CN108836321A - A kind of EEG signals preprocess method based on adaptive noise cancel- ation system - Google Patents
A kind of EEG signals preprocess method based on adaptive noise cancel- ation system Download PDFInfo
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- CN108836321A CN108836321A CN201810413031.8A CN201810413031A CN108836321A CN 108836321 A CN108836321 A CN 108836321A CN 201810413031 A CN201810413031 A CN 201810413031A CN 108836321 A CN108836321 A CN 108836321A
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/369—Electroencephalography [EEG]
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
Abstract
The present invention relates to a kind of EEG signals preprocess methods based on adaptive noise cancel- ation system, the electro-ocular signal in original EEG signals is estimated using fast independent component analysis (FastICA) and wavelet transform (DWT), reference eye electrical input signal as the adaptive noise canceling (ANC), automatically removing for eye electricity artefact in EEG signals is realized by sef-adapting filter and weight coefficient update module, realizes that electro-ocular signal component automatic identification, parameter are automatically updated and automatically removed with electro-ocular signal to solve.
Description
Technical field
The present invention relates to physiology EEG Processing field, in particular to one kind may be implemented noise component(s) automatic identification,
Parameter automatically updates the method automatically removed with noise signal.
Background technique
EEG signals (EEG) are the important bioelectrical signals of human body, include a large amount of biological information of human body.EEG signals with
Brain cognitive process one changes, and can make and respond rapidly to outside stimulus.By being monitored to electroneurographic signal in art,
The reason of feeding back the situation of change of nervous function integrality in art to operation and Anesthetist, finding cognitive impairment in time,
The precautionary measures can be taken to avoid irreversible damage, reduce the risk of postoperative nerve functional impairment.
However, inevitably generating eye because of eyes or eye movement in EEG signals during acquisition
Electric signal (EOG) is formed eye electricity artefact (OA) to seriously affect EEG.And eye electricity artefact is seriously affected since amplitude is larger
The analysis and application of EEG signals.In order to improve acquisition eeg data signal-to-noise ratio, to the data of acquisition carry out pretreatment be
Very important, the eye electricity artefact effectively removed in EEG signals is particularly important.
Currently, commonly the method for removal eye electricity artefact has：Wavelet transformation (WT) and independent component analysis (ICA) etc., WT
It is a kind of typical Time-Frequency Analysis Method, but this method is influenced by the subjective selection of wavelet basis function and Decomposition order, is lacked
Adaptivity；Independent component analysis (ICA) can effectively remove the local feature of signal, but this method solves the meter of separation matrix
Calculation amount is big, in addition will appear and not restrain, real-time is not good enough.For existing methods disadvantage.
Summary of the invention
To solve the above-mentioned problems, the technical scheme is that：
A kind of EEG signals preprocess method based on adaptive noise cancel- ation system, includes the following steps：
S1：Several isolated mutual independent components of FastICA method, meter first are utilized to collected EEG signals
It calculates the coefficient of kurtosis value of each component and automatically identifies an electric component according to the value；Then Mallat QMF compression is utilized
Algorithm carries out the electro-ocular signal that L layer scattering wavelet decomposition is estimated to the eye electric component.
S2：The electro-ocular signal for the estimation that step 1 is obtained is as the reference eye telecommunications of the adaptive noise canceling
It number is inputted；It is handled with reference to electro-ocular signal by the sef-adapting filter based on least square method of recursion described, it will
Pure electro-ocular signal is processed into reference to electro-ocular signal, realize weight coefficient automatically update and optimal filter；Then from acquisition
Pure electro-ocular signal is subtracted in EEG signals, is cancelled the electro-ocular signal in output signal completely, and is only retained useful
EEG signals realize automatically removing for electro-ocular signal.
Preferably, the acquisition methods of the component are：
The FastICA algorithm utilized is by collected EEG signals Y (n)=[y1(n),y2(n),...,yL(n)]TSeparation
At m mutual independent component S (n)=[s1(n),s2(n),...,sm(n)]T；
Wherein, Y (n)=AS (n), S (n)=WY (n), L are port number, and n is sample points, W=[w1,w2,...,
wn] it is separation matrix, A=W-1；Objective function Equation based on the maximum FastICA algorithm of negentropy is：Its
In,J(si)≈ρ(E{Gi(si)}-E{Gi(ν)})2, ρ is normal number, Gi() is non-quadratic function, and E () is
Mean function, ν are that mean value is the gaussian variable that zero variance is 1.
Preferably, the calculation formula of the coefficient of kurtosis value of the component is：
Wherein,
Compared with the existing technology, the beneficial effects of the invention are as follows：
1, the present invention utilizes fast independent component analysis (FastICA) and wavelet transform (DWT) by original brain telecommunications
Electro-ocular signal in number estimates, as the reference eye electrical input signal of the adaptive noise canceling (ANC), by adaptive
It answers filter and weight coefficient update module to realize automatically removing for eye electricity artefact in EEG signals, realizes eye telecommunications to solve
Number component automatic identification, parameter are automatically updated and are automatically removed with electro-ocular signal.
2, using method provided by the present application, the accuracy and eye electrical interference removal effect of electro-ocular signal estimation are improved,
It avoids in experimentation and electro-ocular signal acquisition directly is carried out to subject, reduce the discomfort in subject's experiment.With fast
It the advantages that speed, accurate, adaptive and optimal filter, has great practical value.
Detailed description of the invention
Fig. 1 is system block diagram of the invention；
Fig. 2 is Mallat algorithm Signal separator schematic diagram.
Specific embodiment
It is described in detail below in conjunction with the drawings and specific embodiments.
A kind of EEG signals preprocess method based on the adaptive noise canceling, includes the following steps：
S1：Several isolated mutual independent components of FastICA method, meter first are utilized to collected EEG signals
It calculates the coefficient of kurtosis value of each component and automatically identifies an electric component according to the value；Then Mallat QMF compression is utilized
Algorithm carries out the electro-ocular signal that L layer scattering wavelet decomposition is estimated to the eye electric component.
S2：The electro-ocular signal for the estimation that step 1 is obtained is as the reference eye telecommunications of the adaptive noise canceling
It number is inputted；It is handled with reference to electro-ocular signal by the sef-adapting filter based on least square method of recursion described, it will
Pure electro-ocular signal is processed into reference to electro-ocular signal, realize weight coefficient automatically update and optimal filter；Then from acquisition
Pure electro-ocular signal is subtracted in EEG signals, is cancelled the electro-ocular signal in output signal completely, and is only retained useful
EEG signals realize automatically removing for electro-ocular signal.
The present invention is estimated the electro-ocular signal in original EEG signals using FastICA and wavelet transform DWT,
As the reference eye electrical input signal of the adaptive noise canceling (ANC), mould is updated by sef-adapting filter and weight coefficient
Block realizes that eye electricity artefact automatically removes in EEG signals, thus solve realize electro-ocular signal component automatic identification, parameter from
Dynamic update automatically removes with electro-ocular signal.
Specifically, referring to Fig. 1, the acquisition methods of above-mentioned component are：It will using the maximum FastICA algorithm of negentropy is based on
Collected EEG signals Y (n)=[y1(n),y2(n),...,yL(n)]TIt is separated into m independent component S (n)=[s mutually1
(n),s2(n),...,sm(n)]T；
Wherein, Y (n)=AS (n), S (n)=WY (n), L are port number, and n is sample points, W=[w1,w2,...,
wn] it is separation matrix, A=W-1；
For the process for being separated into isolated component convenient for EEG signals are more clearly understood, provided herein based on negentropy maximum
FastICA algorithm objective function, the description of following formula (1)：
Wherein,J(si)≈ρ(E{Gi(si)}-E{Gi(ν)})2, ρ is normal number, Gi() is non-secondary letter
Number, E () are mean function, and ν is that mean value is the gaussian variable that zero variance is 1.
Specifically, calculating the coefficient of kurtosis value of each component using following formula (2) and automatically identifying eye according to the value
Electrical signal component seog(n), wherein
Specifically, carrying out multiscale analysis to the electro-ocular signal component identified using DWT, tower point of Mallat is utilized
Resolving Algorithm carries out L layer scattering wavelet decomposition to electro-ocular signal component and obtains with reference to electro-ocular signal r2(n)。
Specifically, referring to EEG signals r for obtained above2(n) input signal as adaptive noise cancel- ation system.
Sef-adapting filter based on least square method of recursion (RLS) is filtered reference electro-ocular signal, while output signal acts on
It in being based on least square method of recursion parameter updating module, realizes automatically updating for parameter, is optimal filtering, to realize eye electricity
Signal automatically remove and optimal filter, obtain pure electro-ocular signalFinally obtain clean EEG signals
The application in order to better understand herein sketches Mallat algorithm Signal separator.Referring to Fig. 2,With
Respectively low frequency and high-frequency decomposition filter coefficient, ↓ 2 indicate down-sampling process, as space scale j is gradually increased by 1, realize
More resolution decompositions of signal obtain the Coefficients of Approximation component and each rank detail coefficients component of signal.
By Coefficients of Approximation component ALZero setting, detail coefficients component { DL, DL-1..., D1Remain unchanged, as reference eye electricity
Signal
To any discrete function f (t) ∈ L2(R), wavelet transform is defined as：
Wherein, j, k are respectively frequency resolution and timing shift amount, j, k ∈ Z,For discrete wavelet function, meet For Ψj,k(t) conjugation.
Disclosed above is only the specific embodiment of the application, and however, this application is not limited to this, any this field
Technical staff can think variation, should all fall in the protection domain of the application.
Claims (3)
1. a kind of EEG signals preprocess method based on adaptive noise cancel- ation system, includes the following steps：
S1：First collected EEG signals are calculated every using several isolated mutual independent components of FastICA method
The coefficient of kurtosis value of a component simultaneously automatically identifies an electric component according to the value；Then Mallat QMF compression algorithm is utilized
The electro-ocular signal that L layer scattering wavelet decomposition is estimated is carried out to the eye electric component.
S2：The electro-ocular signal for the estimation that step 1 is obtained as the reference electro-ocular signal of the adaptive noise canceling into
Row input；It is handled with reference to electro-ocular signal by the sef-adapting filter based on least square method of recursion described, will be referred to
Electro-ocular signal is processed into pure electro-ocular signal, realize weight coefficient automatically update and optimal filter；Then from the brain electricity of acquisition
Pure electro-ocular signal is subtracted in signal, is cancelled the electro-ocular signal in output signal completely, and only retains useful brain electricity
Signal realizes automatically removing for electro-ocular signal.
2. preprocess method as described in claim 1, which is characterized in that the acquisition methods of the component are：
The FastICA algorithm utilized is by collected EEG signals Y (n)=[y1(n),y2(n),...,yL(n)]TIt is separated into m
Mutual independent component S (n)=[s1(n),s2(n),...,sm(n)]T；
Wherein, Y (n)=AS (n), S (n)=WY (n), L are port number, and n is sample points, W=[w1,w2,...,wn] be
Separation matrix, A=W-1；Objective function Equation based on the maximum FastICA algorithm of negentropy is：Wherein,J(si)≈ρ(E{Gi(si)}-E{Gi(ν)})2, ρ is normal number, Gi() is non-quadratic function, and E () is equal
Value function, ν are that mean value is the gaussian variable that zero variance is 1.
3. preprocess method as claimed in claim 2, which is characterized in that the calculation formula of the coefficient of kurtosis value of the component
For：
Wherein,
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110353672A (en) * | 2019-07-15 | 2019-10-22 | 西安邮电大学 | Eye artefact removal system and minimizing technology in a kind of EEG signals |
| CN112336355A (en) * | 2020-11-06 | 2021-02-09 | 广东电网有限责任公司电力科学研究院 | Safety supervision system, device and equipment based on electroencephalogram signal operating personnel |
| CN114176605A (en) * | 2021-12-16 | 2022-03-15 | 中国人民解放军火箭军工程大学 | Multi-channel electroencephalogram signal ocular artifact automatic removing method and storage medium |
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