CN108888280A - Student based on electroencephalogramsignal signal analyzing listens to the teacher attention evaluation method - Google Patents
Student based on electroencephalogramsignal signal analyzing listens to the teacher attention evaluation method Download PDFInfo
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Abstract
It listens to the teacher attention evaluation method the invention discloses the student based on electroencephalogramsignal signal analyzing, to solve the problems, such as that student's attention concentration degree of listening to the teacher is difficult to characterize, the attention evaluation method of listening to the teacher of the student based on electroencephalogramsignal signal analyzing is：1. acquiring the EEG signals of student:1) original EEG signals are acquired；2) preposition level-one enhanced processing is carried out to original EEG signals；3) EEG signals of level-one enhanced processing are amplified again；4) amplified EEG signals are converted into digital signal；2. analyzing EEG signals：1) EEG signals Hz noise is removed；2) low-pass filtering treatment is carried out to EEG signals；3) eye electricity artefact is removed；4) feature extraction and quantization；6) Sample Entropy is quantified as attention concentration degree；3. the attention concentration degree of quantization is sent by wireless transmission device；4. receiving attention concentration degree data by radio receiver；5. storing the attention concentration degree data of a period of time；6. being presented by visualization interface.
Description
Technical field
The present invention relates to a kind of evaluation methods for belonging to Cognitive Neuroscience and information technology field, more precisely, this
Invention is related to a kind of student based on electroencephalogramsignal signal analyzing and listens to the teacher attention evaluation method.
Background technique
The variation of emotion, state and the variation of the EEG signals of itself of the mankind has a degree of association.On classroom,
Attention of student is more concentrated, and the said content of teacher can be more clearly grasped, and the variation of attention intensity is real earnestly
It is real closely bound up with the variation of EEG signals.
Brain cell group can generate rhythmic electric field fluctuation in activity.EEG signals
(Electroencephalograph, EEG) is exactly overall reflection of this electric field fluctuation in cerebral cortex or scalp surface, note
The variation of electric signal when brain activity is recorded.EEG signals are a kind of random signals, have the characteristics that non-stationary and nonlinear,
It is able to reflect the state and variation of nerve system of human body.Brain-computer interface technology (Brain Computer Interface, BCI) is just
It is cerebral neuron electrical signal pattern to be sensed, by amplification, filtering, feature extraction by acquiring EEG signals in cerebral cortex
And etc., the signal that can be interpreted is converted it into, therefrom distinguishes state, emotion, idea and the mood etc. of human body.
The frequency range of EEG signals can be divided into tetra- wave bands of δ, θ, α and β in 0-30Hz, according to different frequency, wherein δ wave
Frequency range is in 0.5-4Hz, and in 20-200 μ V, main reflection people is in the state of deep sleep or has serious organic amplitude
Brain illness；θ wave frequency range in 4-8Hz, amplitude in 10-50 μ V, it is main reflect people gently sleep state and it is sleepy when state；α wave
Frequency range is in relaxation and awake state of closing one's eyes in 50 μ V or so, main reflection people in 8-13Hz, amplitude；β wave frequency rate model
It is trapped among 13-30Hz, amplitude has very big correlation with nervous degree in 5-20 μ V, complexity, and complexity is got over
Height generally means that human brain is more awake, stress, has higher attention concentration degree.
Therefore we are listened to the teacher attention by analyzing the Assessment of Changes student of β wave complexity in EEG signals.
There are no electroencephalogramsignal signal analyzing attention concentration degree is applied to student to listen to the teacher note in existing brain-computer interface patent
In power of anticipating evaluation, current existing patented technology relate to attention under driving environment assessment (such as application No. is
CN201410381256), attention training system (such as application No. is CN201611106017.0), Test of attention system be (such as
Application No. is CN201710164162.2), and the variation of brain wave special frequency band is evaluated during being attended class by analyzing student
Student's attention of listening to the teacher is a kind of novel and efficient method, and there has been no related patents to be disclosed.
Summary of the invention
The technical problems to be solved by the invention are exactly in order to which solve that student listens to the teacher that attention concentration degree is difficult to characterize asks
Topic, and provide a kind of student based on electroencephalogramsignal signal analyzing and listen to the teacher attention evaluation method.
In order to solve the above technical problems, the present invention adopts the following technical scheme that realization：It is described based on EEG signals
The student of analysis listen to the teacher attention evaluation method the step of it is as follows：
1) EEG signals of student are acquired；
2) EEG signals are analyzed；
3) the attention concentration degree of quantization is sent by wireless transmission device；
4) attention concentration degree data are received by radio receiver；
5) the attention concentration degree data of a period of time are stored；
6) it is presented by visualization interface.
The EEG signals that student is acquired described in technical solution refer to：
(1) original EEG signals are acquired；
Wet electrode single channel acquisition mode is selected, comprising a data electrode, two reference electrodes, data electrode is placed on
The position Fpz, that is, antinion midpoint in 10-20 normal electrode placement methods as defined in international electroencephalography meeting acquires data, with reference to electricity
Pole A1 and reference electrode A2 are placed at the ear mastoid process of left and right；
(2) preposition level-one enhanced processing is carried out to original EEG signals
Since collected original EEG signals are very faint, amplitude range is in 5 μ of μ V~100 V, it is therefore desirable to its into
Row enhanced processing, gain amplifier are more much higher than general signal, generally to amplify 20000 times or so；
(3) EEG signals of level-one enhanced processing are amplified again
For prevent amplification factor it is excessively high caused by introduce noise be saturated amplifier, amplification process is divided into two stages,
Using simple amplifying circuit in the same direction in this step, the signal of preamplifier amplification is amplified again, amplification factor is
100；
(4) amplified EEG signals are converted into digital signal
Amplified simulation EEG signals are converted by A/D converter, sample frequency is set as each second 512
Sample point, the digital signal after conversion are sent into electroencephalogramsignal signal analyzing unit.
Analysis EEG signals described in technical solution refer to：
(1) EEG signals Hz noise is removed
Under conventional environment, the acquisition of EEG signals is interfered by the line voltage bring power frequency environment that frequency is 50Hz,
Influence the analysis of EEG signals；In this step, the present invention eliminates Hz noise using FIR notch filter, and stopband is set as
45 to 55Hz；
(2) low-pass filtering treatment is carried out to EEG signals
It is dry using Chebyshev I type low-pass filter removal high frequency since useful EEG signals frequency is smaller
It disturbs, cut-off frequecy of passband 50Hz；
(3) eye electricity artefact is removed；
(4) feature extraction and quantization；
(5) Sample Entropy is calculated
The sample of EEG signals is calculated using 1s length as time slip-window i.e. 512 point to EEG signals plus time slip-window
This entropy, window move 64 sampled points every time, and calculate the Sample Entropy of the EEG signals of lower 1s time window, until calculating one point
In clock time until the Sample Entropy of the EEG signals of the last 1s time window of signal, to obtain EEG signals in this section of sample data
The time series of Sample Entropy；
This group of Sample Entropy superimposition is averaged, i.e., the Sample Entropy of signal in the acquisition 1min time；
(6) Sample Entropy is quantified as attention concentration degree
The period attention concentration degree is demarcated by calculating the value of different periods β wave Sample Entropy；The quantizing process will
The value of Sample Entropy according to (0-0.5], (0.5-1.0], (1.0-1.5], (1.5-2.0] and be quantified as greater than 2.0 label1,
Label2, label3, label4, label5 Pyatyi, respectively represent low value, lower value, and normal level, high level, high value pay attention to
Power intensity is gradually increased.
Removal eye electricity artefact described in technical solution refers to：
A. overall experience mode decomposition；
B. fast independent component analysis
The fast independent component analysis algorithm of use is a kind of Independent Component Analysis based on negentropy, for a step
Obtained in each intrinsic modal components input the source signal packet for carrying out blind source separating in independent component analysis system, obtaining respectively
Include brain electric component source signal and eye electric component source signal；
C. threshold determination picks out pure brain electric component
Threshold value is set for component approximate entropy each in source signal, is judged as an electric component, entropy when entropy is greater than 0.6
Judgement less than or equal to 0.6 is brain electric component, while eye electric component is set 0, obtains EEG signals source；
D. fast independent component analysis inverse transformation
It is converted in EEG signals source obtained in above-mentioned step c to from single sheet by fast independent component analysis inverse transformation
The more pure EEG signals extracted in sign modal components；
E. all pure EEG signals obtained in Step d are summed it up
The multiple purified signal components extracted in Step d are summed it up, just realize the pretreatment of EEG signals at this time.
Overall experience mode decomposition described in technical solution refers to：
EEG signals after A/D is converted are broken down into several intrinsic mode point by overall experience mode decomposition
Amount, intrinsic modal components need to meet signal extreme value points amount is equal with zero point quantity or difference for 1 and by signal pole
The local mean value for the lower envelope that the coenvelope and minimum of big value definition define is 0 the two conditions；
Decomposable process is as follows:
It a) is white Gaussian noise N that zero standard difference is constant by mean valuei(t) it is added in input signal S (t) and makes
Si(t)=S (t)+Ni(t)
Wherein：Ni(t) noise that i-th is added is indicated；
B) S is found outi(t) all extreme points, including maximum and minimum；
C) Cubic Spline Fitting is used to extreme point, finds out upper and lower envelope curve, calculate mean value, and then finds out original
The difference h of signal and mean value；
D) judge that can the difference h of original signal and mean value meet two conditions of intrinsic modal components, if it is satisfied, inciting somebody to action
The difference h of original signal and mean value is as first intrinsic modal components；Otherwise before being carried out to the difference h of original signal and mean value
The operation of two steps, repeats this process, meets intrinsic modal components condition until kth walks, has just acquired first intrinsic mode point
Amount, finds out the difference r of original signal Yu intrinsic modal components；
E) using difference r as signal to be decomposed, above-mentioned decomposable process is carried out, until final difference r is monotonic signal
Or until only existing a pole；
Finally decomposition result is：
Wherein：Cj(t) j-th obtained of intrinsic modal components are decomposed, R (t) is to decompose obtained surplus.
Feature extraction described in technical solution refers to quantization：
A. Fast Fourier Transform (FFT)
Fast Fourier Transform (FFT) is carried out to pretreated signal, obtains frequency-region signal；
B. frequency band screens
Retain 13-30Hz frequency band signals, i.e. β wave frequency range, other frequency domains are set 0；
C. inverse fast Fourier transform
Inverse fast Fourier transform is carried out to result obtained in b step, obtains time-domain signal only comprising β wave frequency section.
Compared with prior art the beneficial effects of the invention are as follows：
Student of the present invention based on electroencephalogramsignal signal analyzing listen to the teacher attention evaluation method be by analysis student on
Class hour special frequency band cerebral cortex power information, extract the relevant EEG signals feature of attention intensity, pass through computer
Software feedback goes out student's different periods and listens to the teacher attention, solves student's attention concentration degree of listening to the teacher and is difficult to quantify and what is characterized asks
Topic provides suggestion to teachers' instruction.
Detailed description of the invention
Fig. 1 is that the student of the present invention based on electroencephalogramsignal signal analyzing listens to the teacher the showing of attention evaluation system structure composition
Meaning block diagram；
Fig. 2 is that the student of the present invention based on electroencephalogramsignal signal analyzing listens to the teacher the flow diagram of attention evaluation method；
Fig. 3 is that the student of the present invention based on electroencephalogramsignal signal analyzing EEG signals in attention evaluation method of listening to the teacher are adopted
The flow diagram of set method；
Fig. 4 is that the student of the present invention based on electroencephalogramsignal signal analyzing listens to the teacher EEG signals point in attention evaluation method
The flow diagram of analysis method；
Fig. 5 is that the student of the present invention based on electroencephalogramsignal signal analyzing listens to the teacher the analysis brain telecommunications of attention evaluation method
The flow diagram of eye electricity artefact method is removed in number step；
Fig. 6 is that the student of the present invention based on electroencephalogramsignal signal analyzing listens to the teacher the analysis brain telecommunications of attention evaluation method
The flow diagram of feature extraction and quantization method in number step；
Specific embodiment
To keep the technical problem to be solved in the present invention, technical solution and advantage clearer, below in conjunction with attached drawing and tool
The invention will be further described for body embodiment.
The present invention student listen to the teacher attention concentration degree be difficult to characterize aiming at the problem that, provide a kind of based on EEG signals point
The student of analysis listens to the teacher attention evaluation method and system.
Refering to fig. 1, the student based on electroencephalogramsignal signal analyzing listen to the teacher attention evaluation system include student side and religion
Shi Duan.
The student side is integrated into single channel brain dateline ring, is powered by rechargeable battery, student side/single channel brain dateline
Ring is integrated with eeg signal acquisition unit, electroencephalogramsignal signal analyzing unit and bluetooth wireless transmission unit.
The eeg signal acquisition unit acquires corticocerebral electric signal, and amplifies and analog-to-digital conversion, packet
Acquisition electrode, four part of preposition first stage amplifier, second amplifying circuit and A/D converter are included, the signal after conversion is passed to brain
Electric signal analytical unit；
Acquisition electrode selects single channel acquisition mode in the eeg signal acquisition unit, preferably real using wet electrode
Existing circuit communication, comprising a data electrode, two reference electrodes, data electrode is placed on as defined in international electroencephalography meeting
The position Fpz (i.e. antinion midpoint) in 10-20 normal electrode placement methods, reference electrode A1 and A2 are located at left and right ear mastoid process
Place；
Preposition first stage amplifier selects the instrument amplifier INA128 of TI company in the eeg signal acquisition unit, and 5
Foot ground connection, 3 feet and 2 feet connect differential input, and 8 feet and 1 foot connect the resistance for adjusting gain, and maximum gain is up to 10000 in this step
Times；
Second amplifying circuit is using simple amplifying circuit in the same direction, amplification factor in the eeg signal acquisition unit
100 times；
A/D converter uses 8 voltage-types of MAX548A low-power consumption of Maxim in the eeg signal acquisition unit
No. 2 analog-digital converters, sample frequency are set as 512 sample points each second；
The electroencephalogramsignal signal analyzing unit carries out the removal of Hz noise to incoming EEG signals, at low-pass filtering
Reason, removal eye electricity artefact, associated frequency band signal characteristic abstraction are simultaneously quantified as 5 attention concentration degree grades, by one piece
TMS320LF2407DSP microcontroller executes above-mentioned steps by preset program, with preferable digital signal processing capability,
Arithmetic speed is fast, expansible multiple concurrent peripheral equipment, can be reliably to EEG Processing；
10 pins of TMS320LF2407DSP microcontroller and A/D converter in the electroencephalogramsignal signal analyzing unit
Output connects；
The bluetooth wireless transmission unit selects the BT-06 bluetooth serial ports of Shenzhen Xin Taiwei Science and Technology Ltd. production
Communication module connects the synchronous serial interface (SPI) embedded in DSP microcontroller, by the quantized result of attention of student concentration degree
Real-time transmission is to teacher side；
The teacher side is embedded on computers, including bluetooth wireless receiving unit, database and visualization interface；It learns
It causes trouble and is connect between teacher side for wireless telecommunications.
The bluetooth wireless receiving unit is the Bluetooth adapter 4.0 of Shenzhen Lv Lian Science and Technology Ltd. production, is passed through
USB Universal Serial Interface is connected with teacher's computers, receives the incoming attention concentration degree of student side bluetooth wireless transmission unit
Data；
The database purchase is built in hard disc of computer, using Redis key assignments storing data library, using record file
The persistence for guaranteeing data-base recording, the attention for allowing to inquire or delete at any time certain time class hour section concentrate situation；
The visualization interface is presented by computer display screen, is write by VC++, and data in database are called, and is drawn
Attention of student concentration degree curve out.
The step of attention evaluation method such as referring to Fig.2, the student of the present invention based on electroencephalogramsignal signal analyzing listens to the teacher
Under：
1. acquiring the EEG signals of student
Refering to Fig. 3, the EEG signals for acquiring student are completed by the eeg signal acquisition unit being integrated in brain dateline ring,
Step is：
(1) original EEG signals are acquired
Wet electrode single channel acquisition mode is selected, comprising a data electrode, two reference electrodes, data electrode is placed on
The position Fpz (i.e. antinion midpoint) in 10-20 normal electrode placement methods as defined in international electroencephalography meeting acquires data, reference
Electrode A 1 and A2 are placed at the ear mastoid process of left and right；
(2) preposition level-one enhanced processing is carried out to original EEG signals；
Since collected original EEG signals are very faint, amplitude range is in 5 μ of μ V~100 V, it is therefore desirable to its into
Row multistage enhanced processing, gain amplifier is more much higher than general signal, generally to amplify 20000 times or so；
(3) EEG signals of level-one enhanced processing are amplified again；
For prevent amplification factor it is excessively high caused by introduce noise be saturated amplifier, amplification process is divided into two stages,
Using simple amplifying circuit in the same direction in this step, the signal of preamplifier amplification is amplified again, amplification factor is
100；
(4) amplified EEG signals are converted into digital signal
Amplified simulation EEG signals are converted by aforementioned A/D converter, sample frequency is set as each second
512 sample points, the digital signal after conversion are sent into electroencephalogramsignal signal analyzing unit；
2. analyzing EEG signals
Refering to Fig. 4, the step for completed by the electroencephalogramsignal signal analyzing unit being integrated in brain dateline ring, by being preset at
Program in DSP microcontroller executes, and step is：
(1) EEG signals Hz noise is removed
Under conventional environment, the acquisition of EEG signals is interfered by the line voltage bring power frequency environment that frequency is 50Hz,
Influence the analysis of electrical activity of brain signal；In this step, the present invention eliminates Hz noise, stopband using FIR notch filter
45 are set as to 55Hz；
(2) low-pass filtering treatment is carried out to EEG signals
It is dry using Chebyshev I type low-pass filter removal high frequency since useful EEG signals frequency is smaller
It disturbs, cut-off frequecy of passband 50Hz；
(3) eye electricity artefact is removed
Refering to Fig. 5, the brings eye electricity artefact such as eye movement is very common in EEG signals and drastically influences useful letter
Cease the noise extracted；In this step, disappeared using the method that overall experience mode decomposition and fast independent component analysis combine
Except eye electricity artefact obtains more pure EEG signals；Remove eye electricity artefact the step of be：
A. overall experience mode decomposition
EEG signals after A/D is converted are broken down into several intrinsic mode point by overall experience mode decomposition
Amount, intrinsic modal components need to meet signal extreme value points amount is equal with zero point quantity or difference for one and by signal pole
The local mean value for the lower envelope that the coenvelope and minimum of big value definition define is 0 the two conditions；
Decomposable process is as follows:
It a) is white Gaussian noise N that zero standard difference is constant by mean valuei(t) it is added in input signal S (t) and makes
Si(t)=S (t)+Ni(t)
Wherein：Ni(t) noise that i-th is added is indicated；
B) S is found outi(t) all extreme points, including maximum and minimum；
C) Cubic Spline Fitting is used to extreme point, finds out upper and lower envelope curve, calculate mean value, and then finds out original
The difference h of signal and mean value；
D) judge that can the difference h of original signal and mean value meet two conditions of intrinsic modal components, if it is satisfied, inciting somebody to action
The difference h of original signal and mean value is as first intrinsic modal components；Otherwise before being carried out to the difference h of original signal and mean value
The operation of two steps, repeats this process, meets intrinsic modal components condition until kth walks, has just acquired first intrinsic mode point
Amount, finds out the difference r of original signal Yu intrinsic modal components；
E) using difference r as signal to be decomposed, above-mentioned decomposable process is carried out, until final difference r is monotonic signal
Or until only existing a pole；
Finally decomposition result is：
Wherein：Cj(t) j-th obtained of intrinsic modal components are decomposed, R (t) is to decompose obtained surplus.
B. fast independent component analysis
Here the fast independent component analysis algorithm used is a kind of Independent Component Analysis based on negentropy, for a
Each intrinsic modal components input respectively obtained in step carries out blind source separating in independent component analysis system, obtained source letter
Number include brain electric component source signal and eye electric component source signal；
C. threshold determination picks out pure brain electric component
Threshold value is set for component approximate entropy each in source signal, is judged as an electric component, entropy when entropy is greater than 0.6
Judgement less than or equal to 0.6 is brain electric component, while eye electric component is set 0, obtains EEG signals source；
D. fast independent component analysis inverse transformation
It is converted in EEG signals source obtained in step c to from single eigen mode by fast independent component analysis inverse transformation
The more pure EEG signals extracted in state component；
E. all pure EEG signals obtained in Step d are summed it up
The multiple purified signal components extracted in Step d are summed it up, just realize the pretreatment of EEG signals at this time；
(4) feature extraction and quantization；
Refering to Fig. 6, the brain electrical feature extraction process of attention of student concentration degree be still in DSP microcontroller above-mentioned into
Row realizes the processing of signal by plug-in；The present invention is extracted and attention concentration degree phase by Fast Fourier Transform (FFT)
β wave (i.e. 13-30Hz) EEG signals of pass, step are：
A. Fast Fourier Transform (FFT)；
Fast Fourier Transform (FFT) is carried out to pretreated signal, obtains frequency-region signal；
B. frequency band screens；
Retain 13-30Hz frequency band signals, i.e. β wave frequency range, other frequency domains are set 0；
C. inverse fast Fourier transform；
Inverse fast Fourier transform is carried out to result obtained in b step, obtains time-domain signal only comprising β wave frequency section；
(5) Sample Entropy is calculated
EEG signals are calculated using 1s length as time slip-window (i.e. 512 points) to EEG signals plus time slip-window
Sample Entropy, window move 64 sampled points every time, and calculate the Sample Entropy of the EEG signals of lower 1s time window, until calculating one
In minutes until the Sample Entropy of the EEG signals of the last 1s time window of signal, to obtain this section of sample data midbrain telecommunications
The time series of number Sample Entropy；
By this group of sample sequence superposed average, i.e., the Sample Entropy of signal in the acquisition 1min time；
(6) Sample Entropy is quantified as attention concentration degree
The period attention concentration degree is demarcated by calculating the value of different periods β wave Sample Entropy；The quantizing process will
The value of Sample Entropy according to (0-0.5], (0.5-1.0], (1.0-1.5], (1.5-2.0] and be quantified as greater than 2.0 label1,
Label2, label3, label4, label5 Pyatyi, respectively represent low value, lower value, and normal level, high level, high value pay attention to
Power intensity is gradually increased；
3. the attention concentration degree of quantization is sent by wireless transmission device
BT-06 bluetooth module has been used in this step, as the external equipment of the DSP microcontroller described in step 2,
To realize the wireless transmission of attention concentration degree；
4. receiving attention concentration degree data by radio receiver
The bluetooth wireless receiving unit of teacher side is Bluetooth adapter 4.0, passes through USB serial ports phase between computer
Even, after the bluetooth wireless receiving unit pairing of student side bluetooth wireless sending module and the insertion of teacher side computer, attention
The quantized result of concentration degree is by wireless real-time transmission.
5. storing the attention concentration degree data of a period of time
Data are stored in real time in the key assignments storing data library of computer-internal built, and key assignments storing data library is using note
The persistence that file guarantees data-base recording is recorded, the attention for allowing to inquire certain time class hour section at any time concentrates situation, data
Key is 1,2...n...45 in library, indicates n-th minute in a class hour, and corresponding value concentrates measurement for n-th minute attention
Change result.
6. being presented by visualization interface
A kind of student based on electroencephalogramsignal signal analyzing of the present invention listen to the teacher attention evaluation method teacher side it is visual
Change interface to be write by VC++, calls data in database, draw out attention of student concentration degree curve.Interface function is as follows：It can
The data stored in one class hour (i.e. 45 minutes) interior database are drawn out into attention of student concentration degree curve depending on changing software, are passed through
Computer software feeds back student's different periods out and listens to the teacher attention, and attention concentration degree is that the period curve of high level and high value is
Green, concentration degree, which is designated as red and overstriking for the period curve of low value and lower value, to be indicated, to remind teacher in the red period
Awarded content and method are thought deeply.
It listens to the teacher attention evaluation method the present invention provides a kind of student based on electroencephalogramsignal signal analyzing, passes through analysis student
The brain wave information of upper class hour special frequency band extracts the relevant brain wave feature of attention intensity, passes through computer software
It feeds back student's different periods out to listen to the teacher attention, solves the problems, such as that student's attention concentration degree of listening to the teacher is difficult to quantify and characterize,
Suggestion is provided to teachers' instruction.
Claims (6)
- The attention evaluation method 1. a kind of student based on electroencephalogramsignal signal analyzing listens to the teacher, which is characterized in that described based on brain electricity Signal analysis student listen to the teacher attention evaluation method the step of it is as follows：1) EEG signals of student are acquired；2) EEG signals are analyzed；3) the attention concentration degree of quantization is sent by wireless transmission device；4) attention concentration degree data are received by radio receiver；5) the attention concentration degree data of a period of time are stored；6) it is presented by visualization interface.
- The attention evaluation method 2. the student described in accordance with the claim 1 based on electroencephalogramsignal signal analyzing listens to the teacher, which is characterized in that The EEG signals of the acquisition student refer to：(1) original EEG signals are acquiredWet electrode single channel acquisition mode is selected, comprising a data electrode, two reference electrodes, data electrode is placed on the world The position Fpz, that is, antinion midpoint in 10-20 normal electrode placement methods as defined in electroencephalography meeting acquires data, reference electrode A1 It is placed at the ear mastoid process of left and right with reference electrode A2；(2) preposition level-one enhanced processing is carried out to original EEG signalsSince collected original EEG signals are very faint, amplitude range is in 5 μ of μ V~100 V, it is therefore desirable to put to it Big processing, gain amplifier is more much higher than general signal, generally to amplify 20000 times or so；(3) EEG signals of level-one enhanced processing are amplified againTo prevent the excessively high caused noise that introduces of amplification factor from be saturated amplifier, amplification process is divided into two stages, this step Using simple amplifying circuit in the same direction in rapid, the signal of preamplifier amplification is amplified again, amplification factor 100；(4) amplified EEG signals are converted into digital signalAmplified simulation EEG signals are converted by A/D converter, sample frequency is set as 512 samples each second Point, the digital signal after conversion are sent into electroencephalogramsignal signal analyzing unit.
- The attention evaluation method 3. the student described in accordance with the claim 1 based on electroencephalogramsignal signal analyzing listens to the teacher, which is characterized in that The analysis EEG signals refer to：(1) EEG signals Hz noise is removedUnder conventional environment, the acquisition of EEG signals is interfered by the line voltage bring power frequency environment that frequency is 50Hz, is influenced The analysis of EEG signals；In this step, the present invention eliminates Hz noise using FIR notch filter, stopband be set as 45 to 55Hz；(2) low-pass filtering treatment is carried out to EEG signalsSince useful EEG signals frequency is smaller, High-frequency Interference is removed using Chebyshev I type low-pass filter, is led to Band cutoff frequency is 50Hz；(3) eye electricity artefact is removed(4) feature extraction and quantization(5) Sample Entropy is calculatedThe sample of EEG signals is calculated using 1s length as time slip-window i.e. 512 point to EEG signals plus time slip-window Entropy, window move 64 sampled points every time, and calculate the Sample Entropy of the EEG signals of lower 1s time window, until calculating one minute In time until the Sample Entropy of the EEG signals of the last 1s time window of signal, to obtain EEG signals sample in this section of sample data The time series of this entropy；This group of Sample Entropy superimposition is averaged, i.e., the Sample Entropy of signal in the acquisition 1min time；(6) Sample Entropy is quantified as attention concentration degreeThe period attention concentration degree is demarcated by calculating the value of different periods β wave Sample Entropy；The quantizing process is by sample The value of entropy according to (0-0.5], (0.5-1.0], (1.0-1.5], (1.5-2.0] and be quantified as greater than 2.0 label1, label2, Label3, label4, label5 Pyatyi, respectively represent low value, lower value, normal level, high level, high value, and attention is concentrated Degree is gradually increased.
- The attention evaluation method 4. the student described in accordance with the claim 3 based on electroencephalogramsignal signal analyzing listens to the teacher, which is characterized in that The removal eye electricity artefact refers to：A. overall experience mode decomposition；B. fast independent component analysisThe fast independent component analysis algorithm of use is a kind of Independent Component Analysis based on negentropy, for obtaining in a step To each of intrinsic modal components input carry out blind source separating in independent component analysis system respectively, obtained source signal includes brain Electric component source signal and eye electric component source signal；C. threshold determination picks out pure brain electric componentThreshold value is set for component approximate entropy each in source signal, is judged as that an electric component, entropy are less than when entropy is greater than 0.6 Judgement equal to 0.6 is brain electric component, while eye electric component is set 0, obtains EEG signals source；D. fast independent component analysis inverse transformationIt is converted in EEG signals source obtained in above-mentioned step c to from single eigen mode by fast independent component analysis inverse transformation The more pure EEG signals extracted in state component；E. all pure EEG signals obtained in Step d are summed it upThe multiple purified signal components extracted in Step d are summed it up, just realize the pretreatment of EEG signals at this time.
- 5. the student based on electroencephalogramsignal signal analyzing listens to the teacher attention evaluation method according to claim 4, which is characterized in that The overall experience mode decomposition refers to：EEG signals after A/D is converted are broken down into several intrinsic modal components by overall experience mode decomposition, this Sign modal components need to meet that signal extreme value points amount is equal with zero point quantity or difference is 1 and is determined by the maximum of signal The local mean value for the lower envelope that the coenvelope and minimum of justice define is 0 the two conditions；Decomposable process is as follows:It a) is white Gaussian noise N that zero standard difference is constant by mean valuei(t) it is added in input signal S (t) and makesSi(t)=S (t)+Ni(t)Wherein：Ni(t) noise that i-th is added is indicated；B) S is found outi(t) all extreme points, including maximum and minimum；C) Cubic Spline Fitting is used to extreme point, finds out upper and lower envelope curve, calculates mean value, and then find out original signal With the difference h of mean value；D) judge that can the difference h of original signal and mean value meet two conditions of intrinsic modal components, if it is satisfied, by original Signal and the difference h of mean value are as first intrinsic modal components；Otherwise first two steps are carried out to the difference h of original signal and mean value Operation repeats this process, meets intrinsic modal components condition until kth walks, has just acquired first intrinsic modal components, asked The difference r of original signal and intrinsic modal components out；E) using difference r as signal to be decomposed, carry out above-mentioned decomposable process, until final difference r be monotonic signal or Until only existing a pole；Finally decomposition result is：Wherein：Cj(t) j-th obtained of intrinsic modal components are decomposed, R (t) is to decompose obtained surplus.
- The attention evaluation method 6. the student described in accordance with the claim 3 based on electroencephalogramsignal signal analyzing listens to the teacher, which is characterized in that The feature extraction refers to quantization：A. Fast Fourier Transform (FFT)Fast Fourier Transform (FFT) is carried out to pretreated signal, obtains frequency-region signal；B. frequency band screensRetain 13-30Hz frequency band signals, i.e. β wave frequency range, other frequency domains are set 0；C. inverse fast Fourier transformInverse fast Fourier transform is carried out to result obtained in b step, obtains time-domain signal only comprising β wave frequency section.
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