CN106580320A - EEG signal feature analysis method and system based on visual inducement - Google Patents
EEG signal feature analysis method and system based on visual inducement Download PDFInfo
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- CN106580320A CN106580320A CN201611168668.2A CN201611168668A CN106580320A CN 106580320 A CN106580320 A CN 106580320A CN 201611168668 A CN201611168668 A CN 201611168668A CN 106580320 A CN106580320 A CN 106580320A
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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]
- A61B5/377—Electroencephalography [EEG] using evoked responses
- A61B5/378—Visual stimuli
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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
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
Abstract
The invention discloses an EEG signal feature analysis method and system based on visual inducement. The analysis method comprises the following steps: obtaining original EEG signal data evoked by different vision; by use of a time frequency analysis method, preprocessing the original EEG signal data to obtain preprocessed EEG signals; by use of a nonlinear theory, calculating nonlinear feature parameters of the preprocessed EEG signals; and according to the nonlinear feature parameters, analyzing the original EEG signal data. According to the invention, preprocessing signals are calculated by use of the nonlinear theory, analysis of original EEG signals can be more accurate, and the analysis precision of the original signals is improved.
Description
Technical field
Electroencephalogramsignal signal analyzing field of the present invention, more particularly to a kind of EEG signals feature analysiss side of view-based access control model induction
Method and system.
Background technology
EEG signals are brain electricity the spontaneous of cell mass, the corons of rhythmicity recorded by electrode, and it reflects
The electrical activity and the functional statuses of brain of cerebral tissue, used as a kind of noninvasive detection meanss, its research is related to EEG signals
At neuro physiology, psychology, pathophysiology, cognitive neuroscience, neural engineering or even social psychology, information and signal
The numerous areas such as reason.Research to brain wave feature may be used in the rehabilitation training of disease of brain patient.
In the prior art, it is that feature is extracted from time domain directly earliest to the research of brain wave feature, some important letters
Breath reflection in time domain is projected, but time domain approach prevents the brain electricity situation of the regional of brain from intuitively being reflected, power
The method of Power estimation is only capable of the amplitude of EEG signals to reflect with the situation of time change, using linear in prior art
Method processes EEG signals, relatively low to the Treatment Analysis precision of EEG signals.
The content of the invention
It is an object of the invention to provide a kind of precision it is higher view-based access control model induction EEG signals characteristic analysis method with
System.
For achieving the above object, the invention provides following scheme：
A kind of EEG signals characteristic analysis method of view-based access control model induction, the analysis method include：
Obtain different vision induced original EEG signals data；
The original EEG signals data are carried out with pretreatment using Time-Frequency Analysis Method, pretreatment EEG signals are obtained；
The nonlinear characteristic parameters of the pretreatment EEG signals are calculated using nonlinear theory；
The original EEG signals data are analyzed according to the nonlinear characteristic parameters.
Optionally, the method for the pretreatment specifically includes removal noise, and artefact is removed, at least one in multidimensional signal separation
Person.
Optionally, the nonlinear characteristic parameters are specifically included based on association digit, point correlation dimension, maximum Lyapunov
In index, approximate entropy at least one.
Optionally, the vision induced original EEG signals data of difference that obtain are specifically included by brain electrode cap to not
It is acquired with vision induced EEG signals data, obtains the original EEG signals data.
Optionally, described being analyzed to the original EEG signals data specifically includes：
The Changing Pattern of the nonlinear characteristic parameters is determined according to the nonlinear characteristic parameters；
Different vision induced EEG signals are classified and known according to the Changing Pattern of the nonlinear characteristic parameters
Not.
A kind of EEG signals characteristic analysis system of view-based access control model induction, the analysis system include data acquisition module,
Pretreatment module, computing module, analysis module；The data acquisition module is used to obtain different vision induced described original brains
Electrical signal data；The pretreatment module carries out pretreatment to the original EEG signals data using Time-Frequency Analysis Method, obtains
Obtain pretreatment EEG signals；The computing module calculates the nonlinear characteristic of the pretreatment EEG signals using nonlinear theory
Parameter；The analysis module is analyzed to the original EEG signals data according to the nonlinear characteristic parameters.
Optionally, the data acquisition module is also connected with brain electrode cap, and the brain electrode cap is used to gather original brain telecommunications
Number, the data acquisition module, for obtaining the original EEG signals data of the collection.
Optionally, the analysis module is specifically included：
Determining unit, for the Changing Pattern of the nonlinear characteristic parameters is determined according to the nonlinear characteristic parameters；
Analytic unit, for the Changing Pattern according to the nonlinear characteristic parameters to different vision induced EEG signals
Classified and recognized.
According to the specific embodiment that the present invention is provided, the invention discloses following technique effect：
The present invention carries out pretreatment using Time-Frequency Analysis Method to the original EEG signals data, obtains pretreatment brain electricity
Signal, calculates the nonlinear characteristic parameters of the pretreatment EEG signals using nonlinear theory, more accurately can analyze
Then the original EEG signals data are analyzed by EEG signals further according to nonlinear characteristic parameters, can be very accurate
Different vision induced signals are identified and classification.
Description of the drawings
In order to be illustrated more clearly that the embodiment of the present invention or technical scheme of the prior art, below will be to institute in embodiment
The accompanying drawing that needs are used is briefly described, it should be apparent that, drawings in the following description are only some enforcements of the present invention
Example, for those of ordinary skill in the art, on the premise of not paying creative work, can be being obtained according to these accompanying drawings
Obtain other accompanying drawings.
Fig. 1 is a kind of EEG signals characteristic analysis method flow chart of view-based access control model induction of the present invention；
Fig. 2 is a kind of EEG signals characteristic analysis system composition frame chart of view-based access control model induction of the present invention.
Specific embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation is described, it is clear that described embodiment is only a part of embodiment of the invention, rather than the embodiment of whole.It is based on
Embodiment in the present invention, it is every other that those of ordinary skill in the art are obtained under the premise of creative work is not made
Embodiment, belongs to the scope of protection of the invention.
It is an object of the invention to provide a kind of precision it is higher view-based access control model induction EEG signals characteristic analysis method with
System, improves the precision to electroencephalogramsignal signal analyzing.
It is understandable to enable the above objects, features and advantages of the present invention to become apparent from, it is below in conjunction with the accompanying drawings and concrete real
The present invention is further detailed explanation to apply mode.
As shown in figure 1, the EEG signals characteristic analysis method of view-based access control model of the present invention induction, the analysis method includes：
Step 100：Obtain different vision induced original EEG signals data.
Specifically, the vision induced original EEG signals data of difference that obtain include：By brain electrode cap to difference
Vision induced EEG signals data are acquired, and obtain the original EEG signals data；
Step 200：The original EEG signals data are carried out with pretreatment using Time-Frequency Analysis Method, pretreatment brain is obtained
The signal of telecommunication.
Wherein, the method for the pretreatment specifically includes removal noise, and artefact is removed, at least one in multidimensional signal separation
Person.By original EEG signals are carried out with pretreatment, reduce noise and environmental factorss and the nonlinear characteristic of EEG signals is joined
The impact that several calculating is produced.
Step 300：The nonlinear characteristic parameters of the pretreatment EEG signals are calculated using nonlinear theory.Wherein, institute
State nonlinear characteristic parameters to specifically include based on association digit, put in correlation dimension, maximum Lyapunov exponent, approximate entropy at least
One；
Step 400：The original EEG signals data are analyzed according to the nonlinear characteristic parameters.
Further, analysis process is specifically included：
Step 410：The Changing Pattern of the nonlinear characteristic parameters is determined according to the nonlinear characteristic parameters, can make
Must be more accurate to the analysis of EEG signals；
Step 420：Different vision induced EEG signals are carried out according to the Changing Pattern of the nonlinear characteristic parameters
Classification and identification.
The present invention carries out pretreatment using Time-Frequency Analysis Method to the original EEG signals data, obtains pretreatment brain electricity
Signal, calculates the nonlinear characteristic parameters of the pretreatment EEG signals using nonlinear theory, more accurately can analyze
Then the original EEG signals data are analyzed by EEG signals further according to nonlinear characteristic parameters, can be very accurate
Different vision induced signals are identified and classification.
Additionally, the present invention also provides a kind of EEG signals characteristic analysis system of view-based access control model induction.As shown in Fig. 2 this
The EEG signals characteristic analysis system of invention view-based access control model induction includes data acquisition module 1, pretreatment module 2, computing module
3, analysis module 4.
Wherein, the data acquisition module 1 be used to obtaining different vision induced described original EEG signals data also with
Brain electrode cap connects, and is connected with the data acquisition module 1, for obtaining the original EEG signals data；
The pretreatment module 2 carries out pretreatment to the original EEG signals data using Time-Frequency Analysis Method, obtains
Pretreatment EEG signals；
The computing module 3 calculates the nonlinear characteristic parameters of the pretreatment EEG signals using nonlinear theory；
The analysis module 4 is analyzed to the original EEG signals data according to the nonlinear characteristic parameters, tool
Body includes：
Determining unit, determines the Changing Pattern of the nonlinear characteristic parameters according to the nonlinear characteristic parameters；
Different vision induced EEG signals are carried out by analytic unit according to the Changing Pattern of the nonlinear characteristic parameters
Classification and identification.
In this specification, each embodiment is described by the way of progressive, and what each embodiment was stressed is and other
The difference of embodiment, between each embodiment identical similar portion mutually referring to.For system disclosed in embodiment
For, as which corresponds to the method disclosed in Example, so description is fairly simple, related part is said referring to method part
It is bright.
Specific embodiment used herein is set forth to the principle and embodiment of the present invention, above example
Illustrate that being only intended to help understands the method for the present invention and its core concept；Simultaneously for one of ordinary skill in the art, according to
According to the thought of the present invention, will change in specific embodiments and applications.In sum, this specification content
Should not be construed as limiting the invention.
Claims (8)
1. the EEG signals characteristic analysis method that a kind of view-based access control model induces, it is characterised in that the analysis method includes：
Obtain different vision induced original EEG signals data；
The original EEG signals data are carried out with pretreatment using Time-Frequency Analysis Method, pretreatment EEG signals are obtained；
The nonlinear characteristic parameters of the pretreatment EEG signals are calculated using nonlinear theory；
The original EEG signals data are analyzed according to the nonlinear characteristic parameters.
2. the EEG signals characteristic analysis method that a kind of view-based access control model according to claim 1 induces, it is characterised in that institute
The method for stating pretreatment specifically includes removal noise, and artefact is removed, multidimensional signal separate at least one.
3. the EEG signals characteristic analysis method that a kind of view-based access control model according to claim 1 induces, it is characterised in that institute
State nonlinear characteristic parameters to specifically include based on correlation dimension, put in correlation dimension, maximum Lyapunov exponent, approximate entropy at least
One.
4. the EEG signals characteristic analysis method that a kind of view-based access control model according to claim 1 induces, it is characterised in that institute
State the different vision induced original EEG signals data of acquisition to specifically include by brain electrode cap to different vision induced brains electricity
Signal data is acquired, and obtains the original EEG signals data.
5. the EEG signals characteristic analysis method that a kind of view-based access control model according to claim 1 induces, it is characterised in that institute
State to be analyzed the original EEG signals data and specifically include：
The Changing Pattern of the nonlinear characteristic parameters is determined according to the nonlinear characteristic parameters；
Different vision induced EEG signals are classified and recognized according to the Changing Pattern of the nonlinear characteristic parameters.
6. the EEG signals characteristic analysis system that a kind of view-based access control model induces, it is characterised in that the analysis system includes data
Acquisition module, pretreatment module, computing module, analysis module；
The data acquisition module is used to obtain different vision induced described original EEG signals data；
The pretreatment module carries out pretreatment using Time-Frequency Analysis Method to the original EEG signals data, obtains pretreatment
EEG signals；
The computing module calculates the nonlinear characteristic parameters of the pretreatment EEG signals using nonlinear theory；
The analysis module is analyzed to the original EEG signals data according to the nonlinear characteristic parameters.
7. the EEG signals characteristic analysis system that a kind of view-based access control model according to claim 6 induces, it is characterised in that institute
State data acquisition module and be also connected with brain electrode cap, the brain electrode cap is used to gather original EEG signals, the data acquisition mould
Block is used for the original EEG signals data for obtaining the collection.
8. the EEG signals characteristic analysis system that a kind of view-based access control model according to claim 6 induces, it is characterised in that institute
State analysis module to specifically include：
Determining unit, for the Changing Pattern of the nonlinear characteristic parameters is determined according to the nonlinear characteristic parameters；
Different vision induced EEG signals are carried out by analytic unit for the Changing Pattern according to the nonlinear characteristic parameters
Classification and identification.
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| CN110613446A (en) * | 2019-09-30 | 2019-12-27 | 京东方科技集团股份有限公司 | Signal processing method and device |
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