CN107024987A - A kind of real-time human brain Test of attention and training system based on EEG - Google Patents
A kind of real-time human brain Test of attention and training system based on EEG Download PDFInfo
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Abstract
The invention discloses a kind of real-time human brain Test of attention and training system based on EEG, system includes notice experiment, signal acquisition, data analysis, five parts of real-time Transmission and test feedback, notice experimental section is divided into internal system and outside experiment, and signal acquisition part collects the EEG data of user using brain wave acquisition equipment；Data analysis component carries out denoising, filtering and the analysis of related rhythm and pace of moving things ripple to the signal gathered using data analysis program；Real-time Transmission part will analyze obtained quantized values preservation in case extracting at any time, and transmitted by corresponding interface, and test feedback fraction reads the data of real-time Transmission part using corresponding program, and feedback is realized by a visualization interface.The present invention effectively combines EEG signals with notice level, is mutually presented in the form of variation experiment, improves the interest for the treatment of and treats the sustainable time, can effectively help the crowd of notice existing defects to improve notice level.
Description
Technical field
The invention belongs to the integrated use of Cognitive Neuroscience, areas of information technology and automation field, it is related to utilization
The brain-computer interface BCI technologies of interaction between human brain and computer, the notice level of real-time testing user is simultaneously carried
Rise training.
Background technology
EEG signals (Electroencephalograph, EEG) with our life all the time, be brain cell group from
Hair property, rhythmicity electrical activity can be obtained in the general reaction of cerebral cortex and scalp by the electrode detection being placed on scalp
Arrive.EEG can be divided into the species rhythm ripple of δ, θ, α, β tetra- according to different frequencies.Many external scholar experts are by many experiments point
Analysis finds that the α wave bands in human body electroencephalogram's ripple are the main activities frequencies under quiet, waking state.Attention deficit-hyperactivity disorder
Children show θ electricals activity of brain and the increase of θ/β power ratios, α and β activity reductions.It is, therefore, usually considered that θ slow-wave activities increase
Plus, the increase of θ/β power ratios, α and β activities weaken be decreased attention principal character, but often there is also some for other wave bands
Influence.
BCI:Brain-computer interface technology (Brain Computer Interface, BCI) is exactly by gathering cerebral cortex god
The EEG signals produced through system activity, by methods such as amplification, filtering, are translated into the letter that can be recognized by computer
Number, therefrom distinguish the true intention of people.
EEGLAB:This is a kind of tool box based on Matlab.It is mainly for the treatment of continuous recording EEG signals
(EEG), brain magnetic signal (MEG) and other electric physiological datas.It uses method mainly have independent component analysis (ICA), when
M- frequency analysis, drafting ERP figures, exclusion artefact and several useful visualization formulations are (for being averaging and single-trial extraction number
According to) etc..
Existing brain-computer interface patented technology is few to be applied on human brain Test of attention, current existing patented technology
Pertain only to the assessment of notice under the training (such as Application No. CN201020206845 patent) of notice, driving environment (such as
Application No. CN201410381256 invention), not yet have related special to training system for the real-time testing of human brain notice
Profit is disclosed.
The content of the invention
Present invention solves the technical problem that being to provide one kind realizes notice real-time testing and training, and with fair speed
With the brain machine interface system of precision.The comprehensive multiple features method of the present invention analyzes the notice of human brain, by computer or mobile phone etc. eventually
Real-time Feedback notice level is held, and notice training is carried out according to feedback result.The system accuracy is high, and has necessarily
It is interesting.
Therefore, solution proposed by the present invention is a kind of real-time human brain Test of attention and training system based on EEG,
System includes notice experiment, signal acquisition, data analysis, five parts of real-time Transmission and test feedback, notice experiment
Part is divided into internal system and outside experiment, and signal acquisition part collects the EEG data of user using brain wave acquisition equipment；Number
Denoising, filtering and the analysis of related rhythm and pace of moving things ripple are carried out to the signal gathered using data analysis program according to analysis part；It is real
When hop will analyze obtained quantized values and preserve in case extracting at any time, and transmitted by corresponding interface, test feedback
Part reads the data of real-time Transmission part using corresponding program, is realized and fed back by a visualization interface.
Further, experiment is can arbitrarily distinguish the experiment of notice intensity outside said system, and user can voluntarily determine
It is fixed, play a part of analysis and detection.
Notice assay format inside said system is various, the interest for improving user, the notice of internal system
Experiment can Real-time Feedback, the state at each moment all comes on the scene the influence of power level, and can clearly be reflected to user,
So that user carries out psychological hint, the effect for improving notice is reached.
Preferably, in signal acquisition part, eeg signal acquisition frequency can use 800~1200Hz, the lead of selection
It is Fp1, Fp2, F7, F3, Fz, F4 and F8, by real-time between programming realization electroencephalogramsignal signal collection equipment and data processor
The interface of eeg data transmission.
It is divided into two pieces in real-time Transmission part, first piece is by the real-time data transmission of collection to data analysis component, separately
One piece is to transmit the result of analysis to test feedback and notice experimental section.
Preferably, the above-mentioned real-time data transmission by collection to data analysis component is realized by BCI2000 softwares.
The result of analysis, which is transmitted to test feedback and notice experimental section, to be read accordingly by internal system
Data needed for program is read, the frequency of transmission is determined by the frequency of collection signal.
Above-mentioned data analysis component is handled the eeg data of collection, judges that the intensity of notice is entered successively
Capable processing is that ICA denoisings go artefact, filtering, EEG signals notice correlated characteristic to extract, and ICA is mainly completed to electrocardio, eye
The removal of electricity and random noise etc., wave filter is mainly accomplished that removal low frequency, high frequency and 50Hz Hz noise noises, and
And the rhythm and pace of moving things ripple of each frequency range is isolated, it is characterized extraction and prepares, BP neural network multi parameter analysis side is used in feature extraction
Method.
The data transfer that the notice is tested is to testing feedback fraction, and by visualization interface Real-time Feedback to using
Family.
Compared with prior art, beneficial effects of the present invention：
1, the present invention can effectively help the crowd of notice existing defects to improve notice level.Conventional is directed to note
The medicinal treatment side effect that meaning power defect crowd takes is very big, and the present invention effectively combines EEG signals and notice level
Come, mutually presented in the form of variation experiment, improve the interest for the treatment of, and then the raising treatment sustainable time is to note
The time that power is concentrated.
2, the present invention is by Real-time Feedback, and user can be with the notice level of real-time awareness oneself, so as to be carried out to oneself
Psychological hint goes to improve notice.
3, the present invention will realize that certain impetus is played in inexpensive, efficient attention deficit treatment to future,
It imply that great potential of the brain-computer interface in terms of life, medical treatment.
Brief description of the drawings
Fig. 1 is flow chart of the invention.
Fig. 2 is notice experimental section and test feedback fraction diagram.
Fig. 3 is that initial data reads result.
Fig. 4 is result after ICA processing.
Fig. 5 is filtering process result.
Embodiment
The present invention will be described in detail with instantiation below in conjunction with the accompanying drawings.
The general principle of the present invention is that intensity can be by now EEG when user carries out notice experiment
Rhythm and pace of moving things ripple embodies, when notice is concentrated, and θ electricals activity of brain and θ/β power ratios reduce, α and β activity enhancings, therefore with many
Parameter totally weighs the electric level of brain, gives each parametric distribution corresponding weight, finally quantifies notice level, and carry out anti-
Feedback.Specific data analysis component of the analysis process below can be described in detail.
Real-time human brain Test of attention and training system proposed by the present invention based on EEG include following components：Note
Power of anticipating experimental section, brain wave acquisition part, data analysis component, real-time Transmission and test feedback fraction.Notice experiment point
It is that internal system and its exterior are tested, its exterior experiment is that can arbitrarily distinguish the experiment of notice intensity, Yong Huke
Decide in its sole discretion, play a part of analysis with detection, and the notice of internal system experiment can Real-time Feedback, be favorably improved user
Notice level.Signal acquisition part collects the EEG data of user using brain wave acquisition equipment, and by real-time data transmission
To data analysis program.Data analysis component carries out denoising, filtering and phase using data analysis program to the signal gathered
The analysis of ripple is restrained in joint, so as to reflect the notice degree of user.Real-time Transmission part will analyze obtained quantized values guarantor
Deposit in case extracting at any time, and transmitted by corresponding interface.Test feedback fraction and read real-time Transmission part with corresponding program
Data, and make corresponding reaction.
The notice assay format of internal system is various, the interest for improving user.Have：Flowering, leaf growth,
Stay under water.Common ground is that the state at each moment of game all comes on the scene the influence of power level, and can clearly be fed back
To user, so that user carries out psychological hint, the effect for improving notice is reached.
In signal acquisition part, with Scan4.5 acquisition softwares, eeg signal acquisition frequency can use 800~1200Hz,
The electrode of selection is Fp1, Fp2, F7, F3, Fz, F4 and F8, programming realization electroencephalogramsignal signal collection equipment and data processor it
Between in real time eeg data transmission interface.
Data analysis component is handled the eeg data of collection, judges the intensity of notice.Carry out successively
Being processed as ICA denoisings goes artefact, filtering, EEG signals notice correlated characteristic to extract.ICA mainly complete to electrocardio, eye electricity with
And the removal of random noise etc., wave filter is mainly accomplished that removal low frequency, high frequency and 50Hz Hz noise noises, and point
The rhythm and pace of moving things ripple of each frequency range is separated out, extraction is characterized and prepares.
Real-time Transmission part is divided into two pieces, first piece be the real-time data transmission by collection to analysis part, another piece is
The result of analysis is transmitted to test feedback and notice experimental section.The former is transmitted by BCI2000 softwares, the latter
Data needed for being read by the corresponding reading program of internal system.The frequency of transmission is determined by the frequency of collection signal.
The notice experiment of test feedback fraction and internal system is associated, and Test of attention result feds back through one
Visualization interface is realized, mainly feeds back data visualization, and visualization uses the form feedback of figure.
An instantiation is provided below, is described in detail with the implementation to the present invention.
In this example, electroencephalogramsignal signal collection equipment uses NeuroScan equipment, and Scan4.5 softwares are by the brain telecommunications of collection
Eeg data is transferred to MATLAB softwares through BCI2000 platforms and completes data processing by number in real time.
With reference to Fig. 1, whole system includes notice experiment, brain wave acquisition, data analysis, real-time Transmission and test feedback
Five parts
Notice experimental section and test feedback fraction are as shown in Figure 2.Notice experiment is divided into internal system and system
Outside experiment, its exterior experiment is can arbitrarily distinguish the experiment of notice intensity, and user can decide in its sole discretion, if selection
Its exterior is tested, then system opens directly into visual feedback part, feeds back notice intensity real-time quantitative.If
The notice experiment of internal system is selected, then is had：Flowering, leaf such as grow, stayed under water at the games.That plays is each
The state at moment all comes on the scene the influence of power level, while there is visual feedback part.Here flowering experiment is chosen
It is used as presented example.
User's eeg data is gathered in real time using NeuroScan equipment, and eeg signal acquisition frequency can use 1000Hz, its
In, because notice characteristic potential is primarily generated at the frontal region of brain, so, according to " 10-20 international standards lead ", choose
Seven leads of the position marked as Fp1, Fp2, F7, F3, Fz, F4 and F8 on electrode cap, reference electrode and earth polar are chosen
The default location on electrode cap that NeuroScan is equipped with.The brain wave acquisition result of each passage is as shown in Figure 3.
Data analysis component mainly realizes that after EEG signals data are received, MATLAB is every 5 by MATLAB
The EEG signals data that second processing is once gathered for first 5 seconds, and data are preserved into text, so that hop is carried in real time
Take.
The processing carried out successively to the eeg data of collection is that artefact, filtering, EEG signals notice feature are gone in ICA denoisings
Extract.The eeg data of a length of 5 seconds when analysis is gathered every time.
(1) artefact is removed in ICA denoisings
EEG signals are a kind of randomness very strong electricity physiological signals, and a variety of moods and phychology can all influence it
Change.Therefore, EEG signals have very high time-varying sensitiveness, are easily polluted by uncorrelated noise, so that it is pseudo- to form various brain electricity
Mark, wherein influence maximum is electrocardio and the electric artefact of eye.ICA mainly completes going to electrocardio, eye electricity and random noise etc.
Remove, benefit is that each component obtained through ICA processing not remove only correlation, but also is mutual statistical independence, it is theoretical
Knowledge is：
The first step：Assuming that N-dimensional observation signal is Y (t), Y (t)=[y1(t),y2(t)......yN(t)]T, including collection
Obtained various artefacts and noise component(s), S (t) be produce observation signal M mutual statistical independence source signal, S (t)=
[s1(t),s2(t)......sM(t)]T。
Second step：Observation signal is produced by source signal after system linear mixing, i.e. Y (t)=BS (t), B
For sytem matrix.
3rd step：In the case where hybrid system matrix B and source signal S (t) are unknown, merely with observation signal Y (t)
With source signal statistical iteration it is assumed that finding a linear transformation separation matrix D so that L (t)=DY (t)=DBS (t) to the greatest extent may be used
Can be equal to source signal S (t).Now can with L (t) signals finally given approximately replace original S (t) signal, and by each
Component is all equivalent to be replaced and has separated out.
In instances, it is believed that various potential differences and EEG signal are instantaneous linear mixing respectively by separate source generation
, analysis result is as shown in Figure 4.Wherein abscissa represents the time, and ordinate represents EEG amplitudes.(2) filter
Wave filter is mainly accomplished that removal low frequency, high frequency and 50Hz Hz noise noises, and isolates each frequency
The rhythm and pace of moving things ripple of section, is characterized extraction and prepares.
Low-frequency disturbance is mainly baseline drift, electrode and human contact is bad, amplifier temperature drift or breathing are drawn during by measuring
Rise, High-frequency Interference is mainly Radio frequency interference and myoelectricity interference present in collection.Band logical can be carried out with Butterworth filter
Filtering, in MATLAB, can directly invoke butter functions and filtfilt functions.
The minimizing technology of 50Hz Hz noises is filtered using digital trap, and what is used in matlab is designed, designed
Butterworth type 50Hz trapper function function [Num, Den]=ZB_50_filter (f0, B1, N), wherein f0, B1, N
Respectively trapper centre frequency, unilateral bandwidth and filter order, the checking that this function passes through fdatool tool boxes.
Separate that each species rhythm ripple uses is Finite Impulse Response filter, wherein δ wave frequency rates in 1~4Hz, θ wave frequency rates 4~
7Hz, α wave frequency rate are in 8~13Hz, and β wave frequency rates are in 13~20Hz.Separating resulting is as shown in Figure 5.
(3) feature extraction
For accurate evaluation notice level, the present invention takes more characteristic parameters as standard, specific as follows：
W1:δ wave energies account for the percentage of EEG signals gross energy；
W2:θ wave energies account for the percentage of EEG signals gross energy；
Wα:α wave energies account for the percentage of EEG signals gross energy；
Rel:The ratio of θ wave energies and β wave energies；
Pβ:The absolute value of β energy；
fmax:The Frequency point of ceiling capacity in β ripples.
Nonlinear fitting is carried out using three layers of BP neural network, the neuron number of input layer is N=6, the god of output layer
It is K=2, hidden neuron number M rule of thumb formula through first number：
Desirable M=5, P ≈ 32, excitation function are what Nonlinear Monotone rose
Sigmoid functions.The eeg data when notice of the sample early stage collection of setting study is concentrated, is determined by sample learning
Weight (between 0~1) shared by each parameter, initial weight is set between 0.1~0.3, show that approximate notice is concentrated
When calculation formula, and calculate notice concentrate when number range.Afterwards by a variety of traditional Test of attention methods (as relaxed
Your special square method) test, determine the numerical value of different notice situations, the data for afterwards obtaining real-time collection analysis are with more than
Data compare, and just can embody notice level.Concrete condition has embodied in visual feedback part.
Real-time Transmission part is divided into two pieces, and first piece is transmitted by BCI2000 softwares, and the data of collection are real-time
Transmit to analysis part, another piece be data needed for being read by the corresponding reading program of internal system to visual feedback and
Notice experimental section.The frequency of transmission is determined by the frequency of collection signal, and 1000Hz is should be in this example.
The notice experiment of test feedback fraction and internal system is associated, and Test of attention result feds back through one
Visualization interface is realized, mainly feeds back data visualization, and visualization uses the form feedback of figure, specific performance
Illustrated in notice experimental section.
It will be clear that in order that the example implemented is more detailed, above embodiment is preferred embodiment, for
Some known technologies those skilled in the art can also be implemented using other substitute modes；And accompanying drawing part is merely to more
The description embodiment of body, it is no intended to specifically limited the present invention.
The present invention is not limited to the concrete technical scheme described in above-described embodiment, the technical side of all use equivalent substitution formation
Case is the protection of application claims.
Claims (9)
1. a kind of real-time human brain Test of attention and training system based on EEG, it is characterised in that include notice experiment, signal
Collection, data analysis, five parts of real-time Transmission and test feedback, notice experimental section is divided into internal system and outside is real
Test, signal acquisition part collects the EEG data of user using brain wave acquisition equipment；Data analysis component utilizes data analysis journey
The signal that ordered pair is gathered carries out denoising, filtering and the analysis of related rhythm and pace of moving things ripple；Real-time Transmission part will analyze obtained amount
Change numerical value to preserve in case extracting at any time, and transmit by corresponding interface, test feedback fraction reads real using corresponding program
When hop data, pass through visualization interface and realize feedback.
2. real-time human brain Test of attention and training system according to claim 1 based on EEG, it is characterised in that described
Its exterior experiment is can arbitrarily distinguish the experiment of notice intensity, and user can decide in its sole discretion, play analysis and detection
Effect.
3. real-time human brain Test of attention and training system according to claim 1 based on EEG, it is characterised in that described
The notice assay format of internal system is various, the interest for improving user, and the notice experiment of internal system can be anti-in real time
Feedback, the state at each moment all comes on the scene the influence of power level, and can clearly be reflected to user, so that user enters
Row psychological hint, reaches the effect for improving notice.
4. the method for real-time human brain Test of attention and training system according to claim 1 based on EEG, its feature exists
In in signal acquisition part, eeg signal acquisition frequency can use 800~1200Hz, the lead of selection be Fp1, Fp2, F7, F3,
Fz, F4 and F8, pass through connecing that real-time eeg data between programming realization electroencephalogramsignal signal collection equipment and data processor is transmitted
Mouthful.
5. real-time human brain Test of attention and training system according to claim 1 based on EEG, it is characterised in that in reality
When hop be divided into two pieces, first piece be the real-time data transmission by collection to data analysis component, another piece is to analyze
Result transmit to test feedback and notice experimental section.
6. real-time human brain Test of attention and training system according to claim 5 based on EEG, it is characterised in that described
By the real-time data transmission of collection to data analysis component realized by BCI2000 softwares.
7. real-time human brain Test of attention and training system according to claim 5 based on EEG, it is characterised in that described
The result of analysis, which is transmitted to test feedback and notice experimental section, to be read by the corresponding reading program of internal system
Required data, the frequency of transmission is determined by the frequency of collection signal.
8. real-time human brain Test of attention and training system according to claim 1 based on EEG, it is characterised in that described
Data analysis component the eeg data of collection is handled, judge that the processing that the intensity of notice is carried out successively is
ICA denoisings go artefact, filtering, EEG signals notice correlated characteristic to extract, and ICA mainly completes electric and random to electrocardio, eye
The removal of noise etc., wave filter is mainly accomplished that removal low frequency, high frequency and 50Hz Hz noise noises, and isolates each
The rhythm and pace of moving things ripple of individual frequency range, is characterized extraction and prepares, and BP neural network multi parameter analysis method is used in feature extraction.
9. real-time human brain Test of attention and training system according to claim 1 based on EEG, it is characterised in that by institute
The data transfer of notice experiment is stated to testing feedback fraction, and by visualization interface Real-time Feedback to user.
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