CN106491125A - Electroencephalogram state identification method and device - Google Patents
Electroencephalogram state identification method and device Download PDFInfo
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- 238000010276 construction Methods 0.000 claims description 3
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- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
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Abstract
The invention discloses an electroencephalogram state identification method, which comprises the following steps: acquiring an electroencephalogram recognition model corresponding to a user; collecting electroencephalogram signals of the user; extracting a characteristic value from the electroencephalogram signal; and analyzing the characteristic value by adopting the electroencephalogram recognition model, and recognizing the state type of the electroencephalogram signal. Correspondingly, the invention also discloses an electroencephalogram state identification device. By adopting the embodiment of the invention, the accuracy of electroencephalogram state identification can be improved.
Description
Technical field
The present invention relates to brain electro-technical field, more particularly to a kind of recognition methodss of brain electricity condition and device.
Background technology
Only include brain wave acquisition part in the product of biofeedback training common in the market, not comprising electroencephalogramrecognition recognition
Function so as to lack purposiveness in feedback, lacks data supporting to the assessment of brain states.And minority has electroencephalogramrecognition recognition mould
The product of block is identified to different user using identical electroencephalogramrecognition recognition model.But, brain electricity individual difference is very big, phase
Performance difference of the same electroencephalogramrecognition recognition model on individuality is huge, and the accuracy rate so as to cause electroencephalogramrecognition recognition is low.
Content of the invention
The embodiment of the present invention proposes a kind of recognition methodss of brain electricity condition and device, it is possible to increase it is accurate that brain electricity condition is recognized
Property.
The embodiment of the present invention provides a kind of brain electricity condition recognition methodss, including：
Obtain the electroencephalogramrecognition recognition model corresponding to user；
Gather the EEG signals of the user；
Eigenvalue is extracted from the EEG signals；
The eigenvalue is analyzed using the electroencephalogramrecognition recognition model, identifies the EEG signals state in which
Type.
It is preferably carried out in mode at one, the electroencephalogramrecognition recognition model obtained corresponding to user is specifically included：
The electroencephalogramrecognition recognition model corresponding to the user is obtained from the model library for pre-building.
It is preferably carried out in mode at another, the electroencephalogramrecognition recognition model obtained corresponding to user is specifically included：
Gather user's brain in different conditions when EEG signals sample；
Eigenvalue is extracted respectively in EEG signals sample under each state；
Eigenvalue under each state described is trained, the electroencephalogramrecognition recognition model corresponding to the user is built.
Further, described extract eigenvalue from the EEG signals, specifically include：
The EEG signals are converted to frequency-region signal by time-domain signal, brain electricity frequency-region signal is obtained；
Obtain the electrical energy of brain of each frequency in the brain electricity frequency-region signal；
The difference of the electrical energy of brain of each window in electrical energy of brain and its top n window of current window is calculated respectively,
Obtain the first energy differences；Wherein, the current window is the time period between current time and its front M moment；Wherein, N
>=1, M >=1；
The electrical energy of brain for calculating current window and the user for obtaining in advance are in average electrical energy of brain during relaxation state
Difference, obtains the second energy differences；
Using the electrical energy of brain of each frequency, first energy differences and second energy differences as the brain
The eigenvalue of the signal of telecommunication.
Further, described extract eigenvalue from the EEG signals before, also include：
The EEG signals for collecting are filtered according to default frequency range.
Correspondingly, the embodiment of the present invention also provides a kind of brain electricity condition identifying device, including：
Acquisition module, for obtaining the electroencephalogramrecognition recognition model corresponding to user；
Acquisition module, for gathering the EEG signals of the user；
Extraction module, for extracting eigenvalue from the EEG signals；And,
Identification module, for being analyzed to the eigenvalue using the electroencephalogramrecognition recognition model, identifies the brain electricity
Signal state in which type.
It is preferably carried out in mode at one, the acquisition module is specifically included：
Model acquiring unit, for obtaining the electroencephalogramrecognition recognition mould corresponding to the user from the model library for pre-building
Type.
It is preferably carried out in mode at another, the acquisition module is specifically included：
Sample collection unit, for gather user's brain in different conditions when EEG signals sample；
Extraction unit, for extracting eigenvalue in EEG signals sample respectively under each state；And,
Model construction unit, for being trained, builds the brain electricity of the user to the eigenvalue under each state described
Identification model.
Further, the extraction module is specifically included：
Converting unit, for the EEG signals are converted to frequency-region signal by time-domain signal, obtains brain electricity frequency-region signal；
Electrical energy of brain acquiring unit, for obtaining the electrical energy of brain of each frequency in the brain electricity frequency-region signal；
First energy differences acquiring unit, for calculating in electrical energy of brain and its top n window of current window respectively
The difference of the electrical energy of brain of each window, obtains the first energy differences；Wherein, the current window is current time and its front M
Time period between the individual moment；Wherein, N >=1, M >=1；
Second energy differences acquiring unit, for calculating the electrical energy of brain of current window and the user for obtaining in advance in putting
The difference of average electrical energy of brain during loose state, obtains the second energy differences；And,
Eigenvalue acquiring unit, for by the electrical energy of brain of each frequency, first energy differences and described
Eigenvalue of two energy differences as the EEG signals.
Further, the brain electricity condition identifying device also includes：
Filtering module, for filtering to the EEG signals for collecting according to default frequency range.
Implement the embodiment of the present invention, have the advantages that：
Brain electricity condition provided in an embodiment of the present invention recognition methodss and device, can obtain the electroencephalogramrecognition recognition mould of user itself
Type, and then the electroencephalogramrecognition recognition model using user itself is analyzed to the EEG signals of the user for collecting, and identifies user
EEG signals state in which type, so as to avoid the difference between individuality, improves the accuracy of brain electricity condition identification.
Description of the drawings
Fig. 1 is the schematic flow sheet of the one embodiment for the brain electricity condition recognition methodss that the present invention is provided；
Fig. 2 is the structural representation of the one embodiment for the brain electricity condition identifying device that the present invention is provided.
Specific embodiment
Accompanying drawing in below in conjunction with the embodiment of the present invention, to the embodiment of the present invention in technical scheme carry out clear, complete
Site preparation is described, it is clear that described embodiment is only a part of embodiment of the invention, rather than whole embodiment.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.
The schematic flow sheet of the one embodiment for the brain electricity condition recognition methodss provided referring to Fig. 1, the present invention, including
Electroencephalogramrecognition recognition model corresponding to S1, acquisition user；
S2, the EEG signals for gathering the user；
S3, from the EEG signals, extract eigenvalue；
S4, the eigenvalue is analyzed using the electroencephalogramrecognition recognition model, is identified residing for the EEG signals
Status Type.
It should be noted that each user is respectively provided with the electroencephalogramrecognition recognition model corresponding to which, to user's brain electricity shape
When state is identified, the electroencephalogramrecognition recognition model corresponding to the user is first obtained.After the EEG signals for collecting user, brain is extracted
Eigenvalue in the signal of telecommunication, and the eigenvalue for extracting is analyzed using the electroencephalogramrecognition recognition model corresponding to the user, recognize
User's brain electricity state in which type.Wherein, Status Type includes relaxation state, uses brain state etc..Brain electricity using user
Identification model is identified to the brain electricity condition of user itself, can avoid the difference between user's individuality, improves user's brain electricity
The accuracy of state recognition.The basis that accurately classification is biofeedback training is carried out to user's brain electricity condition, can be by the present invention
The brain electricity condition recognition methodss provided by embodiment apply to biofeedback training, biofeedback therapy, automatic hypnosis and set
In the training such as standby, attention training equipment, Emotion recognition training, memory training, make that training is more purposive, specific aim.
It is preferably carried out in mode at one, the electroencephalogramrecognition recognition model obtained corresponding to user is specifically included：
The electroencephalogramrecognition recognition model corresponding to the user is obtained from the model library for pre-building.
It should be noted that the electroencephalogramrecognition recognition model of user can pre-build and be stored in model library, needing to this
When the brain electricity condition of user is identified, the electroencephalogramrecognition recognition model for obtaining the user from model library is identified.
It is preferably carried out in mode at another, the electroencephalogramrecognition recognition model obtained corresponding to user is specifically included：
Gather user's brain in different conditions when EEG signals sample；
Eigenvalue is extracted respectively in EEG signals sample under each state；
Eigenvalue under each state described is trained, the electroencephalogramrecognition recognition model corresponding to the user is built.
It should be noted that the electroencephalogramrecognition recognition model of user can be also obtained by way of " on-line study ".Use in collection
Before the brain electric model at family is identified, EEG signals when first collection user brain is in different conditions are come " online as sample
Study " goes out the electroencephalogramrecognition recognition model of the user.When the EEG signals sample of user is gathered, user completes to make according to program prompting
Determine action, record all of eeg data of user in user's implementation procedure, and according to the time period to user eeg data
Tagged, wherein, the time period could be arranged to 1 second.In a relaxed state, it is desirable to which user closes eyes, deeply breathe, and as far as possible
Loosen body and brain, continue 60 seconds or the longer time, the eeg data sample that this process is collected is labeled as " loosening "；
With under brain state, it is desirable to which user to open eyes and read news or article by mobile phone or computer screen, requires to use during reading
Family is tried one's best diligently, content can be repeated after reading, continues 60 seconds or the longer time, the brain electricity that this process is collected
Data sample is labeled as " using brain ".
After EEG signals sample when user's brain is acquired in different conditions, EEG signals sample is filtered
Denoising, such as baseline drift, Hz noise, bandpass filtering etc..EEG signals sample is passed through wavelet transformation, transform, Fourier again
The forms such as conversion are converted to frequency domain by time domain, and then arrange the eigenvalue that time window is extracted in EEG signals sample, and will carry
The eigenvalue of taking-up is sent in grader and is trained.Wherein, EEG signals have a non-linear stochastic feature, all meet non-linear
The grader of feature all can play reasonable classifying quality, such as SVM, decision tree, neutral net, Bayes to above-mentioned classification
Model etc..In the training process, the knowledge of electroencephalogramrecognition recognition model is determined by observing cross validation (CrossValidation) result
Other effect.If the result is undesirable, filtering and the partial parameters in characteristics extraction can be changed, such as change wavelet basiss, when
Between window size, window difference number etc., till the result is satisfied with.
Further, described extract eigenvalue from the EEG signals, specifically include：
The EEG signals are converted to frequency-region signal by time-domain signal, brain electricity frequency-region signal is obtained；
Obtain the electrical energy of brain of each frequency in the brain electricity frequency-region signal；
The difference of the electrical energy of brain of each window in electrical energy of brain and its top n window of current window is calculated respectively,
Obtain the first energy differences；Wherein, the current window is the time period between current time and its front M moment；Wherein, N
>=1, M >=1；
The electrical energy of brain for calculating current window and the user for obtaining in advance are in average electrical energy of brain during relaxation state
Difference, obtains the second energy differences；
Using the electrical energy of brain of each frequency, first energy differences and second energy differences as the brain
The eigenvalue of the signal of telecommunication.
It should be noted that after the EEG signals for collecting user, the data for collecting are become by wavelet transformation, Z
Change, Fourier transformation is changed etc., and form is converted to frequency-region signal, obtain brain electricity frequency-region signal, and then extract from brain electricity frequency-region signal
Go out eigenvalue.In the extraction of eigenvalue, obtain brain electricity frequency-region signal in each frequency domain electrical energy of brain, current window and its
First energy differences of each window in top n window, the second energy differences of current window.Wherein, the big I of window
Arranged according to the label of labelling during eeg signal acquisition, while window is elapsed over time sliding backward.In addition, user is in
Average electrical energy of brain during relaxation state is collection user's EEG signals sample in a relaxed state, and by the brain under the state
Signal of telecommunication sample is converted to the meansigma methodss of the electrical energy of brain of all frequencies for calculating acquisition after frequency domain by time domain.
Further, described extract eigenvalue from the EEG signals before, also include：
The EEG signals for collecting are filtered according to default frequency range.
It should be noted that the EEG signals for collecting are real-time voltage data, need comprising substantial amounts of noise in the data
Can just use after filtering.Wherein, the mode of filtering includes that baseline drift, Hz noise, bandpass filtering are interested to obtain
Data.
Brain electricity condition recognition methodss provided in an embodiment of the present invention, can obtain the electroencephalogramrecognition recognition model of user itself, enter
And the EEG signals of the user for collecting are analyzed using the electroencephalogramrecognition recognition model of user itself, identify user's brain telecommunications
Number state in which type, so as to avoid the difference between individuality, improves the accuracy of brain electricity condition identification.
Accordingly, the present invention also provides a kind of brain electricity condition identifying device, can realize the brain electricity shape in above-described embodiment
All flow processs of state recognition methodss.
Referring to Fig. 2, it is the structural representation of the one embodiment for the brain electricity condition identifying device that the present invention is provided, including：
Acquisition module 1, for obtaining the electroencephalogramrecognition recognition model corresponding to user；
Acquisition module 2, for gathering the EEG signals of the user；
Extraction module 3, for extracting eigenvalue from the EEG signals；And,
Identification module 4, for being analyzed to the eigenvalue using the electroencephalogramrecognition recognition model, identifies the brain electricity
Signal state in which type.
It is preferably carried out in mode at one, the acquisition module is specifically included：
Model acquiring unit, for obtaining the electroencephalogramrecognition recognition mould corresponding to the user from the model library for pre-building
Type.
It is preferably carried out in mode at another, the acquisition module is specifically included：
Sample collection unit, for gather user's brain in different conditions when EEG signals sample；
Extraction unit, for extracting eigenvalue in EEG signals sample respectively under each state；And,
Model construction unit, for being trained, builds the brain electricity of the user to the eigenvalue under each state described
Identification model.
Further, the extraction module is specifically included：
Converting unit, for the EEG signals are converted to frequency-region signal by time-domain signal, obtains brain electricity frequency-region signal；
Electrical energy of brain acquiring unit, for obtaining the electrical energy of brain of each frequency in the brain electricity frequency-region signal；
First energy differences acquiring unit, for calculating in electrical energy of brain and its top n window of current window respectively
The difference of the electrical energy of brain of each window, obtains the first energy differences；Wherein, the current window is current time and its front M
Time period between the individual moment；Wherein, N >=1, M >=1；
Second energy differences acquiring unit, for calculating the electrical energy of brain of current window and the user for obtaining in advance in putting
The difference of average electrical energy of brain during loose state, obtains the second energy differences；And,
Eigenvalue acquiring unit, for by the electrical energy of brain of each frequency, first energy differences and described
Eigenvalue of two energy differences as the EEG signals.
Further, the brain electricity condition identifying device also includes：
Filtering module, for filtering to the EEG signals for collecting according to default frequency range.
Brain electricity condition identifying device provided in an embodiment of the present invention, can obtain the electroencephalogramrecognition recognition model of user itself, enter
And the EEG signals of the user for collecting are analyzed using the electroencephalogramrecognition recognition model of user itself, identify user's brain telecommunications
Number state in which type, so as to avoid the difference between individuality, improves the accuracy of brain electricity condition identification.
The above is the preferred embodiment of the present invention, it is noted that for those skilled in the art
For, under the premise without departing from the principles of the invention, some improvements and modifications can also be made, these improvements and modifications are also considered as
Protection scope of the present invention.
Claims (10)
1. a kind of brain electricity condition recognition methodss, it is characterised in that include：
Obtain the electroencephalogramrecognition recognition model corresponding to user；
Gather the EEG signals of the user；
Eigenvalue is extracted from the EEG signals；
The eigenvalue is analyzed using the electroencephalogramrecognition recognition model, identifies the EEG signals state in which class
Type.
2. brain electricity condition recognition methodss as claimed in claim 1, it is characterised in that the brain electricity corresponding to the acquisition user is known
Other model, specifically includes：
The electroencephalogramrecognition recognition model corresponding to the user is obtained from the model library for pre-building.
3. brain electricity condition recognition methodss as claimed in claim 1, it is characterised in that the brain electricity corresponding to the acquisition user is known
Other model, specifically includes：
Gather user's brain in different conditions when EEG signals sample；
Eigenvalue is extracted respectively in EEG signals sample under each state；
Eigenvalue under each state described is trained, the electroencephalogramrecognition recognition model corresponding to the user is built.
4. brain electricity condition recognition methodss as claimed in claim 1, it is characterised in that described extract from the EEG signals
Eigenvalue, specifically includes：
The EEG signals are converted to frequency-region signal by time-domain signal, brain electricity frequency-region signal is obtained；
Obtain the electrical energy of brain of each frequency in the brain electricity frequency-region signal；
The difference of the electrical energy of brain of each window in the electrical energy of brain and its top n window of calculating current window, obtains respectively
First energy differences；Wherein, the current window is the time period between current time and its front M moment；Wherein, N >=1, M
≥1；
Calculate current window electrical energy of brain and the user for obtaining in advance be in relaxation state when average electrical energy of brain difference,
Obtain the second energy differences；
Using the electrical energy of brain of each frequency, first energy differences and second energy differences as the brain telecommunications
Number eigenvalue.
5. the brain electricity condition recognition methodss as described in any one of Claims 1-4, it is characterised in that described from brain electricity
Before eigenvalue is extracted in signal, also include：
The EEG signals for collecting are filtered according to default frequency range.
6. a kind of brain electricity condition identifying device, it is characterised in that include：
Acquisition module, for obtaining the electroencephalogramrecognition recognition model corresponding to user；
Acquisition module, for gathering the EEG signals of the user；
Extraction module, for extracting eigenvalue from the EEG signals；And,
Identification module, for being analyzed to the eigenvalue using the electroencephalogramrecognition recognition model, identifies the EEG signals
State in which type.
7. brain electricity condition identifying device as claimed in claim 6, it is characterised in that the acquisition module is specifically included：
Model acquiring unit, for obtaining the electroencephalogramrecognition recognition model corresponding to the user from the model library for pre-building.
8. brain electricity condition identifying device as claimed in claim 6, it is characterised in that the acquisition module is specifically included：
Sample collection unit, for gather user's brain in different conditions when EEG signals sample；
Extraction unit, for extracting eigenvalue in EEG signals sample respectively under each state；And,
Model construction unit, for being trained, builds the electroencephalogramrecognition recognition of the user to the eigenvalue under each state described
Model.
9. brain electricity condition identifying device as claimed in claim 6, it is characterised in that the extraction module is specifically included：
Converting unit, for the EEG signals are converted to frequency-region signal by time-domain signal, obtains brain electricity frequency-region signal；
Electrical energy of brain acquiring unit, for obtaining the electrical energy of brain of each frequency in the brain electricity frequency-region signal；
First energy differences acquiring unit, for each in the electrical energy of brain and its top n window of calculating current window respectively
The difference of the electrical energy of brain of window, obtains the first energy differences；Wherein, when the current window is current time and its first M
Time period between quarter；Wherein, N >=1, M >=1；
Second energy differences acquiring unit, loosens shape for calculating the electrical energy of brain of current window and being in the user for obtaining in advance
The difference of average electrical energy of brain during state, obtains the second energy differences；And,
Eigenvalue acquiring unit, for by the electrical energy of brain of each frequency, first energy differences and second energy
Eigenvalue of the amount difference as the EEG signals.
10. the brain electricity condition identifying device as described in any one of claim 6 to 9, it is characterised in that the brain electricity condition identification
Device also includes：
Filtering module, for filtering to the EEG signals for collecting according to default frequency range.
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| CN108852348A (en) * | 2018-05-14 | 2018-11-23 | 中国人民解放军军事科学院军事医学研究院 | The collection point sort method and system of scalp brain electricity |
| CN111035459A (en) * | 2020-01-03 | 2020-04-21 | 新疆医科大学第一附属医院 | Neurosurgical head support positioning control system and method |
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