CN100998503A - Method for automatically recogniting and eliminating ophthalmogyric interference in electroencephalo-signals - Google Patents
Method for automatically recogniting and eliminating ophthalmogyric interference in electroencephalo-signals Download PDFInfo
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- CN100998503A CN100998503A CNA2006101709631A CN200610170963A CN100998503A CN 100998503 A CN100998503 A CN 100998503A CN A2006101709631 A CNA2006101709631 A CN A2006101709631A CN 200610170963 A CN200610170963 A CN 200610170963A CN 100998503 A CN100998503 A CN 100998503A
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Abstract
A method for automatically recognizing and eliminating the eye moving interference from electroencephalogram (EEG) includes such steps as extracting the components from EEG by ICA, dipole locating analyzing, creating a bipole position distribution model about eye moving interference, and ICA reverse transformation to automatically eliminate said interference.
Description
Technical field
The present invention relates to the processing of biomedical signals technology, be specifically related to identification automatically and the method for eliminating eye movement interference in the EEG signals.
Background technology
Current constantly have new achievement in research to emerge in large numbers in the EEG Processing technical field both at home and abroad, and these achievements are in medical research and bringing into play important effect clinically.Clinical brain electricity (EEG) is the cranial nerve cell group's that notes by electrode spontaneity, the electrical activity of rhythmicity, has become the diagnosis of clinical medicine brain diseases and has observed the conventional means and the strong instrument of brain function.EEG research is very active at present, is clinical EEG research on the one hand, and it has great importance to identification, morbid state forecast and the diagnosis and treatment of brain diseases; Be based on the brain function and the cognitive research of EEG signals on the other hand, and some new research branches occurred, as brain-machine interface (BCI) and thinking EEG research etc.All these, all analysis and the feature extraction to EEG signals proposed requirements at the higher level.Because it is non-stationary that the brain electricity has, and very faint, very easily is subjected to the interference as ocular movement, nictation during measurement, it can have a strong impact on the interpretation and the identification of brain electricity, and the feature extraction of ill brain electricity and diagnosis.
For the eye movement interference in the brain electricity, adopt the method for classical filtering to be removed usually, because that the frequency range of interferential frequency range and EEG signals exists is overlapping, therefore in the filtering interfering composition, usually the electric composition of brain is filtered together.Remove eye movement interference and also have methods such as Regression, PCA at present.Homing method needs to write down eye electricity (EOG) as a reference simultaneously, owing to can contain the electric composition of brain in the eye electricity that is write down, therefore when eliminating eye movement interference, can remove some brains electricity compositions inevitably; The PCA method is orthogonal component with signal decomposition, and its weak point is to realize separating fully of the close eye movement interference of amplitude and brain electricity composition, and interference and brain electricity might not have orthogonality relation.By independent component analysis (Independent Component Analysis, ICA) though EEG signals and eye movement interference component separating can be come, but how to discern brain electricity slow wave and eye movement interference, usually need doctor's artificial judgment, cause denoising work very time-consuming objective, fail to realize that automatization handles with shortage.
To sum up analyze, the method for eliminating eye movement interference automatically and meeting clinical needs, significant to brain electricity analytical, diagnosis and brain function research.
Summary of the invention
At the deficiencies in the prior art, the present invention proposes a kind of automatic identification and the method for eliminating eye movement interference in the EEG signals, this method can be eliminated eye movement interference in the EEG signals automatically.
Summary of the invention
Utilize computer and relevant device, based on the various compositions in independent component analysis (ICA) separation and the extraction brain electricity, it is carried out dipole localization (Dipole localization) analysis, set up the dipole position distributed model of eye movement interference, and know eye movement interference in view of the above automatically, and then, realize the automatic elimination of eye movement interference in the brain electricity by the ICA inverse transformation.On basis of the present invention, can further realize the automatic extraction of brain electrical feature and identification, brain diseases automatic diagnosis, promote brain function research aspect technical development.
Detailed Description Of The Invention
A kind of automatic identification of the present invention and the method for eliminating eye movement interference in the EEG signals, step is as follows:
1, start computer,
2, gather eeg data from scalp,
3, employing independent component analysis (ICA) algorithm separates each independent element in the EEG signals,
4, to each independent element by separating inverse problem, carry out source location,
5, according to the position distribution of each source at head, the identification eye movement interference,
6, by independent component analysis (ICA) inverse operation, realize the automatic de-noising of EEG signals,
7, EEG signals output.
8, finish.
Above-mentioned steps 2 is gathered eeg data from scalp, can select existing brain wave acquisition multiplying arrangement for use, by the A/D conversion signal is imported computer.
The mixed model of signal is in above-mentioned steps 3 described independent component analysis (ICA) algorithms: x (t)=As (t), wherein s (t)=[s
1(t) ..., s
m(t)]
TBe source signal independent of each other, A is a hybrid matrix, x (t)=[x
1(t) ..., x
n(t)]
TBe the EEG signals of gathering, and source signal s (t) and all the unknowns of hybrid system A.ICA is output as: s (t)=Wx (t), wherein W is for separating mixed matrix, and A=W
-1Through ICA EEG signals x (t) is separated into separately independently ingredient s (t).
Above-mentioned steps 4 specifically, can get according to ICA linear hybrid relation, each row of hybrid matrix A have reflected the energy mapping situation of this independent element in the scalp electrode position, utilization in the electroencephalography (eeg) inverse problem single dipole the method location, determine each composition position of pairing dipole in brain one by one.
The pairing composition of dipole that above-mentioned steps 5 preferably will be positioned at eye socket and eye socket place is identified as the eye movement interference composition.
Above-mentioned steps 6 is preferred with the interference component zero setting that identifies, and again by the ICA inverse operation, realizes the automatic de-noising of EEG signals.
Excellent results of the present invention is:
1, need not doctor's operation in the eye movement interference process in eliminating EEG signals, realize that fully automatization handles.
2, the interferential while of the present invention in eliminating the brain electricity, can also obtain the position distribution of brain electric dipole in brain, can be used for studying and observing the spatial characteristics of brain electrical acti, in the diagnosis of diseases such as epilepsy and brain function positioning analysis, can play a significant role.
Description of drawings
Fig. 1 is the main-process stream block diagram of the inventive method.
Fig. 2 is the clinical EEG signals of gathering.
Fig. 3 is the EEG signals of eliminating automatically after the eye movement interference.
The specific embodiment
The present invention will be further described below in conjunction with embodiment, but be not limited thereto.
Embodiment:
Automatically identification and the method for eliminating eye movement interference in the EEG signals, step is as follows:
1) start computer,
2) use the brain wave acquisition multiplying arrangement, gather the EEG signals of noting by scalp electrode, by A/D conversion input computer, electrode is placed the international 10-20 of employing system,
3) adopt the mixed model of signal in the independent component analysis algorithm to be: x (t)=As (t), wherein s (t)=[s
1(t) ..., s
m(t)]
TBe source signal independent of each other, A is a hybrid matrix, x (t)=[x
1(t) ..., x
n(t)]
TBe the EEG signals of gathering, and source signal s (t) and all the unknowns of hybrid system A.ICA is output as: s (t)=Wx (t), wherein W is for separating mixed matrix, by general JADE algorithm computation.Hybrid matrix A obtains by asking the contrary of W.Through ICA EEG signals x (t) is separated into separately independently ingredient s (t),
4) can get according to ICA linear hybrid relation, each row of hybrid matrix A have reflected the energy mapping situation of this independent element in the scalp electrode position, utilization in the electroencephalography (eeg) inverse problem single dipole the method location, adopt four layers of homocentric sphere model, its radius is taken as 71mm successively, 72mm, 79mm and 85mm, head part is slit into 4 parts: brain, cerebrospinal fluid, skull and scalp, its coefficient of conductivity is respectively 0.33s/m, 1.00s/m, 0.0042s/m and 0.33s/m, determine each composition position of pairing dipole in brain one by one
5) the pairing composition of dipole that is positioned at eye socket and eye socket place is identified as the eye movement interference composition,
6) the interference component zero setting that identifies by the ICA inverse operation, realizes the automatic de-noising of EEG signals,
7) by the EEG signals after printer or the display output denoising.As shown in Figure 3.
8) finish.
Claims (6)
1, a kind of automatic identification and the method for eliminating eye movement interference in the EEG signals, step is as follows:
(1) start computer,
(2) gather eeg data from scalp,
(3) employing independent component analysis algorithm separates each independent element in the EEG signals,
(4) to each independent element by separating inverse problem, carry out source location,
(5) according to the position distribution of each source at head, the identification eye movement interference,
(6) by the independent component analysis inverse operation, realize the automatic de-noising of EEG signals,
(7) EEG signals output,
(8) finish.
2, automatic identification as claimed in claim 1 and the method for eliminating eye movement interference in the EEG signals is characterized in that, described step 2 is gathered eeg data from scalp, uses the brain wave acquisition multiplying arrangement, by A/D conversion input computer, signal input computer.
3, automatic identification as claimed in claim 1 and the method for eliminating eye movement interference in the EEG signals is characterized in that the mixed model of signal is in the described independent component analysis algorithm of step (3): x (t)=As (t), wherein s (t)=[s
1(t) ..., s
m(t)]
TBe source signal independent of each other, A is a hybrid matrix, x (t)=[x
1(t) ..., x
n(t)]
TThe EEG signals of be gathering, and source signal s (t) and all the unknowns of hybrid system A, ICA is output as: s (t)=Wx (t), wherein W is for separating mixed matrix, and A=W
-1, EEG signals x (t) is separated into separately independently ingredient s (t) through ICA.
4, automatic identification as claimed in claim 1 and the method for eliminating eye movement interference in the EEG signals, it is characterized in that, can get according to ICA linear hybrid relation during step (4) concrete operations, each row of hybrid matrix A have reflected the energy mapping situation of this independent element in the scalp electrode position, utilization in the electroencephalography (eeg) inverse problem single dipole the method location, determine each composition position of pairing dipole in brain one by one.
5, automatic identification as claimed in claim 1 and the method for eliminating eye movement interference in the EEG signals is characterized in that described step (5) is that the pairing composition of dipole that will be positioned at eye socket and eye socket place is identified as the eye movement interference composition.
6, automatic identification as claimed in claim 1 and the method for eliminating eye movement interference in the EEG signals is characterized in that described step (6) is the interference component zero setting that will identify, and again by the ICA inverse operation, realize the automatic de-noising of EEG signals.
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101966080A (en) * | 2010-10-26 | 2011-02-09 | 东北大学 | Portable active electroencephalogram monitor and control method thereof |
| US8112147B2 (en) | 2007-12-04 | 2012-02-07 | Shenzhen Mindray Bio-Medical Electronics Co., Ltd. | Method and apparatus for generating determination indexes for identifying ECG interfering signals |
| CN102525447A (en) * | 2010-12-31 | 2012-07-04 | 财团法人交大思源基金会 | Physiological signal map analyzing system and method, and map establishing method |
| CN103654773A (en) * | 2013-12-20 | 2014-03-26 | 北京飞宇星电子科技有限公司 | Brain electrical physiological experiment teaching device |
| CN105094324A (en) * | 2015-07-14 | 2015-11-25 | 南京航空航天大学 | Brain state recognition method based on electroencephalogram generated from left and right hand motor imagery |
| CN105640500A (en) * | 2015-12-21 | 2016-06-08 | 安徽大学 | Scanning signal feature extraction method based on independent component analysis and recognition method |
| CN106859640A (en) * | 2017-01-24 | 2017-06-20 | 东莞见达信息技术有限公司 | A kind of EEG measuring device and method based on independent component analysis |
| CN108836321A (en) * | 2018-05-03 | 2018-11-20 | 江苏师范大学 | A kind of EEG signals preprocess method based on adaptive noise cancel- ation system |
| CN113057655A (en) * | 2020-12-29 | 2021-07-02 | 深圳迈瑞生物医疗电子股份有限公司 | Recognition method, recognition system and detection system for electroencephalogram signal interference |
| CN113598794A (en) * | 2021-08-12 | 2021-11-05 | 中南民族大学 | Training method and system for detection model of ice drug addict |
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2006
- 2006-12-28 CN CNA2006101709631A patent/CN100998503A/en active Pending
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8112147B2 (en) | 2007-12-04 | 2012-02-07 | Shenzhen Mindray Bio-Medical Electronics Co., Ltd. | Method and apparatus for generating determination indexes for identifying ECG interfering signals |
| CN101966080A (en) * | 2010-10-26 | 2011-02-09 | 东北大学 | Portable active electroencephalogram monitor and control method thereof |
| CN101966080B (en) * | 2010-10-26 | 2012-06-20 | 东北大学 | Portable active electroencephalogram monitor and control method thereof |
| CN102525447A (en) * | 2010-12-31 | 2012-07-04 | 财团法人交大思源基金会 | Physiological signal map analyzing system and method, and map establishing method |
| CN102525447B (en) * | 2010-12-31 | 2014-10-22 | 财团法人交大思源基金会 | Physiological signal map analyzing system and method, and map establishing method |
| CN103654773A (en) * | 2013-12-20 | 2014-03-26 | 北京飞宇星电子科技有限公司 | Brain electrical physiological experiment teaching device |
| CN105094324A (en) * | 2015-07-14 | 2015-11-25 | 南京航空航天大学 | Brain state recognition method based on electroencephalogram generated from left and right hand motor imagery |
| CN105094324B (en) * | 2015-07-14 | 2018-02-23 | 南京航空航天大学 | Brain states recognition methods based on right-hand man's Mental imagery EEG signals |
| CN105640500A (en) * | 2015-12-21 | 2016-06-08 | 安徽大学 | Scanning signal feature extraction method based on independent component analysis and recognition method |
| CN106859640A (en) * | 2017-01-24 | 2017-06-20 | 东莞见达信息技术有限公司 | A kind of EEG measuring device and method based on independent component analysis |
| CN108836321A (en) * | 2018-05-03 | 2018-11-20 | 江苏师范大学 | A kind of EEG signals preprocess method based on adaptive noise cancel- ation system |
| CN113057655A (en) * | 2020-12-29 | 2021-07-02 | 深圳迈瑞生物医疗电子股份有限公司 | Recognition method, recognition system and detection system for electroencephalogram signal interference |
| CN113598794A (en) * | 2021-08-12 | 2021-11-05 | 中南民族大学 | Training method and system for detection model of ice drug addict |
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