CN1792324A - Method for eliminating brain noise - Google Patents
Method for eliminating brain noise Download PDFInfo
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- CN1792324A CN1792324A CN 200510021945 CN200510021945A CN1792324A CN 1792324 A CN1792324 A CN 1792324A CN 200510021945 CN200510021945 CN 200510021945 CN 200510021945 A CN200510021945 A CN 200510021945A CN 1792324 A CN1792324 A CN 1792324A
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Abstract
A method for removing electroencephalic noise includes such steps as determining transfer array, using multi-channel electroencephalic signal detection system to obtain actually recorded electroencephalic signal Y, finding out the electroencephalic inverse problem to obtain the estimated value of equivalent source distribution, and forward calculating to obtain the estimated the potential value after the noise has been removed by use of said estimated equivalent source distribution.
Description
Technical field
A kind of method of removing brain noise belongs to the biology information technology field, relates to a kind of removal method of brain noise, be mainly used in human brain function and with the research and the diagnosis of human brain relevant disease.
Background technology
Before multiple tracks eeg recording signal being carried out deep processing, analyzing, be necessary to remove the noise jamming of sneaking in the EEG signals.Current have a variety of methods to eliminate noise in the brain electricity, and relatively more commonly used have a wavelet decomposition (Quiroga RQ 2000Obtaining single stimulus evoked potentials with wavelet denoising Phy.D145278-92.; Schiff SJ, Aldrouby A, Unser M, Sato S 1994 Fast wavelet transformationof EEG, Electr.Clin.Neurophysiol.91442-455.), sef-adapting filter (Benny SC, Hu Y, Lu W, Keith DK, Chang CQ, Qiu W, Francis HY 2005Multi-adaptive filtering techniquefor surface somatosensory evoked potentials processing Medical engineering ﹠amp; Physics 27257-66.), independent component analysis (Jung TP., Makeig S., McKeown MJ, Bell AJ, LeeTW, Sejnowski TJ 2001 Imaging brain dynamics using independent component analysisProc IEEE 89 1107-1122.), method such as principal component analysis and bandpass filtering.
The signal model of above method is generally: Y=S+ ε, and wherein Y is the observation primary signal, S is the signal that does not have sound pollution, the noise of ε for introducing in the record.These methods mostly are to consider noise remove from the signal processing aspect, do not consider the physiological property and the individual variation thereof of brain, are the denoising methods with Human Physiology characteristic irrelevant (physiology free).These class methods are to be based upon on the statistics or the property difference that becomes to grade of signal and noise, and this species diversity is unconspicuous sometimes, thereby have influenced the isolating effect of noise, and the separating resulting that obtains may not be inconsistent with physiology is actual.Present technique is emphasized useful signal all from brain inside, therefore can utilize the anatomical features of brain, and consider that there is individual variation in people's brain anatomical structure, and therefore realistic head model is used in suggestion.The signal that adopts this thinking to separate has tangible physiological correlations.Present technique is not repelled existing filtering method, promptly after using present technique, can be according to circumstances, and further use existing other filtering method and handle.
Summary of the invention
The invention provides a kind of space brain noise removal method,, can obtain to meet more the denoising result of Electroencephalo condition by individual difference is considered in the process of noise remove based on the individual physical difference constraint.
Based on head model denoising principle:
In the method, establishing an apparent multitrack recording EEG signals that measures is:
Y＝AX+ε (1)
Wherein Y is the top layer electric potential signal that utilizes the multitrack recording electrode detection to arrive from the head, is the matrix of M * T; A is that dimension is the transfer matrix of M * N, and X is a human brain internal activity source information matrix, and dimension is N * T, ε be in record, introduce with the incoherent noise signal of transfer matrix.In current EEG research, A normally scans the actual image information that is obtained by magnetic functional imaging technology (MRI)/computer tomography technology (CT) to human brain, utilizing dipole model (or other equivalent brain power source model, as point charge etc.) to carry out numerical computations obtains.The noise that exists in record is to EEG research and analyze very big influence is arranged, and before the brain electricity is analysed in depth, is necessary to carry out the early stage filter preprocessing to reduce or to eliminate the influence of noise ε.Current filtering method great majority only are to consider from the signal processing aspect, such as: if select small echo to come filtering, then, all adopt same wavelet basis to decompose, and wavelet basis differ and well portray the physiological feature of all individual brain electricity surely to all experimental subject data.Simultaneously, people's EEG signals has very big differences of Physiological because of individual difference, in order to obtain rational result, is necessary physiological property is taken into account in processing procedure.Transfer matrix A is a linear approximation portrayal to human brain neuroelectricity physiological activity characteristic, its string is illustrated in the current potential spatial distribution that produces at the head table when placing the unit source on the correspondence position, so transfer matrix reflects the spatial distribution physiological property of brain electricity to a certain extent.From (1) formula t observation voltage Y constantly as can be seen
tCan be expressed as,
Y
t＝AX
t+ε
t，1≤t≤T (2)
X wherein
tFor at t distribution in electrical activity source in the brain constantly time the, ε
tBe the noise in t writes down constantly.(2) equation represented of formula can be found the solution and obtain this moment X by multiple brain power supply inverting localization method (electroencephalography (eeg) inverse problem method)
tDistribution.Because inverting is to carry out X under the constraint of the A that individual variation is arranged
tEstimated result X
tSatisfy this constraint, thereby meet people's physiological property, represented the endogenous information of brain, and the noise ε that introduces when measuring
tThen be suppressed because of the constraint of not satisfying A.So, calculate through brain electricity forward model again: Y
t=AX
t, just can recover the source in removal that the head table produces the current potential after the outside noise influence.
Detailed technology scheme of the present invention is:
A kind of method of removing brain noise may further comprise the steps:
Step 1. is determined transfer matrix A, comprises step by step following:
1), the head of object to be measured is carried out MRI or CT scan, obtains the image information of cranial anatomy structure;
2), extraction step 1) brain part in the image information of gained, then brain is cut apart, extract the functional areas, source (mainly comprising positions such as grey matter, Hippocampus, cerebellum) of brain part again;
3), with the grid of certain precision with step 2) functional areas, brain source of gained carry out subdivision, determine solution space grid (the locus sequence number that comprises dimension He each grid of solution space);
4), determine the spatial positional information of each electrode of multiple tracks EEG signals recording system;
5), determine the model of brain power supply:
6), solution space grid, electrode position information and the brain power source model that utilizes step 3) to determine in the step 5), utilize forward modeling method to calculate transfer matrix A, concrete grammar is as follows: the source of placing unit on each solution space position, utilize numerical computation method to calculate the Potential distribution that this unit source produces at the electrode position place, this Potential distribution constitutes the string in the transfer matrix, by that analogy, after all solution space traversals are placed the unit source, just can obtain transfer matrix A;
Step 2. is obtained the EEG signals Y of physical record by multiple tracks EEG signals recording system, normally under certain test stimulus of design, obtains the stimuli responsive current potential;
Step 3. electroencephalography (eeg) inverse problem is found the solution, and obtains the estimated value X of equivalent source distribution X: promptly for Y
t=AX
t+ ε
t, 1≤t≤T is with the observation Y in a certain moment
tDetermine the endogenous distribution X of brain in this moment
tEstimated value
To difference observation Y constantly
t, carry out above-mentioned inverse problem and find the solution, obtain the estimation equivalent source distribution matrix X in this observation time section.
Step 4. is just being drilled calculating, utilizes the equivalent source distribution X that estimates to obtain the current potential spatial domain estimated result Y:Y=A X behind the removal noise in the observation time section.
In the such scheme, the grid of the certain precision described in the step 3) of step 1. is taken all factors into consideration computational accuracy and efficient, generally gets the 10mm/ lattice; Multiple tracks EEG signals recording system described in the step 4) of step 1. can be the EEG signals recording system of 32 roads, 64 roads, 128 roads and the 256 road electrodes of standard; Brain power source model described in the step 5) of step 1. is generally point charge model or dipole model; Numerical computation method described in the step 6) of step 1. can be boundary element algorithm or finite element algorithm; The method for solving of electroencephalography (eeg) inverse problem described in the step 3. has a lot, such as: low chromatography imaging method, FOCUSS method, the l of differentiating
p(p≤1) sparse solution, minimum modulus are separated, Subspace Decomposition and Weighted optimal differentiate and separate etc. (Yao is German-Chinese. electricity theory and method that brain function is surveyed.Beijing: Science Press, 2003,195-243), these methods have fully utilized technology such as the physiological bounds of transfer matrix and regularization when X is estimated, can remove effect of noise, obtain the estimated result X of source distribution X.
Beneficial effect of the present invention:
The former method of comparing, this method mainly contains following advantage: 1. utilize actual MRI/CT image information (realistic head model) to calculate transfer matrix, by transfer matrix individual physical difference is considered the denoising process; 2. utilize electroencephalography (eeg) inverse problem to calculate the interior source distribution of equivalent brain that acquisition is subjected to individual physiological bounds; 3. the head table Potential distribution after the noise jamming is removed in brain electricity forward model effect, acquisition.
Description of drawings
Fig. 1 a kind of flow chart of removing the method for brain noise of the present invention.
The topography contrast (300ms-340ms period) of overlooking of the eeg data of the denoising result of one section true EEG signals of Fig. 2 is schemed.
The topography contrast (344ms-380ms period) of overlooking of the eeg data of the denoising result of one section true EEG signals of Fig. 3 is schemed.
The specific embodiment
In following two embodiments, (Boundary Elements Method, BEM), head model generates with the MRI image just to drill the employing boundary element.L is adopted in inverting
p(p=1) the sparse inversion method of loft.We have carried out denoising to one section EEG signals of an analogue signal and true record, and contrast with the small echo denoising result that adopts usually, and following result is arranged.
The specific embodiment one-simulation denoising result:
Method: under the realistic head model, the MRI head model that obtains by scanning, the dipole source moving position is limited to the grey matter, Hippocampus of brain and other may the source movable part, is separated into 910 positions by the 10mm mesh generation, employing standard 128 road electrode systems calculate and obtain transfer matrix A.Place the fixed dipole source of square 34 fixed mesh subdivision positions (being the distributed source in a lamellar zone) and simulate the table record current potential that a certain moment produces, to its Gaussian noise that applies varying level, the noise level that relates in this work is meant the energy ratio of noise and signal.Utilize respectively based on the denoising method and small echo (adopting the Symmlet small echo the to carry out 5 grades of decomposition in this experiment) denoising method of head model and do denoising mixing noisy this simulation moment signal, simultaneously to the denoising result of two kinds of methods, calculated correlation coefficient (CC) and the relative error (RE) of itself and primary signal respectively, the result is presented in the following table 1.
Correlation coefficient (CC) under the different noise levels of table 1 and relative error (RE)
| Noise level | CC | RE | ||
| Based on head model | Wavelet | Based on head model | Wavelet | |
| 10％ | 0.9978 | 0.9938 | 0.48％ | 1.50％ |
| 20％ | 0.9949 | 0.9657 | 1.04％ | 7.85％ |
| 30％ | 0.9896 | 0.9373 | 2.34％ | 12.98％ |
| 40％ | 0.9855 | 0.9116 | 2.90％ | 16.94％ |
| 50％ | 0.9764 | 0.8910 | 4.92％ | 20.71％ |
From the quantitative analysis relatively to the denoising result on the different noise levels of analog data, obviously being better than with the small echo based on the denoising method of realistic head model as can be seen is the denoising method that is not subjected to physiological bounds of representative.
The specific embodiment two-to the denoising result of one section true EEG signals
Method: in vision and the experiment of audition binary channel synchronous detecting, with oddball is stimulus modelity, under the sample rate of 250HZ, obtain 128 road eeg datas, per pass data correspondence 211 stimulations, each stimulate corresponding the eeg data of 1.2s, choosing stimulates for the 35th time the one piece of data between 300ms ~ 400ms in the correspondent section to carry out decomposition denoising experiment based on head model.Before handling according to the actual electrode coordinate that records, 128 electrodes after carrying out registration on the realistic head model, with simulation experiment in similar mode calculate transfer matrix A.Topography to the eeg data of data before and after the processing compares, and the result is presented among Fig. 2,3.
To this oddball stimulus data, in topography, electrical energy of brain should mainly concentrate on occipital lobe (Occipital) part, from filtered result as can be seen: after of the method filtering of data process based on realistic head model, become scattered about other regional noises and effectively eliminated, signal energy mainly concentrates on occipital lobe (Occipital) part.Compare with the denoising result of small echo, topography based on the filtering result of realistic head model method is smoother and clear, the physiological property foundation that meets the brain electricity more: head table Potential distribution is the result of the current potential that produces of source after through low-pass filtering such as skulls, should be level and smooth.
Claims (6)
1, a kind of method of removing brain noise is characterized in that may further comprise the steps:
Step 1. is determined transfer matrix A, comprises step by step following:
1), the head of object to be measured is carried out MRI or CT scan, obtains the image information of cranial anatomy structure;
2), extraction step 1) brain part in the image information of gained, then brain is cut apart, extract the functional areas, source (mainly comprising positions such as grey matter, Hippocampus, cerebellum) of brain part again;
3), with the grid of certain precision with step 2) functional areas, brain source of gained carry out subdivision, determine solution space grid (the locus sequence number that comprises dimension He each grid of solution space);
4), determine the spatial positional information of each electrode of multiple tracks EEG signals recording system;
5), determine the model of brain power supply;
6), solution space grid, electrode position information and the brain power source model that utilizes step 3) to determine in the step 5), utilize forward modeling method to calculate transfer matrix A, concrete grammar is as follows: the source of placing unit on each solution space position, utilize numerical computation method to calculate the Potential distribution that this unit source produces at the electrode position place, this Potential distribution constitutes the string in the transfer matrix, by that analogy, after all solution space traversals are placed the unit source, just can obtain transfer matrix A;
Step 2. is obtained the EEG signals Y of physical record by multiple tracks EEG signals detection system, normally under certain test stimulus of design, obtains the stimuli responsive current potential;
Step 3. electroencephalography (eeg) inverse problem is found the solution, and obtains the estimated value X of equivalent source distribution X: promptly for Y
t=AX
t+ ε
t, 1≤t≤T is with the observation Y in a certain moment
tDetermine the endogenous distribution X of brain in this moment
tEstimated value
To difference observation Y constantly
t, carry out above-mentioned inverse problem and find the solution, obtain the estimation equivalent source distribution matrix X in this observation time section.
Step 4. is just being drilled calculating, utilizes the equivalent source distribution X that estimates to obtain the current potential spatial domain estimated result Y behind the removal noise in the observation time section: Y=A X.
2, a kind of method of removing brain noise according to claim 1 is characterized in that, the grid of the certain precision described in the step 3) of step 1. is taken all factors into consideration computational accuracy and efficient, generally gets the 10mm/ lattice.
3, a kind of method of removing brain noise according to claim 2, it is characterized in that the multiple tracks EEG signals recording system described in the step 4) of step 1. can be the EEG signals recording system of standard 32 road electrodes, the EEG signals recording system of standard 64 road electrodes or the EEG signals recording system of standard 128 road electrodes etc.
4, a kind of method of removing brain noise according to claim 1 is characterized in that, the brain power source model described in the step 5) of step 1. is generally point charge model or dipole model.
5, a kind of method of removing brain noise according to claim 4 is characterized in that, the numerical computation method described in the step 6) of step 1. can be boundary element algorithm or finite element algorithm.
6, a kind of method of removing brain noise according to claim 1 is characterized in that, the method for solving of electroencephalography (eeg) inverse problem described in the step 3. has a lot, such as: low chromatography imaging method, FOCUSS method, the l of differentiating
P(P≤1) sparse solution, minimum modulus are separated, Subspace Decomposition and Weighted optimal are differentiated and separated etc., these methods are when estimating X, fully utilize technology such as the physiological bundle of transfer matrix and regularization, can remove effect of noise, obtained the estimated result X of source distribution X.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101548885B (en) * | 2009-04-17 | 2010-12-08 | 南京大学 | Method for eliminating power frequency interfering signals in electrophysiological signals |
| CN104545897A (en) * | 2014-12-04 | 2015-04-29 | 电子科技大学 | Conversion device and conversion method for electroencephalogram record references |
| CN105395194A (en) * | 2015-12-14 | 2016-03-16 | 中国人民解放军信息工程大学 | Electroencephalogram (EEG) channel selection method assisted by functional magnetic resonance imaging |
| CN109144277A (en) * | 2018-10-19 | 2019-01-04 | 东南大学 | A kind of construction method for realizing brain control intelligent carriage based on machine learning |
| CN111317466A (en) * | 2019-07-03 | 2020-06-23 | 重庆邮电大学 | Electroencephalogram signal imaging method and system and computer equipment |
| CN111513711A (en) * | 2020-05-22 | 2020-08-11 | 电子科技大学 | Electroencephalogram bad lead interpolation method based on reference electrode |
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| US4753246A (en) * | 1986-03-28 | 1988-06-28 | The Regents Of The University Of California | EEG spatial filter and method |
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101548885B (en) * | 2009-04-17 | 2010-12-08 | 南京大学 | Method for eliminating power frequency interfering signals in electrophysiological signals |
| CN104545897A (en) * | 2014-12-04 | 2015-04-29 | 电子科技大学 | Conversion device and conversion method for electroencephalogram record references |
| CN105395194A (en) * | 2015-12-14 | 2016-03-16 | 中国人民解放军信息工程大学 | Electroencephalogram (EEG) channel selection method assisted by functional magnetic resonance imaging |
| CN105395194B (en) * | 2015-12-14 | 2018-03-16 | 中国人民解放军信息工程大学 | A kind of brain electric channel system of selection of functional mri auxiliary |
| CN109144277A (en) * | 2018-10-19 | 2019-01-04 | 东南大学 | A kind of construction method for realizing brain control intelligent carriage based on machine learning |
| CN109144277B (en) * | 2018-10-19 | 2021-04-27 | 东南大学 | Method for constructing intelligent vehicle controlled by brain based on machine learning |
| CN111317466A (en) * | 2019-07-03 | 2020-06-23 | 重庆邮电大学 | Electroencephalogram signal imaging method and system and computer equipment |
| CN111513711A (en) * | 2020-05-22 | 2020-08-11 | 电子科技大学 | Electroencephalogram bad lead interpolation method based on reference electrode |
| CN111513711B (en) * | 2020-05-22 | 2021-06-04 | 电子科技大学 | Electroencephalogram bad lead interpolation method based on reference electrode |
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