shortcomings and limitations of itself are there is also.Condenser type is done
The sharpest edges of electrode can be to record biopotential signals across clothing, have the disadvantage that noise is smaller, easily be disturbed.Therefore
Larger electrode area is generally required, and the larger signal of relative intensity (such as monitoring of electrocardiosignal) can only have been measured.Therefore,
The EEG signals signal to noise ratio that the dry electrode of condenser type is collected is relatively low, and gatherer process stands up action and respiratory movement to patient all extremely
Sensitivity, the antijamming capability of signal is poor.The dry electrode of impedance type based on micro-nano columnar arrays is dense through hair due to being difficult to
Region, therefore be difficult be used for extract scalp brain electricity.The dry electrode of the pillar array of grade or Centimeter Level, can due to its large scale
Effectively to pass through hair, can be used for hair area.Because with larger contact impedance, this electrode is often at electrode rear portion
Immediately following a follow-up amplifier, referred to as active dry electrode (active dry electrode).The introducing of needle electrode, impedance type
Dry electrode often can produce tingling sensation in patient's sleep compressing electrode；The introducing of amplifier, can cause the cost of implementation of electrode
Higher, circuit design is complex, and moves extremely sensitive to the body of patient.
The content of the invention
The purpose of the present invention is directed to above-mentioned deficiency of the prior art, there is provided a kind of brain based on independent component analysis
Electrical measurement measuring device.
To achieve the above object, concrete scheme of the invention is as follows：A kind of EEG measuring device based on independent component analysis,
For the reference electrode tied up the elastic band on forehead, be placed on ear and the earth electrode being placed on ear；It is described
Elastic band is provided with several metal electrodes.
The present invention is further arranged to, and the spacing of the two neighboring metal electrode is equal.
The present invention is further arranged to, and the elastic band is provided with thread gluing.
Using a kind of method of measurement brain electricity of EEG measuring device described in claim 1, comprise the following steps：
A：Metal electrode in earth electrode, reference electrode and elastic band is accessed into related brain by electrode wires respectively
Electric collecting device, carries out the collection of sleep cerebral electricity, so as to draw brain electricity, the myoelectricity mixed signal of diverse location；
B：Mixed signal is calculated using Independent component analysis, separation is drawn including brain electric component and myoelectricity
Separate source signal；
C：Source signal is filtered using adaptive-filtering.
The present invention is further arranged to, Independent component analysis in stepb, comprises the following steps：
S1：According to formula X=[x1(t),x2(t),…xk(t)]TMixed signal matrix is drawn, the wherein value of k is metal
The quantity of electrode, xiT observation sample that () [i=1,2 ... k] is obtained for different metal electrode；
S2：Source signal matrix S=[s are set1(t),s2(t),…sz(t)]T, wherein z is the quantity of source signal, si(t)[i
=1,2 ... z] it is separate source signal, include the brain electric component such as myoelectricity and α ripples, β ripples；
S3：Hybrid matrix A is set, is used to describe the feature of signal mixed process；
S4：Noise matrix n is set；Draw the relation X=AS+n of mixed signal and source signal；
S5：The estimate Y of piece-rate system matrix W and source signal S is set, relation Y=WX is drawn；
S6：W values are calculated using fixing point algorithm, so as to draw separate source signal si(t) [i=1,2 ... z].
The present invention is further arranged to, the adaptive-filtering in step C, comprises the following steps：
K1：The initial value of initialization weight coefficient vector w, is set as 0；
K2：Update n=0,1,2 ..., with formula e (n)=d (n)-xT(n) w (n), w (n+1)=μ e of w (n)+2 (n) x
N () is calculated weights；
K3：Try to achieve filtering weighting coefficient w (n+1), repeat step K2, untill getting to stable state always；
K4：According to formulaEach source signal is filtered.
Beneficial effects of the present invention：The present invention proposes a kind of new electrode optimization method, only need to place 8 appearances in forehead
Smooth metal electrode, using independent component analysis and adaptive-filtering software algorithm separate brain electricity composition in tracer signal and
The electric composition of eye, the analysis and assessment finally slept with isolated brain electricity and eye electricity.This electrode optimization method is avoided
A large amount of electrodes are placed in all multiposition of brain, electrode has been inherently eliminated and has been laid influence to patient's ortho；The hair
The prioritization scheme of bright proposition concentrates on forehead all electrodes, it is entirely avoided influence of the hair to electrode contact, so as to carry
The signal quality and the stability of result of sleep monitor high.
Brief description of the drawings
Invention is described further using accompanying drawing, but embodiment in accompanying drawing does not constitute any limitation of the invention,
For one of ordinary skill in the art, on the premise of not paying creative work, it can also be obtained according to the following drawings
Its accompanying drawing.
Fig. 1 is the structural representation of EEG measuring device of the present invention；
Fig. 2 is the flow chart of present invention measurement brain method for electrically；
Fig. 3 is the theory diagram of brain electricity ingredient breakdown of the present invention；
Fig. 4 is the schematic diagram of brain electricity composition denoising of the present invention；
Fig. 5 is the structured flowchart of adaptive transversal filter of the present invention；
Wherein：1- elastic bands；2- metal electrodes；3- thread gluings.
Specific embodiment
The invention will be further described with the following Examples.
As shown in figure 1, a kind of EEG measuring device based on independent component analysis described in the present embodiment, for tying up in forehead
On elastic band 1, the reference electrode being placed on ear and the earth electrode being placed on ear；The elastic band 1 is provided with
Several metal electrodes 2.A kind of EEG measuring device based on independent component analysis described in the present embodiment, the two neighboring gold
The spacing for belonging to electrode 2 is equal.A kind of EEG measuring device based on independent component analysis described in the present embodiment, the elastic band 1
It is provided with thread gluing 3.Specifically, the EEG measuring device of the present embodiment needs to be connected with brain wave acquisition equipment when in use, metal
Electrode 2, reference electrode and earth electrode will be transmitted to brain wave acquisition equipment after measurement of correlation, then be calculated；Its
The quantity of middle metal electrode 2 is 8, and the centre of elastic band 1 is evenly distributed on according to the matrix of 4X2, is caused by thread gluing 3 elastic
Tied up on the forehead of user with 1.
Using a kind of method of measurement brain electricity of EEG measuring device described in claim 1, comprise the following steps：
A：Metal electrode 2 in earth electrode, reference electrode and elastic band 1 is accessed into correlation by electrode wires respectively
Brain wave acquisition equipment, carries out the collection of sleep cerebral electricity, so as to draw brain electricity, the myoelectricity mixed signal of diverse location；
B：Mixed signal is calculated using Independent component analysis, separation is drawn including brain electric component and myoelectricity
Separate source signal；
C：Source signal is filtered using adaptive-filtering.
The present invention is further arranged to, Independent component analysis in stepb, comprises the following steps：
S1：According to formula X=[x1(t),x2(t),…xk(t)]TMixed signal matrix is drawn, the wherein value of k is metal
The quantity of electrode 2, xiT observation sample that () [i=1,2 ... k] is obtained for different metal electrode 2；
S2：Source signal matrix S=[s are set1(t),s2(t),…sz(t)]T, wherein z is the quantity of source signal, si(t)[i
=1,2 ... z] it is separate source signal, include the brain electric component such as myoelectricity and α ripples, β ripples；
S3：Hybrid matrix A is set, is used to describe the feature of signal mixed process；
S4：Noise matrix n is set；Draw the relation X=AS+n of mixed signal and source signal；
S5：The estimate Y of piece-rate system matrix W and source signal S is set, relation Y=WX is drawn；
S6：W values are calculated using fixing point algorithm, so as to draw separate source signal si(t) [i=1,2 ... z].
The present invention is further arranged to, the adaptive-filtering in step C, comprises the following steps：
K1：The initial value of initialization weight coefficient vector w0, is set as 0；
K2：Update n=0,1,2 ..., with formula e (n)=d (n)-xT(n) w (n), w (n+1)=μ e of w (n)+2 (n) x
N () is calculated weights；
K3：Try to achieve filtering weighting coefficient wn+1, repeat step K2, untill getting to stable state always；
K4：According to formulaEach source signal is filtered.
Specifically, as shown in Figures 2 and 3, metal electrode 2 gather information when, can collect correlation myoelectricity with
And brain electric component, its midbrain electric component includes α ripples, β ripple aliquots, and myoelectricity can be mixed when collection with brain electric component
Close and mix in matrix A, meanwhile, have noise matrix n and disturbed, now, drawn point using Independent component analysis
From matrix W, so that isolated myoelectricity and brain electric component；And during optimal separation matrix W is found, set up independent criterion
The criterion such as function G, such as non-Gaussian system measurement, mutual information minimization, Informax and Maximum-likelihood estimation, using boarding steps
The methods such as degree method, adaptive algorithm, twiddle factor product algorithm, find the optimal solution of criterion function.Calculated using FastICA simultaneously
Method, also known as fixing point (Fixed-Point) algorithm.FastICA algorithms are substantially that a kind of minimum estimates component mutual information
Neural net method, is, come approximate negentropy, and by a suitable nonlinear function G and to reach using principle of maximum entropy
It is optimal；So as to draw the value of separation matrix W, source signal s can be obtained according to formula Y=WXiT () [i=1,2's ... z] estimates
Calculation value Y；
FastICA algorithms can also utilize principle of maximum entropy to estimate optimal source signal number, therefore the present invention is used
FastICA algorithms, first estimate the number of myoelectricity and brain electric component, then respectively obtain the corresponding time domain waveform of each component.This
Following treatment has also been done before invention application FastICA so that the calculating of ICA is simpler：
1 centralization：If each component average of mixed signal is all zero, the calculating of ICA can be simplified, therefore apply ICA
Usually by observation sample centralization before algorithm, the center of sample is set to move to zero point；
2 albefactions：One group of sample is given, their correlation is removed using linear transformation, obtain one group of sample independent mutually
This, and as the input of ICA, the convergence rate of ICA algorithm can be accelerated.Signal is entered using the method for Eigenvalues Decomposition generally
Row albefaction；
3 filtering：Except centralization and albefaction, some occasions are also using wave filter to being mixed with the observation sample of incoherent noise
It is filtered, to reduce influence of the noise to result.
As shown in Figure 4 and Figure 5, adaptive-filtering mainly includes the digital filter and adaptive algorithm two of Parameter adjustable
Point, wherein x (n) is referred to as input signal, and y (n) is referred to as output signal, and d (n) is referred to as desired signal, and e (n) is error signal, its
In, e (n)=d (n)-y (n).Here, desired signal d (n) is come selection, the output of sef-adapting filter according to different purposes
Signal y (n) estimates that desired signal d (n) control and adjust automatically of the filtering parameter by error signal e (n) make
Output signal y (n) is closest to desired signal d (n).In addition to e (n), input signal x (n) is also used sometimes wave filter is joined
Number is adjusted.Sef-adapting filter control mechanism is to its wave filter system with error sequence e (n) according to certain criterion and algorithm
Number is adjusted, and finally makes the minimization of object function of adaptive-filtering.In adaptive algorithm, least mean square algorithm Least
Mean Square, LMS are with widest class algorithm.Adaptive transversal filter structured flowchart, it is that a kind of have can
Adjust the transversal filter of tap weight coefficient, weight coefficient w1(n), w2(n) ..., wMN () represents the value at the n moment.
Make weight coefficient vector：W (n)=[w1(n),w2(n),…wM(n)]T, filter tap input signal vector：x(n)
=[x (n), x (n-1) ... x (n-M)]T, therefore, output signal y (n) is
Error sequence e (n) can be write as e (n)=d (n)-y (n).The criterion that LMS algorithm is used is to make the expectation of wave filter
(mean square error refers to parameter to the criterion that mean square error (MSE) between output valve and real output value is minimized in mathematical statistics
The desired value of the difference square of estimate and parameter true value, the value of MSE is smaller, illustrates that forecast model describes experimental data and has more preferably
Accuracy), i.e. the desired value of the square value of e (n) is minimum, and changes weight coefficient w (n) to reach most according to this criterion
It is excellent, that is, determine that w (n) makes the value of object function minimum.
Steepest descent method, also known as gradient descent method, is an optimization iterative algorithm, and it analyzes adaptive using gradient information
Answer filtering performance and follow the trail of optimum filtering state.The iterative formula of the LMS algorithm based on steepest descent method is as follows：
W (n+1)=μ e of w (n)+2 (n) x (n)
This formula is the final expression formula of LMS algorithm, and in formula, x (n) is the input of sef-adapting filter；D (n) is reference signal；
E (n) is error；W (n) is weight coefficient；μ is step-length.The convergent condition of LMS algorithm is：0<μ<1/λmax, λmaxIt is input signal
The eigenvalue of maximum of autocorrelation matrix.In actual use, it is necessary to reasonable selection step-size parameter mu, the parameter will influence algorithm
Convergence and imbalance performance.
Based on above derivation, LMS adaptive filter algorithms are as follows to the flow that brain electric component carries out denoising：
Step 1：Initialization weight coefficient vector w (0) is arbitrary initial value, can typically be set to 0；
Step 2：Update n=0,1,2 ..., weights are calculated with following two formula：
E (n)=d (n)-xT(n)w(n)
W (n+1)=μ e of w (n)+2 (n) x (n)
Step 3：Try to achieve filtering weighting coefficient w (n+1), repeat step 2, untill reaching up to stable state.
Step 4：According to formulaEach obtained to ICA algorithm point
Amount is filtered, so that the further interference such as removal ambient noise, spontaneous brain electricity.
Finally it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than the present invention is protected
The limitation of scope is protected, although being explained to the present invention with reference to preferred embodiment, one of ordinary skill in the art should
Work as understanding, the technical scheme invented can be modified or equivalent, without deviating from the essence of technical solution of the present invention
And scope.
Claims (6)
1. a kind of EEG measuring device based on independent component analysis, it is characterised in that：Include elastic on forehead for tying up
Band (1), the reference electrode being placed on ear and the earth electrode being placed on ear；The elastic band (1) is provided with some
Individual metal electrode (2).
2. according to claim requirement 1 described in a kind of EEG measuring device based on independent component analysis, it is characterised in that：It is adjacent
The spacing of two metal electrodes (2) is equal.
3. according to claim requirement 1 described in a kind of EEG measuring device based on independent component analysis, it is characterised in that：It is described
Elastic band (1) is provided with thread gluing (3).
4. using the method that a kind of measurement brain of EEG measuring device described in claim 1 is electric, it is characterised in that：Including following step
Suddenly：
A：Metal electrode (2) in earth electrode, reference electrode and elastic band (1) is accessed into correlation by electrode wires respectively
Brain wave acquisition equipment, carries out the collection of sleep cerebral electricity, so as to draw brain electricity, the myoelectricity mixed signal of diverse location；
B：Mixed signal is calculated using Independent component analysis, separation draws mutual including brain electric component and myoelectricity
Independent source signal；
C：Source signal is filtered using adaptive-filtering.
5. it is according to claim 4 it is a kind of measure brain electricity method, it is characterised in that：Isolated component in stepb point
Analysis method, comprises the following steps：
S1：According to formula X=[x1(t),x2(t),…xk(t)]TMixed signal matrix is drawn, the wherein value of k is metal electrode
(2) quantity, xiT observation sample that () [i=1,2 ... k] is obtained for different metal electrode (2)；
S2：Source signal matrix S=[s are set1(t),s2(t),…sz(t)]T, wherein z is the quantity of source signal, si(t) [i=1,
2 ... z] it is separate source signal, include the brain electric component such as myoelectricity and α ripples, β ripples；
S3：Hybrid matrix A is set, is used to describe the feature of signal mixed process；
S4：Noise matrix n is set；Draw the relation X=AS+n of mixed signal and source signal；
S5：The estimate Y of piece-rate system matrix W and source signal S is set, relation Y=WX is drawn；
S6：W values are calculated using fixing point algorithm, so as to draw separate source signal si(t) [i=1,2 ... z].
6. it is according to claim 5 it is a kind of measure brain electricity method, it is characterised in that：Adaptive-filtering in step C,
Comprise the following steps：
K1：The initial value of initialization weight coefficient vector w (0), is set as 0；
K2：Update n=0,1,2 ..., with formula e (n)=d (n)-xTN () w (n), w (n+1)=μ e of w (n)+2 (n) x (n) are to power
Value is calculated；
K3：Try to achieve filtering weighting coefficient w (n+1), repeat step K2, untill stable state；
K4：According to formulaEach source signal is filtered.
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Cited By (1)
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Application publication date: 20170620 |
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