CN107024987B - Real-time human brain attention testing and training system based on EEG - Google Patents
Real-time human brain attention testing and training system based on EEG Download PDFInfo
- Publication number
- CN107024987B CN107024987B CN201710164162.2A CN201710164162A CN107024987B CN 107024987 B CN107024987 B CN 107024987B CN 201710164162 A CN201710164162 A CN 201710164162A CN 107024987 B CN107024987 B CN 107024987B
- Authority
- CN
- China
- Prior art keywords
- attention
- data
- frequency
- experiment
- real
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000012360 testing method Methods 0.000 title claims abstract description 36
- 210000004556 brain Anatomy 0.000 title claims abstract description 16
- 238000012549 training Methods 0.000 title claims abstract description 10
- 238000002474 experimental method Methods 0.000 claims abstract description 55
- 230000005540 biological transmission Effects 0.000 claims abstract description 32
- 238000004458 analytical method Methods 0.000 claims abstract description 23
- 238000007405 data analysis Methods 0.000 claims abstract description 18
- 238000000605 extraction Methods 0.000 claims abstract description 15
- 238000001914 filtration Methods 0.000 claims abstract description 15
- 230000033764 rhythmic process Effects 0.000 claims abstract description 13
- 230000000007 visual effect Effects 0.000 claims abstract description 13
- 238000000034 method Methods 0.000 claims description 13
- 230000006870 function Effects 0.000 claims description 12
- 230000000694 effects Effects 0.000 claims description 7
- 239000011159 matrix material Substances 0.000 claims description 6
- 210000002569 neuron Anatomy 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 238000013528 artificial neural network Methods 0.000 claims description 4
- 238000012800 visualization Methods 0.000 claims description 4
- 238000001514 detection method Methods 0.000 claims description 3
- 238000000926 separation method Methods 0.000 claims description 3
- 230000008901 benefit Effects 0.000 claims description 2
- 238000004364 calculation method Methods 0.000 claims description 2
- 230000008859 change Effects 0.000 claims description 2
- 230000008451 emotion Effects 0.000 claims description 2
- 230000005284 excitation Effects 0.000 claims description 2
- 238000005259 measurement Methods 0.000 claims description 2
- 238000011527 multiparameter analysis Methods 0.000 claims description 2
- 230000003183 myoelectrical effect Effects 0.000 claims description 2
- 230000029058 respiratory gaseous exchange Effects 0.000 claims description 2
- 230000035945 sensitivity Effects 0.000 claims description 2
- 230000009466 transformation Effects 0.000 claims description 2
- 238000012795 verification Methods 0.000 claims description 2
- 230000005611 electricity Effects 0.000 claims 2
- 230000000630 rising effect Effects 0.000 claims 1
- 230000007547 defect Effects 0.000 abstract 1
- 238000012880 independent component analysis Methods 0.000 description 11
- 230000008569 process Effects 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 5
- 238000002582 magnetoencephalography Methods 0.000 description 2
- 210000004761 scalp Anatomy 0.000 description 2
- 230000009897 systematic effect Effects 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 230000003321 amplification Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 210000004958 brain cell Anatomy 0.000 description 1
- 210000003710 cerebral cortex Anatomy 0.000 description 1
- 230000001149 cognitive effect Effects 0.000 description 1
- 230000002301 combined effect Effects 0.000 description 1
- 238000013075 data extraction Methods 0.000 description 1
- 230000002950 deficient Effects 0.000 description 1
- 230000006735 deficit Effects 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000002015 leaf growth Effects 0.000 description 1
- 210000000653 nervous system Anatomy 0.000 description 1
- 238000003199 nucleic acid amplification method Methods 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000001020 rhythmical effect Effects 0.000 description 1
- 230000002269 spontaneous effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
- G06F3/015—Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
- A61B5/168—Evaluating attention deficit, hyperactivity
-
- 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]
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/02—Preprocessing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/08—Feature extraction
Landscapes
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Theoretical Computer Science (AREA)
- Molecular Biology (AREA)
- Biophysics (AREA)
- Public Health (AREA)
- Animal Behavior & Ethology (AREA)
- General Engineering & Computer Science (AREA)
- Pathology (AREA)
- Psychiatry (AREA)
- Heart & Thoracic Surgery (AREA)
- Medical Informatics (AREA)
- Veterinary Medicine (AREA)
- Surgery (AREA)
- Developmental Disabilities (AREA)
- Psychology (AREA)
- Signal Processing (AREA)
- General Physics & Mathematics (AREA)
- Artificial Intelligence (AREA)
- Mathematical Physics (AREA)
- Dermatology (AREA)
- Evolutionary Computation (AREA)
- Computing Systems (AREA)
- Computational Linguistics (AREA)
- Hospice & Palliative Care (AREA)
- Educational Technology (AREA)
- Software Systems (AREA)
- Social Psychology (AREA)
- Data Mining & Analysis (AREA)
- Neurology (AREA)
- Neurosurgery (AREA)
- Human Computer Interaction (AREA)
- Child & Adolescent Psychology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physiology (AREA)
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
The invention discloses a real-time human brain attention testing and training system based on EEG, which comprises an attention experiment part, a signal acquisition part, a data analysis part, a real-time transmission part and a test feedback part, wherein the attention experiment part is divided into an internal experiment part and an external experiment part of the system, and the signal acquisition part acquires EEG data of a user by utilizing EEG acquisition equipment; the data analysis part utilizes a data analysis program to perform denoising, filtering and related rhythm wave analysis on the acquired signals; the real-time transmission part stores the quantized numerical values obtained by analysis for extraction at any time and transmits the quantized numerical values through a corresponding interface, and the test feedback part reads data of the real-time transmission part by using a corresponding program and realizes feedback through a visual interface. The electroencephalogram signal and the attention level are effectively combined and presented in a diversified experiment form, so that the interestingness of treatment and the sustainable time of treatment are improved, and the attention level of people with attention defects can be effectively improved.
Description
Technical Field
The invention belongs to the comprehensive application of the fields of cognitive neuroscience, information technology and automatic control, and relates to a brain-computer interface BCI technology for testing the attention level of a user in real time and performing promotion training by using the interaction between the human brain and a computer.
Background
EEG (electroencephalogram) is the general response of spontaneous and rhythmic electrical activity of brain cell groups on cerebral cortex and scalp along with the life of people and can be detected by electrodes placed on the scalp, EEG can be divided into four rhythm waves of delta, theta, α and β according to different frequencies.
BCI (Brain Computer Interface) technology is that Brain electrical signals generated by Brain cortex nervous system activities are collected and converted into signals which can be recognized by a Computer through methods of amplification, filtering and the like, and the real intention of a person is recognized from the signals.
EEGLAB, a Matlab-based tool kit. It is mainly used for processing continuously recorded electroencephalogram signals (EEG), magnetoencephalography signals (MEG) and other electrophysiological data. The method mainly comprises Independent Component Analysis (ICA), time-frequency analysis, ERP drawing, artifact elimination, several useful visualization modes (for averaging and single data extraction), and the like.
The existing brain-computer interface patent technology is rarely applied to a human brain attention test, the existing patent technology only relates to attention training (such as a patent with an application number of CN 201020206845), attention assessment in a driving environment (such as an invention with an application number of CN 201410381256), and a real-time testing and training system for human brain attention is not disclosed.
Disclosure of Invention
The invention solves the technical problem of providing a brain-computer interface system which realizes the real-time attention test and training and has higher speed and precision. The invention analyzes the attention of human brain by integrating a multi-feature method, feeds back the attention level in real time through terminals such as a computer or a mobile phone and the like, and carries out attention training according to the feedback result. The system has high accuracy and certain interest.
The system comprises an attention experiment part, a signal acquisition part, a data analysis part, a real-time transmission part and a test feedback part, wherein the attention experiment part is divided into an internal experiment part and an external experiment part of the system, and the signal acquisition part acquires EEG data of a user by utilizing EEG acquisition equipment; the data analysis part utilizes a data analysis program to perform denoising, filtering and related rhythm wave analysis on the acquired signals; the real-time transmission part stores the quantized numerical values obtained by analysis for extraction at any time and transmits the quantized numerical values through a corresponding interface, and the test feedback part reads data of the real-time transmission part by using a corresponding program and realizes feedback through a visual interface.
Furthermore, the external experiment of the system is an experiment capable of distinguishing the attention concentration degree, and a user can decide by himself to play a role in analysis and detection.
The attention experiment in the system has various forms and is used for improving the interest of the user, the attention experiment in the system can feed back in real time, the state at each moment is influenced by the attention level, and the state can be clearly reflected to the user, so that the user can give psychological hint, and the effect of improving the attention is achieved.
Preferably, in the signal acquisition part, the electroencephalogram signal acquisition frequency can be 800-1200 Hz, the selected leads are Fp1, Fp2, F7, F3, Fz, F4 and F8, and the interface for real-time electroencephalogram data transmission between the electroencephalogram signal acquisition equipment and the data processing program is realized through programming.
The real-time transmission part is divided into two blocks, the first block transmits the acquired data to the data analysis part in real time, and the other block transmits the analysis result to the test feedback and attention experiment part.
Preferably, the real-time transmission of the collected data to the data analysis part is realized by BCI2000 software.
The transmission of the analysis results to the test feedback and attention experiment part is to read the required data by the corresponding reading program inside the system, and the transmission frequency is determined by the frequency of the collected signals.
The data analysis part processes the acquired electroencephalogram data, judges the concentration degree of attention and sequentially processes the acquired electroencephalogram data into ICA denoising and artifact removing, filtering and electroencephalogram signal attention related feature extraction, ICA mainly removes electrocardio, electrooculogram, random noise and the like, and the filter mainly removes low-frequency, high-frequency and 50Hz power frequency interference noise, separates out rhythm waves of each frequency band and prepares for feature extraction, and the feature extraction applies a BP neural network multi-parameter analysis method.
And transmitting the data of the attention experiment to a test feedback part, and feeding back the data to a user in real time through a visual interface.
Compared with the prior art, the invention has the beneficial effects that:
1, the invention can effectively help the attention-deficient people to improve the attention level. The invention effectively combines the electroencephalogram signal and the attention level and presents the combined effect in the form of diversified experiments, thereby improving the interestingness of treatment and further improving the sustainable time of treatment, namely the time of attention concentration.
2, the invention can make the user know the attention level in real time through real-time feedback, thereby giving psychological hint to the user to improve the attention.
3, the invention plays a certain role in promoting the realization of low-cost and high-efficiency attention deficit treatment in the future, and also indicates the great potential of the brain-computer interface in the aspects of life and medical treatment.
Drawings
FIG. 1 is a flow chart of the present invention.
Fig. 2 is a graphical representation of the attention experiment section and the test feedback section.
FIG. 3 shows the result of raw data reading.
FIG. 4 shows the results after ICA treatment.
Fig. 5 shows the result of the filtering process.
Detailed Description
The invention is described in detail below with reference to the drawings and specific examples.
The basic principle of the invention is that when a user carries out an attention experiment, the concentration degree can be embodied by rhythm waves in EEG at the moment, when the user is concentrated, theta electroencephalogram activity and theta/β power ratio are reduced, α and β activities are enhanced, therefore, the electroencephalogram level is generally measured by using multiple parameters, corresponding weight is distributed to each parameter, the attention level is finally quantified and fed back, and the detailed analysis process is detailed in the data analysis part later.
The real-time human brain attention testing and training system based on the EEG comprises the following parts: the system comprises an attention experiment part, an electroencephalogram acquisition part, a data analysis part, a real-time transmission part and a test feedback part. The attention experiment is divided into an experiment inside the system and an experiment outside the system, the experiment outside the system is an experiment capable of arbitrarily distinguishing the attention concentration degree, a user can decide by himself to play a role in analysis and detection, the attention experiment inside the system can feed back in real time, and the attention level of the user can be improved. The signal acquisition part collects EEG data of a user by using an EEG acquisition device and transmits the data to a data analysis program in real time. The data analysis part utilizes a data analysis program to perform denoising, filtering and related rhythm wave analysis on the acquired signals, so that the attention degree of the user is reflected. The real-time transmission part stores the quantized numerical values obtained by analysis for extraction at any time and transmits the quantized numerical values through a corresponding interface. The test feedback part reads the data of the real-time transmission part by using a corresponding program and reacts correspondingly.
The attention experiment form inside the system is various and is used for improving the interest of users. Comprises the following steps: flower blooming, leaf growth, sinking and submerging, and the like. The common point is that the state of the game at each moment is influenced by the attention level and can be clearly fed back to the user, so that the user can give psychological hint to achieve the effect of improving the attention.
In the signal acquisition part, Scan4.5 acquisition software is used, the electroencephalogram signal acquisition frequency can be 800-1200 Hz, the selected electrodes are Fp1, Fp2, F7, F3, Fz, F4 and F8, and an interface for realizing real-time electroencephalogram data transmission between electroencephalogram signal acquisition equipment and a data processing program is programmed.
The data analysis part processes the acquired electroencephalogram data and judges the concentration degree of attention. Sequentially carrying out ICA denoising and artifact removing, filtering and electroencephalogram signal attention related feature extraction. ICA mainly removes electrocardio, electrooculogram, random noise and the like, and the filter mainly removes low-frequency, high-frequency and 50Hz power frequency interference noise, separates rhythm waves of each frequency band and prepares for feature extraction.
The real-time transmission part is divided into two blocks, wherein the first block transmits the acquired data to the analysis part in real time, and the other block transmits the analysis result to the test feedback and attention experiment part. The former is transmitted through BCI2000 software, and the latter reads required data through a corresponding reading program in the system. The frequency of transmission is determined by the frequency of the acquired signal.
The test feedback part is connected with an attention experiment in the system, the feedback of the attention test result is realized through a visual interface, data is mainly fed back visually, and the visualization adopts a graphical form for feedback.
The following provides a specific example to explain the implementation of the present invention in detail.
In the embodiment, the electroencephalogram signal acquisition equipment adopts NeuroScan equipment, and the Scan4.5 software transmits the acquired electroencephalogram signal data to MATLAB software in real time through a BCI2000 platform to complete data processing.
Referring to fig. 1, the whole system comprises five parts of attention experiment, electroencephalogram acquisition, data analysis, real-time transmission and test feedback
The attention experiment part and the test feedback part are shown in fig. 2. The attention experiment is divided into an experiment inside the system and an experiment outside the system, the experiment outside the system is an experiment capable of distinguishing the attention concentration degree, a user can decide by himself, and if the experiment outside the system is selected, the system is directly opened to the visual feedback part to quantitatively feed back the attention concentration degree in real time. If attention experiments inside the system are chosen, then: flower blooming, leaf growing, sinking and submerging and the like. The state at each moment of the game is affected by the level of attention, while the visual feedback section appears. The flower blooming experiment is taken here as an example demonstration.
The electroencephalogram data of a user are collected in real time by using a Neuroscan device, the electroencephalogram signal collection frequency can be 1000Hz, wherein, as attention characteristic potentials are mainly generated in the frontal area of the brain, seven leads with the position labels of Fp1, Fp2, F7, F3, Fz, F4 and F8 on an electrode cap are selected according to a 10-20 international standard lead, and default positions on the electrode cap matched with the Neuroscan are selected as a reference electrode and a ground electrode. The brain electrical acquisition results of each channel are shown in fig. 3.
The data analysis part is mainly realized through MATLAB, after the electroencephalogram signal data are received, the MATLAB processes the electroencephalogram signal data acquired in the previous 5 seconds every 5 seconds, and the data are stored in a text file for the transmission part to extract in real time.
The acquired electroencephalogram data are sequentially processed into ICA denoising, artifact removing, filtering and electroencephalogram signal attention characteristic extraction. And analyzing and acquiring the electroencephalogram data with the time length of 5 seconds each time.
(1) ICA denoising and artifact removing
The electroencephalogram signal is an electrophysiological signal with strong randomness, and various emotions and mind states can influence the change of the electroencephalogram signal. Therefore, the electroencephalogram signals have high time-varying sensitivity and are easily polluted by irrelevant noise, so that various electroencephalogram artifacts are formed, wherein the electroencephalogram artifacts and ocular artifacts have the greatest influence. ICA mainly accomplishes the removal to electrocardio, electro-oculogram and random noise etc. the advantage is that each component obtained through ICA processing not only removes the correlation, but also is mutually statistical independent, the theoretical knowledge is:
the first step is as follows: suppose that the N-dimensional observation signal is y (t), y (t) ═ y1(t),y2(t)......yN(t)]TIncluding various artifacts and noise components, S (t) is M mutually independent source signals for generating observation signals, S (t) [ s ]1(t),s2(t)......sM(t)]T。
The second step is that: the observation signal is generated by the source signal after being subjected to systematic linear mixing, i.e., y (t) ═ bs (t), and B is a systematic matrix.
The third step: in the case where the hybrid system matrix B and the source signal s (t) are unknown, a linear transformation separation matrix D is found so that l (t) dy (t) dbs (t) is as equal to the source signal s (t) as possible, using only the assumption that the observed signal y (t) and the source signal are statistically independent. The original s (t) signal can now be replaced by the final l (t) signal approximation and the components are replaced equivalently and separated out.
In the example, it is believed that the various parallax and EEG signals are each generated by separate sources and are temporally linearly mixed, with the analysis shown in FIG. 4. Where the abscissa represents time and the ordinate represents EEG amplitude. (2) Filtering
The filter is mainly used for removing low-frequency, high-frequency and 50Hz power frequency interference noise, separating rhythm waves of each frequency band and preparing for feature extraction.
The low-frequency interference is mainly baseline drift and is caused by poor contact between an electrode and a human body, temperature drift of an amplifier or respiration during measurement, and the high-frequency interference is mainly radio frequency interference and myoelectric interference existing in acquisition. The band-pass filtering can be performed by a Butterworth filter, and the button function and the filtfiltfiltfilt function can be directly called in MATLAB.
The method for removing the 50Hz power frequency interference uses a digital trap filter, and a self-designed Butterworth type 50Hz trap function (Num, Den) ═ ZB _50_ filter (f0, B1, N) is applied in matlab, wherein f0, B1 and N are the center frequency, the unilateral bandwidth and the filter order of the trap respectively, and the function passes the verification of an fdatool tool box.
The FIR digital filter is used for separating various rhythm waves, wherein the frequency of delta wave is 1-4 Hz, the frequency of theta wave is 4-7 Hz, the frequency of α wave is 8-13 Hz, and the frequency of β wave is 13-20 Hz., and the separation result is shown in FIG. 5.
(3) Feature extraction
In order to accurately evaluate the attention level, the invention adopts multi-feature parameters as standards, and the specific steps are as follows:
W1the delta wave energy accounts for the total energy of the electroencephalogram signal;
W2the theta wave energy accounts for the total energy of the electroencephalogram signal;
Wαα percentage of wave energy to total energy of brain electrical signal;
the ratio of theta wave energy to β wave energy;
Pββ absolute value of energy;
fmaxβ the frequency point of the largest energy in the wave.
Adopting a three-layer BP neural network to carry out nonlinear fitting, wherein the number of neurons of an input layer is N-6, the number of neurons of an output layer is K-2, and the number of neurons of a hidden layer M is obtained according to an empirical formula:
The real-time transmission part is divided into two parts, the first part transmits the acquired data to the analysis part in real time through BCI2000 software, and the other part reads the required data to the visual feedback and attention experiment part through a corresponding reading program in the system. The frequency of transmission is determined by the frequency of the acquired signal, which in this example should be 1000 Hz.
The test feedback part is connected with the attention experiment in the system, the feedback of the attention test result is realized through a visual interface, data is mainly fed back visually, the visualization adopts a graphic form for feedback, and the specific expression is explained in the attention experiment part.
It should be noted that, in order to make the implementation example more detailed, the above embodiment is the preferred embodiment, and those skilled in the art can also implement other alternative ways; also, the drawings are only for purposes of more particularly describing embodiments and are not intended to particularly limit the invention.
The invention is not limited to the specific technical solutions described in the above embodiments, and all technical solutions formed by equivalent substitutions are all the claims of the present invention.
Claims (1)
1. A real-time human brain attention test and training system based on EEG is characterized by comprising five parts of attention experiment, signal acquisition, data analysis, real-time transmission and test feedback, wherein the attention experiment part is divided into an internal experiment part and an external experiment part of the system, and the signal acquisition part acquires EEG data of a user by utilizing EEG acquisition equipment; the data analysis part utilizes a data analysis program to perform denoising, filtering and related rhythm wave analysis on the acquired signals; the real-time transmission part stores the quantized numerical values obtained by analysis for being extracted at any time and transmits the quantized numerical values through a corresponding interface, the test feedback part reads data of the real-time transmission part by using a corresponding program and realizes feedback through a visual interface, the experiment outside the system is an experiment capable of distinguishing the attention concentration degree at will, a user can decide by himself and plays a role in analysis and detection, the attention experiment inside the system is various in form and is used for improving the interest of the user, the attention experiment inside the system can feed back in real time, the state at each moment is influenced by the attention level and can be clearly reflected to the user, so that the user can give psychological hint to achieve the effect of improving the attention, in the signal acquisition part, the electroencephalogram signal acquisition frequency can be set to 800 Hz 1200, and the selected leads are Fp1, Fp2, F7, F3, Fz, F4 and F8, an interface for realizing real-time electroencephalogram data transmission between an electroencephalogram signal acquisition device and a data processing program through programming is divided into two blocks in a real-time transmission part, the first block is used for transmitting acquired data to a data analysis part in real time, the other block is used for transmitting an analysis result to a test feedback and attention experiment part, the real-time transmission of the acquired data to the data analysis part is realized through BCI2000 software, the transmission of the analysis result to the test feedback and attention experiment part is used for reading required data through a corresponding reading program in a system, the transmission frequency is determined by the frequency of the acquired signals, the data analysis part is used for processing the acquired electroencephalogram data, the concentration degree of attention is judged, and the processing is sequentially carried out to carry out ICA denoising, artifact removal, filtering and electroencephalogram signal attention related characteristic extraction, and ICA finishes the electrocardio processing, Removing ocular electricity, random noise and the like, wherein the filter is used for removing low-frequency, high-frequency and 50Hz power frequency interference noise, separating rhythm waves of each frequency band, preparing for characteristic extraction, and transmitting data of the attention experiment to a test feedback part by using a BP neural network multi-parameter analysis method and feeding back the data to a user in real time through a visual interface;
the acquired electroencephalogram data are sequentially processed into ICA denoising and artifact removing, filtering and electroencephalogram signal attention characteristic extraction, and the electroencephalogram data with the acquisition time of 5 seconds are analyzed each time:
(1) ICA denoising and artifact removing;
the electroencephalogram signal is an electrophysiological signal with strong randomness, various emotions and heart states can influence the change of the electroencephalogram signal, the electroencephalogram signal has high time-varying sensitivity and is extremely easy to be polluted by irrelevant noise, so that various electroencephalogram artifacts are formed, the electrocardio and the ocular artifacts have the greatest influence, the ICA finishes removing the electrocardio, the ocular electricity, the random noise and the like, and the method has the advantages that the relevance of each component obtained by ICA treatment is removed, the components are mutually counted and independent, and the theoretical knowledge is as follows:
the first step is as follows: falseLet the N-dimensional observation signal be Y (t), Y (t) ═ y1(t),y2(t)......yN(t)]TIncluding various artifacts and noise components, S (t) is M mutually independent source signals for generating observation signals, S (t) [ s ]1(t),s2(t)......sM(t)]T；
The second step is that: the observation signal is generated by the linear mixing of the source signal, i.e. y (t) ═ bs (t), B is the system matrix;
the third step: under the condition that a hybrid system matrix B and a source signal S (t) are unknown, only by using the assumption that observation signals Y (t) and source signals are statistically independent, a linear transformation separation matrix D is found, so that L (t) DY (t) DBS (t) is equal to the source signal S (t) as much as possible, the finally obtained L (t) signal can be used for approximately replacing the original S (t) signal, and all components are equivalently replaced and separated;
(2) filtering;
the filter removes low-frequency, high-frequency and 50Hz power frequency interference noise, separates out rhythm waves of each frequency band and prepares for feature extraction;
low-frequency interference is baseline drift, caused by poor contact between an electrode and a human body during measurement, temperature drift of an amplifier or respiration, high-frequency interference is radio frequency interference and myoelectric interference existing in acquisition, band-pass filtering is carried out by using a Butterworth filter, and a button function and a filtfiltfiltfilt function are directly called in MATLAB;
the method for removing the 50Hz power frequency interference uses a digital trap filter, and a self-designed Butterworth type 50Hz trap function [ Num, Den ] ═ ZB _50_ filter (f0, B1, N) is applied in matlab, wherein f0, B1 and N are the center frequency, the unilateral bandwidth and the filter order of the trap respectively, and the function passes the verification of an fdatool tool box;
FIR digital filters are used for separating various rhythm waves, wherein the frequency of delta waves is 1-4 Hz, the frequency of theta waves is 4-7 Hz, the frequency of α waves is 8-13 Hz, and the frequency of β waves is 13-20 Hz;
(3) extracting characteristics;
in order to accurately assess the attention level, a multi-feature parameter is taken as a standard, and the following are concrete:
W1the delta wave energy accounts for the total energy of the electroencephalogram signal;
W2the theta wave energy accounts for the total energy of the electroencephalogram signal;
Wαα percentage of wave energy to total energy of brain electrical signal;
the ratio of theta wave energy to β wave energy;
Pββ absolute value of energy;
fmaxβ the frequency point of the maximum energy in the wave;
adopting a three-layer BP neural network to carry out nonlinear fitting, wherein the number of neurons of an input layer is N-6, the number of neurons of an output layer is K-2, and the number of neurons of a hidden layer M is obtained according to an empirical formula:
the real-time transmission part is divided into two parts, the first part is transmitted through BCI2000 software and transmits the acquired data to the analysis part in real time, the other part is used for reading the required data to the visual feedback and attention experiment part through a corresponding reading program in the system, the transmission frequency is determined by the frequency of the acquired signals, and in the example, the transmission frequency is 1000 Hz;
the test feedback part is connected with an attention experiment in the system, the feedback of the attention test result is realized through a visual interface, data is fed back visually, and the visualization adopts a graphical form for feedback.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710164162.2A CN107024987B (en) | 2017-03-20 | 2017-03-20 | Real-time human brain attention testing and training system based on EEG |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710164162.2A CN107024987B (en) | 2017-03-20 | 2017-03-20 | Real-time human brain attention testing and training system based on EEG |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107024987A CN107024987A (en) | 2017-08-08 |
| CN107024987B true CN107024987B (en) | 2020-04-14 |
Family
ID=59526188
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710164162.2A Active CN107024987B (en) | 2017-03-20 | 2017-03-20 | Real-time human brain attention testing and training system based on EEG |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107024987B (en) |
Families Citing this family (32)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11151440B2 (en) * | 2017-11-08 | 2021-10-19 | International Business Machines Corporation | Neural response human detector |
| CN107981997B (en) * | 2017-11-23 | 2019-11-29 | 郑州布恩科技有限公司 | A kind of method for controlling intelligent wheelchair and system based on human brain motion intention |
| CN108537704A (en) * | 2018-04-17 | 2018-09-14 | 深圳市心流科技有限公司 | Classroom evaluating method, device and computer readable storage medium |
| CN108766532A (en) * | 2018-05-11 | 2018-11-06 | 深圳市心流科技有限公司 | Improve teaching method, device and the computer readable storage medium of attention |
| CN108670276A (en) * | 2018-05-29 | 2018-10-19 | 南京邮电大学 | Study attention evaluation system based on EEG signals |
| CN109011096A (en) * | 2018-06-28 | 2018-12-18 | 昆明理工大学 | A kind of system fed back based on brain electric nerve for the brain concentration function that trains soldiers |
| CN109324692A (en) * | 2018-11-30 | 2019-02-12 | 中山大学 | A kind of virtual reality method and system for attention deficit hyperactivity disorder detection |
| CN109464151B (en) * | 2018-12-29 | 2021-09-07 | 中国科学院深圳先进技术研究院 | Attention suppression capability acquisition method, device, equipment and storage medium |
| CN109924975A (en) * | 2019-04-04 | 2019-06-25 | 燕山大学 | Attention level detection method based on complex brain network analysis |
| CN109907755A (en) * | 2019-04-26 | 2019-06-21 | 上海理工大学 | A kind of fatigue driving monitoring and interfering system based on BCI |
| CN110478593A (en) * | 2019-05-15 | 2019-11-22 | 常州大学 | Brain electricity attention training system based on VR technology |
| CN110403604B (en) * | 2019-07-03 | 2022-02-18 | 陈琦 | Method for constructing environment space and training attention based on attention concentration degree |
| CN112331304A (en) * | 2019-08-05 | 2021-02-05 | 王东承 | Children attention training system based on EEG technology |
| CN110968950A (en) * | 2019-11-27 | 2020-04-07 | 武汉理工大学 | Human-computer cooperation disassembly sequence planning method based on personnel fatigue |
| CN111414835B (en) * | 2020-03-16 | 2022-04-05 | 西南大学 | Detection and determination method for electroencephalogram signals caused by love impulsion |
| CN111887845A (en) * | 2020-07-31 | 2020-11-06 | 昆明理工大学 | Attention regulation system based on EEG nerve feedback |
| CN111743554A (en) * | 2020-08-04 | 2020-10-09 | 河南省安信科技发展有限公司 | Attention deficit hyperactivity disorder diagnosis and monitoring system based on attention analysis algorithm for brain wave analysis |
| CN112205985B (en) * | 2020-09-29 | 2021-09-03 | 北京航空航天大学 | Visual continuous attention training device, training and testing system and method thereof |
| CN112168185B (en) * | 2020-09-29 | 2021-11-09 | 北京航空航天大学 | Visual sustained attention testing device and method |
| CN112245756A (en) * | 2020-10-16 | 2021-01-22 | 郑州大学 | Attention training method based on single-channel electroencephalogram |
| CN112494774A (en) * | 2020-10-16 | 2021-03-16 | 杭州师范大学 | Continuous attention regulation and control system based on delayed electroencephalogram feedback |
| CN112336353B (en) * | 2020-11-04 | 2023-07-28 | 西安科技大学 | Multistage attention grading method based on Shulter square and LSTM |
| CN112363627A (en) * | 2020-11-26 | 2021-02-12 | 西安慧脑智能科技有限公司 | Attention training method and system based on brain-computer interaction |
| CN113011444B (en) * | 2020-12-18 | 2022-05-13 | 浙江大学 | Image identification method based on neural network frequency domain attention mechanism |
| CN112842825B (en) * | 2021-02-24 | 2023-06-09 | 郑州铁路职业技术学院 | Training device for rehabilitation and recovery of lower limbs |
| CN113208634A (en) * | 2021-04-07 | 2021-08-06 | 北京脑陆科技有限公司 | Attention detection method and system based on EEG brain waves |
| CN113288171A (en) * | 2021-05-20 | 2021-08-24 | 常州大学 | ADHD (ADHD) child cognitive function monitoring system based on electroencephalogram characteristics |
| TWI802908B (en) * | 2021-06-15 | 2023-05-21 | 南開科技大學 | Auxiliary analysis system for providing answer suggestion and method thereof |
| CN113577495A (en) * | 2021-07-21 | 2021-11-02 | 大连民族大学 | Children attention deficit hyperactivity disorder auxiliary treatment system based on BCI-VR |
| CN114020148B (en) * | 2021-10-12 | 2023-06-16 | 北京师范大学 | Training method and device for improving attention and electronic equipment |
| CN116027890A (en) * | 2022-10-24 | 2023-04-28 | 南京航空航天大学 | On-line attention detection system and detection method based on electroencephalogram signals |
| CN117481667A (en) * | 2023-10-24 | 2024-02-02 | 沈阳工业大学 | Electroencephalogram signal acquisition system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012044261A1 (en) * | 2010-09-29 | 2012-04-05 | The Office Of National Telecommunications Commission | System and method for ssvep based control of electrical devices |
| CN102553222A (en) * | 2012-01-13 | 2012-07-11 | 南京大学 | Brain function feedback training method supporting combat mode and system |
| CN103345640A (en) * | 2013-07-02 | 2013-10-09 | 北京工业大学 | Classification method for continuous imagining brain electrical signals |
| CN104202644A (en) * | 2014-09-29 | 2014-12-10 | 深圳市九洲电器有限公司 | Standardized output method, device, set-top box and system for brain wave signals |
| US9104467B2 (en) * | 2012-10-14 | 2015-08-11 | Ari M Frank | Utilizing eye tracking to reduce power consumption involved in measuring affective response |
-
2017
- 2017-03-20 CN CN201710164162.2A patent/CN107024987B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012044261A1 (en) * | 2010-09-29 | 2012-04-05 | The Office Of National Telecommunications Commission | System and method for ssvep based control of electrical devices |
| CN102553222A (en) * | 2012-01-13 | 2012-07-11 | 南京大学 | Brain function feedback training method supporting combat mode and system |
| US9104467B2 (en) * | 2012-10-14 | 2015-08-11 | Ari M Frank | Utilizing eye tracking to reduce power consumption involved in measuring affective response |
| CN103345640A (en) * | 2013-07-02 | 2013-10-09 | 北京工业大学 | Classification method for continuous imagining brain electrical signals |
| CN104202644A (en) * | 2014-09-29 | 2014-12-10 | 深圳市九洲电器有限公司 | Standardized output method, device, set-top box and system for brain wave signals |
Non-Patent Citations (1)
| Title |
|---|
| 基于 Android 平台的注意力监测系统研究与实现;孔亮;《中国优秀硕士学位论文全文数据库 信息科技辑》;20170115;参见正文第12-38页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107024987A (en) | 2017-08-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107024987B (en) | Real-time human brain attention testing and training system based on EEG | |
| CN108236464B (en) | Feature extraction method based on electroencephalogram signals and detection extraction system thereof | |
| CN109224242B (en) | Psychological relaxation system and method based on VR interaction | |
| CN106407733A (en) | Depression risk screening system and method based on virtual reality scene electroencephalogram signal | |
| CN110765920A (en) | Motor imagery classification method based on convolutional neural network | |
| CN110946576A (en) | Visual evoked potential emotion recognition method based on width learning | |
| CN106236080B (en) | The removing method of myoelectricity noise in EEG signals based on multichannel | |
| CN111184509A (en) | Emotion-induced electroencephalogram signal classification method based on transfer entropy | |
| CN112137616B (en) | Consciousness detection device for multi-sense brain-body combined stimulation | |
| CN112488002B (en) | Emotion recognition method and system based on N170 | |
| CN108958486A (en) | Mood brain machine interface system based on recognition of face brain electricity N170 ingredient | |
| CN108143412B (en) | Control method, device and system for electroencephalogram emotion analysis of children | |
| CN117064409B (en) | Method, device and terminal for evaluating transcranial direct current intervention stimulation effect in real time | |
| CN112426162A (en) | Fatigue detection method based on electroencephalogram signal rhythm entropy | |
| CN109871831A (en) | A kind of emotion identification method and system | |
| CN112806994A (en) | System and method for predicting individual stress coping mode based on physiological signal | |
| CN111616702A (en) | Lie detection analysis system based on cognitive load enhancement | |
| CN107510451B (en) | pitch perception ability objective assessment method based on brainstem auditory evoked potentials | |
| Islam et al. | Probability mapping based artifact detection and wavelet denoising based artifact removal from scalp EEG for BCI applications | |
| CN111144450A (en) | Method for constructing ERP paradigm based on name stimulation with different lengths | |
| Paulraj et al. | A machine learning approach for distinguishing hearing perception level using auditory evoked potentials | |
| CN117918863A (en) | Method and system for processing brain electrical signal real-time artifacts and extracting features | |
| CN106618486B (en) | Sleep state identification method and system in intelligent sleep assistance | |
| Ahmed et al. | Effective hybrid method for the detection and rejection of electrooculogram (EOG) and power line noise artefacts from electroencephalogram (EEG) mixtures | |
| CN110192875B (en) | Lie detection method based on multi-lead electroencephalogram signal form factor |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20211027 Address after: No.13, 3rd floor, building 1, No.1, Tidu street, Qingyang District, Chengdu, Sichuan 610000 Patentee after: Chengdu yishenrui Technology Co.,Ltd. Address before: 210023 9 Wen Yuan Road, Ya Dong new town, Qixia District, Nanjing, Jiangsu. Patentee before: NANJING University OF POSTS AND TELECOMMUNICATIONS Effective date of registration: 20211027 Address after: 100000 1419, floor 14, building 3, yard 38, Huangcun East Street, Daxing District, Beijing Patentee after: Beijing maidehaike Medical Technology Co.,Ltd. Address before: No.13, 3rd floor, building 1, No.1, Tidu street, Qingyang District, Chengdu, Sichuan 610000 Patentee before: Chengdu yishenrui Technology Co.,Ltd. |
|
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20211126 Address after: 213000 room 5066, 5th floor, block C, building 7, No. 123, Hexiang Road, West Taihu science and Technology Industrial Park, Changzhou City, Jiangsu Province Patentee after: Jiangsu ruinao Qizhi Medical Technology Co.,Ltd. Address before: 100000 1419, floor 14, building 3, yard 38, Huangcun East Street, Daxing District, Beijing Patentee before: Beijing maidehaike Medical Technology Co.,Ltd. |
|
| EE01 | Entry into force of recordation of patent licensing contract | ||
| EE01 | Entry into force of recordation of patent licensing contract |
Application publication date: 20170808 Assignee: Hainan Fangcun Hospital Management Co.,Ltd. Assignor: Jiangsu ruinao Qizhi Medical Technology Co.,Ltd. Contract record no.: X2022990000257 Denomination of invention: A EEG based real-time brain attention testing and training system Granted publication date: 20200414 License type: Exclusive License Record date: 20220610 |
|
| EC01 | Cancellation of recordation of patent licensing contract | ||
| EC01 | Cancellation of recordation of patent licensing contract |
Assignee: Hainan Fangcun Hospital Management Co.,Ltd. Assignor: Jiangsu ruinao Qizhi Medical Technology Co.,Ltd. Contract record no.: X2022990000257 Date of cancellation: 20231109 |
|
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20231127 Address after: Room 1419, 14th Floor, Building 3, No. 38 Huangcun East Street, Daxing District, Beijing, 102600 Patentee after: Beijing maidehaike Medical Technology Co.,Ltd. Address before: 213000 room 5066, 5th floor, block C, building 7, No. 123, Hexiang Road, West Taihu science and Technology Industrial Park, Changzhou City, Jiangsu Province Patentee before: Jiangsu ruinao Qizhi Medical Technology Co.,Ltd. |