CN108670276A - Study attention evaluation system based on EEG signals - Google Patents
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Abstract
The study attention evaluation system based on EEG signals that the invention discloses a kind of, including：Brain wave acquisition subsystem is pre-processed for acquiring EEG signals, removes Hz noise and baseline, and EEG signals are transmitted to brain electricity analytical subsystem；Brain electricity analytical subsystem extracts the feature vector that temporal signatures and frequency domain character are classified as attention, feature vector is inputted attention grader, obtains attention grade for analyzing collected EEG signals；Learning state display alarm subsystem feeds back to the attention grade that brain electricity analytical subsystem obtains at student and teacher；It realizes the real-time monitoring of attention of student, solves the technical issues of student's learning state is difficult to objective evaluation.
Description
Technical field
The invention belongs to attention detection technique fields, and in particular to a kind of study attention evaluation based on EEG signals
System.
Background technology
The detection of classroom learning state is the important link during learning aid.Traditional classroom detection mode is often with class
Based on the means such as hall enquirement, quiz, observation student's expression, teacher can not often take into account the learning state of every student,
And judging result randomness is big, subjectivity is strong.Therefore, realize that objective effective detection of student's learning state is particularly important.
In recent years, with the rapid development of brain-computer interface technology so that realized pair only by acquisition and the analysis to EEG signals
Human body psychological activity is captured as possibility.Attention plays important as a kind of psychological activity in the learning activities of student
Effect.The height of student classroom attention intensity largely reflects listen to the teacher quality and the learning state of student.
Lot of documents points out, the activity condition of each rhythm and pace of moving things wave of EEG signals and the state of attention residing for human body, which have, closely to be contacted.
Invention content
To solve the above problems, the present invention proposes a kind of study attention evaluation system based on EEG signals, realizes and learn
The real-time monitoring of raw attention, solves the technical issues of student's learning state is difficult to objective evaluation.
The present invention adopts the following technical scheme that, is that every student is equipped with a portable brain electric collecting device, using can wear
The eeg data for wearing brain wave acquisition equipment acquisition student, by analyzing the eeg data of class student to Real-Time Evaluation student's
State of attention reflects the situation of giving lessons of the study and teacher of student in real time, and by result via cell phone application or the ends PC program
Mode feeds back to student and teacher, to achieve the effect that learn attention Real-time Feedback, a kind of study based on EEG signals
Attention evaluation system, specifically includes：Brain wave acquisition subsystem, brain electricity analytical subsystem and learning state display alarm subsystem
System, wherein
Brain wave acquisition subsystem is pre-processed for acquiring EEG signals, removes Hz noise and baseline, and brain is electric
Signal transmission is to brain electricity analytical subsystem；
Brain electricity analytical subsystem extracts temporal signatures and frequency domain character as attention for analyzing collected EEG signals
Feature vector is inputted attention grader, obtains attention grade by the feature vector of power classification；
The attention grade that brain electricity analytical subsystem obtains is fed back to student and religion by learning state display alarm subsystem
At teacher.
Preferably, the brain wave acquisition subsystem uses the ADS1298 chips of TI companies as AFE(analog front end), acquires top
The EEG signals of P7, P3, Pz, P4 and P8 lead；The pretreatment of EEG signals is carried out using the CC2652R microprocessors of TI companies
It is transmitted with EEG signals.
Preferably, it includes bandpass filtering and removal baseline that the brain wave acquisition subsystem, which carries out pretreatment, constructs number tape
Bandpass filter removes 50Hz Hz noises；Using least square fitting baseline, and by baseline from original EEG signals
It removes.
Preferably, the communication between the brain wave acquisition subsystem and brain electricity analytical subsystem passes through the star-like networkings of Zigbee
It realizes, brain electricity analytical subsystem is as host to brain wave acquisition subsystem request data, brain wave acquisition subsystem in a manner of poll
It unites and returns the EEG signals data of designated length after receiving instruction.
Preferably, the brain wave acquisition subsystem includes fixation top beam, includes power supply and data transmission and processing unit, number
According to transmission processing unit for receiving EEG signals data, and EEG signals are pre-processed and transmitted；Dry electrode is acquired,
Acquisition is connected to by elastomeric element on top beam with dry electrode；Ear's reference electrode acquires dry electrode and ear with reference to electricity
Pole is by the signal transmission of acquisition to data transmission and processing unit.
Preferably, the frequency domain character of the brain electricity analytical subsystem extraction is the work(of δ waves, four θ waves, α waves and β waves frequency ranges
Rate value, temporal signatures are Sample Entropy and standard deviation, first-order difference mean value, second differnce mean value, construction feature vector, the note of use
Meaning power grader is probabilistic neural network PNN, and attention grade is divided into high, medium and low three classes.
Preferably, the training sample of the probabilistic neural network PNN is public database and experimental data, wherein disclosing number
It is DEAP databases according to library, experimental data is that the attention self-appraisal for the testee that attention is tested is carried out to testee
Level data.
Preferably, the ends PC program and hand are used between the brain electricity analytical subsystem and learning state display alarm subsystem
The mode that machine APP is combined feeds back attention grade to Faculty and Students, and each mobile phone terminal is wirelessly connect with the ends PC.
Preferably, the learning state display alarm subsystem includes teacher's system and student system, wherein
Teacher's system includes：
Attention of student display module, the attention of attention grade and individual student for showing current all students
Grade changes over time curve；
Low attention warning module, for judging and showing that current attention grade is less than the student of setting value；
Student system includes：
Attention of student display module, the attention grade for showing current student；
Attention warning module, for pointing out power grade is less than setting value setting time by sound-light alarm
It is raw.
The reached advantageous effect of invention：The present invention is a kind of study attention evaluation system based on EEG signals, real
The real-time monitoring of existing attention of student, solves the technical issues of student's learning state is difficult to objective evaluation；Present invention use can be worn
Formula brain wave acquisition subsystem is worn, and using the microprocessor for integrating Zigbee module, reduces the volume of brain wave acquisition subsystem
With weight, cost is reduced, convenient for wearing, there is good portability and practicability；EEG signals are divided using PNN algorithms
Class has many advantages, such as that learning process is simple, training speed is fast, classification is accurate, zmodem；Study based on physiology signal
State-detection detects the learning state of every student using external equipment, significantly reduces the teaching pressure of teacher, and evaluate knot
Fruit objective and fair；In addition for statistical analysis to the performance of student after teaching, with assisted teacher to students ' learning performance
Objective evaluation is carried out, teaching means can be continuously improved.
Description of the drawings
Fig. 1 is the system architecture diagram of the embodiment of the present invention；
Fig. 2 is brain wave acquisition subsystem structure figure；
Fig. 3 is the graphical interfaces under the connection status of the ends teacher PC；
Fig. 4 is the ends teacher PC attention display interface；
Fig. 5 show teacher's mobile phone terminal APP surface charts；
Fig. 6 show mobile phone for pupil end APP surface charts.
Reference numeral：1- fixation top beams, 2- acquire dry electrode, 3- ears reference electrode, the serial ports of 4- computer equipments
Number, the attention of the connection status of 5- student, 6- acknowledgement keys, 7- attention of student situation totality display windows, the single students of 8- is bent
Line window, the low attention early warning windows of 9-, 10- student's totality attention situation statistic window, the low attention list display windows of 11-, 12-
Raw attention assessment report window, 13- people's attention situation display window.
Specific implementation mode
Below according to attached drawing and technical scheme of the present invention is further elaborated in conjunction with the embodiments.
As shown in Figure 1, the following technical solution that the present invention uses, a kind of study attention evaluation system based on EEG signals
System, specifically includes：Brain wave acquisition subsystem, brain electricity analytical subsystem and learning state display alarm subsystem, wherein
Brain wave acquisition subsystem pre-processes analog signal, removes Hz noise and base for acquiring EEG signals
Line, and EEG signals are transmitted to brain electricity analytical subsystem；
Brain electricity analytical subsystem extracts temporal signatures and frequency domain character as attention for analyzing collected EEG signals
Feature vector is inputted attention grader, obtains attention grade by the feature vector of power classification；
The attention grade that brain electricity analytical subsystem obtains is fed back to student and religion by learning state display alarm subsystem
At teacher.
Preferably, the brain wave acquisition subsystem uses the ADS1298 chips of TI companies as AFE(analog front end), acquires top
The EEG signals of P7, P3, Pz, P4 and P8 lead；The pretreatment of EEG signals is carried out using the CC2652R microprocessors of TI companies
It is transmitted with EEG signals.
Preferably, it includes bandpass filtering and removal baseline that the brain wave acquisition subsystem, which carries out pretreatment, constructs number tape
Bandpass filter removes 50Hz Hz noises；Using least square fitting baseline, and by baseline from original EEG signals
It removes.
Preferably, the communication between the brain wave acquisition subsystem and brain electricity analytical subsystem passes through the star-like networkings of Zigbee
It realizes, brain electricity analytical subsystem is as host to brain wave acquisition subsystem request data, brain wave acquisition subsystem in a manner of poll
It unites and returns the EEG signals data of designated length after receiving instruction.
Preferably, the brain wave acquisition subsystem includes fixation top beam, includes power supply and data transmission and processing unit, number
According to transmission processing unit for receiving EEG signals data, and EEG signals are pre-processed and transmitted；Dry electrode is acquired,
Acquisition is connected to by elastomeric element on top beam with dry electrode；Ear's reference electrode acquires dry electrode and ear with reference to electricity
Pole is by the signal transmission of acquisition to data transmission and processing unit.
Preferably, the frequency domain character of the brain electricity analytical subsystem extraction is the work(of δ waves, four θ waves, α waves and β waves frequency ranges
Rate value, temporal signatures are Sample Entropy, standard deviation, first-order difference mean value and second differnce mean value, construction feature vector, the note of use
Meaning power grader is probabilistic neural network PNN, and attention grade is divided into high, medium and low three classes.
Preferably, the training sample of the probabilistic neural network PNN is public database and experimental data, wherein disclosing number
It is DEAP databases according to library, experimental data is that the attention self-appraisal for the testee that attention is tested is carried out to testee
Level data.
Preferably, pc client and hand are used between the brain electricity analytical subsystem and learning state display alarm subsystem
The mode that machine APP is combined feeds back attention grade to Faculty and Students, and mobile phone terminal is wirelessly connect with the ends PC.
Preferably, the learning state display alarm subsystem includes teacher's system and student system, wherein
Teacher's system includes：
Attention of student display module, the attention of attention grade and individual student for showing current all students
Grade changes over time curve；
Low attention warning module, for judging and showing that current attention grade is less than the student of setting value；
Student system includes：
Attention of student display module, the attention grade for showing current student；
Attention warning module, for pointing out power grade is less than setting value setting time by sound-light alarm
It is raw.
The main implementation process of the present invention is as follows：
Step 1：Brain wave acquisition subsystem acquire student's EEG signals data, after pretreatment, wirelessly to
Brain electricity analytical subsystem sends EEG signals data.
In the present embodiment, brain wave acquisition subsystem is specific as shown in Fig. 2, brain wave acquisition subsystem can acquire student's top,
Dry electrode is close to head when use, and reference electrode is sandwiched on ear-lobe.
In order to reduce the volume of brain wave acquisition subsystem, AFE(analog front end) uses the ADS1298 chips of TI companies production, tool
There are higher sampling precision and stability, sample rate is 250Hz in the present embodiment.
The pretreatment of EEG signals includes bandpass filtering and removal baseline, constructs the digital band-pass filter of 0.1-40Hz,
Remove 50Hz Hz noises；It is removed from original EEG signals signal using least square fitting baseline, and by baseline.This reality
Apply pretreatment and transmission that example realizes signal using the CC2652R microprocessors of TI companies production.The processor has ARM simultaneously
Cortex M4 cores and Zigbee communication module, also reduce cost while meeting technical need.
Step 2：Data interaction, brain electricity point are wirelessly carried out between brain wave acquisition subsystem and brain electricity analytical system
It analyses subsystem and obtains EEG signals data by reading serial ports buffer area first, the brain electricity of each lead is obtained by protocol analysis
Signal carries out time domain waveform and shows；Extraction temporal signatures and the feature vector classified as attention of frequency domain character, by feature to
Amount input attention grader, obtains attention grade, reacts student's learning state.
In the present embodiment, brain electricity analytical subsystem is set to the ends PC, the graphical interfaces under teacher's pc client connection status
As shown in Figure 3.COM shows that connected device, the connection status 5 of the serial port for selecting computer equipment, student are shown in Fig. 3
Student's connection, is not connected with as red, has connected as green, when all students complete connection, you can press acknowledgement key 6
" next step " enters attention display interface into formal detection pattern.
In order to reduce the energy consumption of system and adapt to Classroom attention detection requirement of real-time, using polling method obtain it is each
The EEG signals data of student, after brain electricity analytical subsystem and brain wave acquisition subsystem complete connection, as coordinator
Zigbee module sends out request of data to each brain wave acquisition subsystem successively at regular intervals；Brain wave acquisition subsystem
In a dormant state when not receiving request instruction, it to reduce overall power consumption, is connected to after request instruction and is waken up beginning work immediately
Make, data then turn again to dormant state after being sent completely.
The selection of characteristic of division is the key that attention classification, and the present invention is in such a way that time-domain and frequency-domain feature is combined
Complete the extraction of feature.EEG signals are extracted in four δ waves, θ waves, α waves, β waves frequencies using Fast Fourier Transform (FFT) (FFT) algorithm
The performance number of section, has the characteristics that arithmetic speed is fast.Using Sample Entropy, standard deviation with one, second differnce mean value as time domain spy
Sign, effectively reflects the time complexity of EEG signals.
After completing feature extraction, each feature composition characteristic vector is sent into attention grader, using probabilistic neural network
(PNN) it is used as attention grader, attention grade is divided into high, medium and low 3 class, using public database and experimental data phase
In conjunction with mode PNN networks are trained.Wherein, experimental data means suitable subject watches net class segment, with after class
Self-appraisal attention grade is as input；Public database then uses DEAP databases.
Step 3：The attention grade that brain electricity analytical subsystem obtains is fed back to by learning state display alarm subsystem
At raw and teacher, the attention of student display module at the setting of learning state display alarm subsystem and the ends PC, teacher's system will be noted
Meaning power grade is fed back at teacher, and Fig. 4 show teacher's pc client attention display interface, and the upper left corner can show and currently attend class
Class, click the attention grade of start button then statistic；It clicks and stops statistics when finishing class button.
Attention of student situation totality display window 7 shows the attention of student grade of current totality, attention collection middle grade
High is green, and medium is yellow, and grade is low to be red, someone seat is clicked in attention of student situation totality display window 7
The attention force curve of this person, the low attention warning module of teacher's system can be shown in the attention force curve window 8 of single student
Judge and count the low student's list of current attention grade, is shown in low attention early warning window 9, for reminding teacher to look into
See the horizontal too low student of current attention.
Fig. 5 show teacher's mobile phone terminal APP surface charts.The attention of student display module of teacher's system is by student by attention
The high, normal, basic three grades of power grade classifies and counts student's ratio of three grades, and student's totality attention situation statistic window 10 is aobvious
Show that the attention grade of all students, attention of student assessment report window 12 show student's ratio of attention three grades.Religion
The low attention warning module of teacher's system judges and counts the low student's list of current attention grade, is shown in cell phone application
Low attention list display window 11 in, remind teacher to check the horizontal too low student of current attention
Fig. 6 show mobile phone for pupil end APP surface charts.The attention of student display module of student system is by current student's
Attention grade is shown in personal attention situation display window 13.
The attention warning module of student system reminds in low attention grade setting time by sound-light alarm
It is raw.
In the present embodiment, mobile phone terminal and the connection at the ends PC are wirelessly completed, by building WiFi LANs
Form completes the transmission of data.
In addition for statistical analysis to the performance of student after teaching and evaluation is shared, is learned student with assisted teacher
It practises effect and carries out objective evaluation, teaching means can be continuously improved.
Schematically the invention and embodiments thereof are described above, description is not limiting, attached drawing
Shown in also be the invention one of embodiment, actual structure is not limited to this.So if this field
Those of ordinary skill enlightened by it, in the case where not departing from this creation objective, not inventively design and the technology
The similar frame mode of scheme and embodiment, should all belong to the protection domain of this patent.
Claims (9)
1. a kind of study attention evaluation system based on EEG signals, which is characterized in that including：Brain wave acquisition subsystem, brain
Electroanalysis subsystem and learning state display alarm subsystem, wherein
Brain wave acquisition subsystem is pre-processed for acquiring EEG signals, removes Hz noise and baseline, and by EEG signals
It is transmitted to brain electricity analytical subsystem；
Brain electricity analytical subsystem extracts temporal signatures and frequency domain character as attention point for analyzing collected EEG signals
Feature vector is inputted attention grader, obtains attention grade by the feature vector of class；
Learning state display alarm subsystem feeds back to the attention grade that brain electricity analytical subsystem obtains at student and teacher.
2. the study attention evaluation system according to claim 1 based on EEG signals, which is characterized in that the brain electricity
Acquisition subsystem uses the ADS1298 chips of TI companies as AFE(analog front end), acquires the brain of top P7, P3, Pz, P4 and P8 lead
Electric signal；The pretreatment of EEG signals is carried out using the CC2652R microprocessors of TI companies and EEG signals transmit.
3. the study attention evaluation system according to claim 1 or 2 based on EEG signals, which is characterized in that described
It includes bandpass filtering and removal baseline that brain wave acquisition subsystem, which carries out pretreatment, constructs digital band-pass filter, removes 50Hz works
Frequency interferes；It is removed from original EEG signals using least square fitting baseline, and by baseline.
4. the study attention evaluation system according to claim 1 based on EEG signals, which is characterized in that the brain electricity
Communication between acquisition subsystem and brain electricity analytical subsystem passes through the star-like networking realizations of Zigbee, brain electricity analytical subsystem conduct
For host to brain wave acquisition subsystem request data in a manner of poll, brain wave acquisition subsystem returns specified length after receiving instruction
The EEG signals data of degree.
5. the study attention evaluation system according to claim 1 based on EEG signals, which is characterized in that the brain electricity
Acquisition subsystem includes fixation top beam, includes power supply and data transmission and processing unit, data transmission and processing unit is for receiving
EEG signals data, and EEG signals are pre-processed and transmitted；Dry electrode is acquired, acquisition passes through elastic portion with dry electrode
Part is connected on top beam；Ear's reference electrode acquires dry electrode and ear's reference electrode by the signal transmission of acquisition to number
According to transmission processing unit.
6. the study attention evaluation system according to claim 1 based on EEG signals, which is characterized in that the brain electricity
Analyzing subsystem extraction frequency domain character be δ waves, four θ waves, α waves and β waves frequency ranges performance number, temporal signatures be Sample Entropy and
Standard deviation, first-order difference mean value, second differnce mean value, construction feature vector, the attention grader used is probabilistic neural net
Attention grade is divided into high, medium and low three classes by network PNN.
7. the study attention evaluation system according to claim 6 based on EEG signals, which is characterized in that the probability
The training sample of neural network PNN is public database and experimental data, and wherein public database is DEAP databases, tests number
According to carry out the self-appraisal attention level data of testee that attention is tested to testee.
8. the study attention evaluation system according to claim 1 based on EEG signals, which is characterized in that the brain electricity
Between analyzing subsystem and learning state display alarm subsystem using pc client in such a way that cell phone application is combined to teacher
With Students ' Feedback attention grade, mobile phone terminal is wirelessly connect with the ends PC.
9. the study attention evaluation system according to claim 1 based on EEG signals, which is characterized in that the study
Status display reminding subsystem includes teacher's system and student system, wherein
Teacher's system includes：
Attention of student display module, the attention grade of attention grade and individual student for showing current all students
Change over time curve；
Low attention warning module, for judging and showing that current attention grade is less than the student of setting value；
Student system includes：
Attention of student display module, the attention grade for showing current student；
Attention warning module, for pointing out the student that power grade is less than setting value setting time by sound-light alarm.
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| 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 |
| CN117158972A (en) * | 2023-11-04 | 2023-12-05 | 北京视友科技有限责任公司 | Attention transfer capability evaluation method, system, device and storage medium |
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