CN107280663A - A kind of method of the tired brain electrical feature research based on different experiments difficulty - Google Patents
A kind of method of the tired brain electrical feature research based on different experiments difficulty Download PDFInfo
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Abstract
The invention discloses a kind of method of the tired brain electrical feature research based on different experiments difficulty.Mental fatigue is a kind of gradual, accumulation phenomenon, typically shows the decrease of human physiological activity.It is of the invention to induce the electricity of the tired brain under different conditions by setting the Stroop of two groups of different difficulty to test, signature analysis is carried out to the EEG signals under clear-headed and fatigue state in two groups of experiments using WAVELET PACKET DECOMPOSITION and Sample Entropy algorithm.Test result indicates that：From regaining consciousness under fatigue state, α ripples and the increase of θ ripples relative energy, β ripple relative energies significantly reduce (P<0.05), parameter α/β, (α+θ)/β ratio (P<0.05) as the intensification of fatigue gradually increases, the sample entropy of each brain area is in reduction trend；And compared to more highly difficult experimental group, Parameters variation in low difficulty task becomes apparent, therefore, parameter α/β, (α+θ)/β can be as the potential index of mental fatigue be weighed, while can verify that appropriate increase experiment difficulty can resist the generation of mental fatigue to a certain extent.
Description
Technical field
A kind of properties study method of the tired brain electricity based on different experiments difficulty of present invention design.Refer specifically to, design two
Change of the Experiment of Psychology of the different difficulty of group to induce tired brain electricity under the tired brain electricity of human body, analysis different experiments difficulty becomes
Gesture, the invention belongs to the combination of Cognitive Neuroscience and areas of information technology, belongs to digital signal processing technique field.
Background technology
Physiological central fatigue turns into the major issue in city, seriously threatens the healthy and life of people
Property safety.In the work such as traffic driving, aerospace operations, man-machine system monitoring, caused by operator's mental fatigue
Dispersion attention, delay of response or the harmony of moment not enough, may all cause extremely serious accident.Therefore, tired brain electricity
Analysis and prevention work just become particularly important.
The decision method of spirit mental fatigue is roughly divided into two classes at present：Subjective assessment method and objective evaluation method.Subjectivity is commented
The form for determining the main questionnaire by inquiry of method is carried out, and this assessment method can provide the much information of mental fatigue, and such as spirit is tired
Whether labor produces, generation time, Producing reason and degree of fatigue, it is conventional have the tired scales of Piper, Ep-worth is drowsiness
Scale and the tired scales of Stanford etc..But subjective assessment method is easily influenceed by researcher's subjective judgement ability, in detection fatigue
There is certain limitation during state, and standards of grading are difficult unification, cause detecting system consistently can not correctly report
Driver fatigue state.The general applied mental index evaluation method of objective evaluation method, measurement-electroencephalogram of such as brain signal
(Electroenceophalograpy, EEG), eye movement signals measurement-electroculogram (Electroculography,
EOG) and heart rate and heart rate variability signals (HRV) measurement-electrocardiogram (Electrocardiograph, ECG).At this
In a little physiological signal parameters, EEG signals are considered as to analyze EEG signals most due to the correlated activation of straight reflection brain
Reliable index, is widely used in every research of mental fatigue.EEG signal is by collecting the electrode being placed on scalp
Potential change reflects corticocerebral movable important physiological signal.Due to the significant relation with cognitive stimulation, EEG signal is
It is assessed as detecting one of most suitable method of mental fatigue caused by Cognitive task.
EEG signals are to reflect one of optimal parameter of brain activity, and the most critical for analyzing EEG signals is brain telecommunications
Number feature extraction.The feature extracting method of current EEG signals has a lot, be broadly divided into temporal analysis, frequency domain analysis and
Nonlinear analysis method.Fast Fourier Transform (FFT) FFT (Fast Fourier Transform), autoregression model AR
(Autoregressive), power spectral density PSD (Power Spectrum Density) belongs to frequency-domain analysis method, passes through
The EEG signals that amplitude is changed over time are transformed into the spectrogram that electroencephalogram power changes with frequency, so as to extract EEG signals
Frequency domain character.But the shortcoming of these methods is to be only suitable for analyzing stationary signal, for analysis this kind of non-stationary of EEG signals
Signal has significant limitation.Wavelet transformation belongs to Time Domain Analysis, inherits and developed Fourier's change in short-term local
The thought of change, with multi-resolution characteristics, the local message of non-stationary signal can be analyzed well.Sample Entropy is a kind of typical
Nonlinear analysis method, reflects its nonlinear characteristic by measuring the complexity of EEG signals, using a nonnegative number table
Show the generation rate of fresh information in the complexity of time series, reaction time sequence, time series is more complicated, and sample entropy is bigger.
The content of the invention
The need under existing multitask visual information to cognitive features research, the present invention is analyzed using WAVELET PACKET DECOMPOSITION
Two groups of different lower waking states of difficulty Stroop experiments to the α ripples, β ripples, θ ripples of fatigue state energy ratio parameter α/β, (α+
θ)/β variation tendency, analyzes the complexity variation tendency that different difficulty test lower EEG signals, so as to probe into using Sample Entropy
The feasibility of EEG characteristic evaluation EEG signals is utilized in tired induction task, and different task difficulty is to EEG parameters and fatigue
The influence of generation.
To achieve these goals, the present invention is adopted the following technical scheme that：
A kind of properties study method of the tired brain electricity based on different experiments difficulty, comprises the following steps：
(1) in the implementation gap of each experiment, the Subjective fatigue degree of subject is recorded；
(2) reaction time and reaction accuracy reacted subject's button in experiment carry out calculating analysis；
(3) EEG signals collected are pre-processed, linearly corrected using low pass filter, remove baseline drift
Move and Hz noise；
(4) pretreated EEG signals in (3) are used with Independent Component Analysis Algorithm, the electric artefact of eye is removed；
(5) WAVELET PACKET DECOMPOSITION and reconstruct are carried out to removing the EEG signals after the electric artefact of eye, calculates α, β, the energy of θ frequency bands
Amount, and analysing energy parameter F values and R values；
(6) EEG signals for removing the electric artefact of eye are carried out with Sample Entropy analysis, the change of the electric complexity of analysis brain.
Further improve of the invention is：The Subjective fatigue evaluation of subject uses subjective tired scale in step (1),
The degree of Subjective fatigue is represented with 0-10 score values；
Further improve of the invention is：The experimental program implemented in step (1) is two groups of different difficulty
Stroop is tested, and utilizes the relative program of two groups of experiments of E-Prime software programmings；
Further improve of the invention is：Experiment, which implements details, in step (2) is：In every group of experiment, computer
Bounce shows the font of different colours on display, and subject presses corresponding button by font color, and computer is simultaneously
Record reaction time and the reaction accuracy of subject；
Further improve of the invention is：Using being analyzed in SPSS13.0 under two groups of different difficulty experiments, from regain consciousness to
The reaction time of subject and reaction accuracy, are averaging to it and carry out paired-samples T-test during fatigue state；
Further improve of the invention is：The 10- that the placement of brain scalp electrode can be demarcated using international electroencephalography
20 electrode lead localization criterias, with the vertical connection of ears, injection conductive paste wears the electric cap of brain to strengthen the electric conductivity of electrode, correctly.
EEG signals are gathered in real time by Neuroscan64 brains electric equipment in experiment, and are amplified by amplifier, modulus
Conversion, is output in computer；
Further improve of the invention is：Pretreatment in step (3) is to utilize the EEGLAB in Matlab softwares soft
Part bag carries out related pretreatment to original brain electricity, and original brain electricity contains much noise and artifacts, filtered by low pass filter
Except Hz noise, and remove baseline drift；
Further improve of the invention is：For the electric artefact of a large amount of eyes in original brain electricity in step (4), using independence
PCA algorithm is removed accordingly；
Further improve of the invention is：Processing method in step (5) is：Pretreated EEG signals are using small
Ripple bag decomposition algorithm is decomposed and reconstructed, and calculates α, β, the energy of θ frequency bands, and is analyzed under the different difficulty experiments of two groups of height,
The variation tendency of energy parameter F values (α/β) and R values ((α+θ)/β) during from regaining consciousness fatigue state；
Further improve of the invention is：Processing method in step (6) is：Calculate high using Sample Entropy parser
Under low two kinds different difficulty experiments, the brain of subject electric complexity variation tendency during from regaining consciousness fatigue state.
Beneficial effect
In the prior art, EEG signals induce the single psychology task that uses, or various forms of multitasks reality
Test, do not take into full account in same type task, the influence that different task difficulty is induced fatigue, the beneficial effects of the invention are as follows：
For the Stroop experiments of single type, two groups of different difficulty experiment-low difficulty groups and highly difficult group are devised.In low difficulty
In degree group, what font and color and the meaning of word were consistent with；In highly difficult group, the color and the meaning of word of font are contradicted.It is logical
Cross and perform two groups of experiments of different difficulty to analyze the different influences for inducing fatigue of experiment difficulty.
Two kinds of algorithms are analyzed in electroencephalogramsignal signal analyzing part also in relation with WAVELET PACKET DECOMPOSITION and Sample Entropy, from different analytic angles
Carry out objective description in the experiment of different difficulty, from the overall variation trend for EEG signals of regaining consciousness under fatigue state.
Brief description of the drawings
Fig. 1 is the general frame figure of tired brain wave acquisition and analysis.
Fig. 2 is the passage lead schematic diagram of EEG signals.
Font Show Styles figure in the Stroop experiments of two groups of Fig. 3 positions.
Fig. 4 is the variation tendency of subject's Subjective fatigue value.
Fig. 5 for subject's button reaction time and reaction accuracy change schematic diagram, (a) be subject's reaction time with
Time trend figure (b) is that subject's average response accuracy changes over time tendency chart.
Fig. 6 is EEG signals oscillogram before and after pretreatment；(a) it is that original EEG signals EEG (b) is clear-headed under waking state
EEG signals EEG under state after baseline correction.
Fig. 7 is WAVELET PACKET DECOMPOSITION schematic diagram.
Fig. 8 is the change schematic diagram of ratio parameter F values of regaining consciousness in height difficulty is tested under fatigue state；(a) it is low difficulty
Regain consciousness under the fatigue state changing trend diagram (b) of F values of degree group experiment is that highly difficult group of experiment is regained consciousness under fatigue state F values
Changing trend diagram.
Fig. 9 is the change schematic diagram of ratio parameter R values of regaining consciousness in height difficulty is tested under fatigue state；(a) it is low difficulty
Regain consciousness under the fatigue state changing trend diagram (b) of R values of degree group experiment is that highly difficult group of experiment is regained consciousness under fatigue state R values
Changing trend diagram.
Embodiment
To make the purpose of the present invention, technical scheme and advantage are clearer, below in conjunction with accompanying drawing to embodiment party of the present invention
Case is further elaborated.
As shown in figure 1, the figure is the overall plan of this experiment, experimental design part use classical Experiment of Psychology-
Stroop is tested, and is tested using the Stroop of two kinds of different difficulty of E-Prime Software for Design, operation is on computers.Tested
Cheng Zhong, subject carries out the stroop experiments of height difficulty group respectively, and software passes through button feedback record subject's reaction time
With reaction accuracy, in each experiment gap, subject's Subjective fatigue value is recorded.Neuroscan64 equipment is used for gathering experiment
EEG signals in whole process are used for subsequent analysis.Analysis of experimental data part：Subjective fatigue data and behavioral data (reaction
Time and reaction accuracy) SPSS13.0 software paired-samples T-tests are used, original EEG signals are input in Matlab softwares, made
Handled with EEGLAB kits：First carry out LPF and go baseline drift, related eye is being removed using Independent Component Analysis Algorithm
Electric artefact；Pretreated EEG signals carry out WAVELET PACKET DECOMPOSITION and Sample Entropy analysis respectively, study two groups of different difficulty experiments
In from the variation tendency of tired brain electricity of regaining consciousness under fatigue state.
As shown in Fig. 2 the figure is brain Scalp Potential laying method.The present invention can be demarcated using international electroencephalography
10-20 electrode lead localization criterias, with the vertical connection of ears, injection conductive paste wears brain electricity to strengthen the electric conductivity of electrode, correctly
Cap.Ten passages of FP1, FP2, F7, F8, T7, T8, C3, C4, O1, O2 of Different brain region are selected to place electrode, to gather different brains
The EEG signals in area, passage M1, M2 regard reference electrode.
Fig. 3 is the card figure of the Stroop experiments of two kinds of different difficulty, upper four are low difficulty group card, wherein card
Font color is consistent with the meaning of word：I.e. the color of " red " word is red, and the color of " Huang " word is Huang, and the color of " indigo plant " word is indigo plant,
The color of " green " word is green；Lower four are highly difficult group of card, and the color and the meaning of word of wherein font are contradicted：That is " red " word
Color be it is any in yellow, green, basket, i.e., the color of " Huang " word be it is any in red, green, blue, i.e., the color of " green " word be Huang,
Any in red, basket, i.e., the color of " indigo plant " word is any in yellow, green, red.Test in implementation process, subject is according to interface
The color of card is shown to press corresponding button, rather than by the meaning of word come button.
As shown in Figure 4：The line chart shows subject in whole experiment process, and the mean change of Subjective fatigue value becomes
Gesture.Each experimental period is about a hour, 12 sections is divided into, every time is 5 minutes, in each experiment gap, record
The Subjective fatigue value of subject, finally counts the mean subjective fatigue data variation tendency of all subjects.Shown in Fig. 5 for subject
The change broken line of person's behavioral data.In experimentation, E-Prime softwares feed back according to button, the button reaction of record subject
Time and reaction accuracy, the figure illustrates in two groups of different difficulty experimentations, the change of reaction time and reaction accuracy
Trend.
As shown in fig. 6, the EEG signals that electroencephalogramsignal signal collection equipment is collected need to use the EEGLAB works in Matlab
Tool bag carries out correlated characteristic analysis.It is the EEG signals display waveform in EEGLAB in figure.(a) figure is untreated in the figure
Original EEG signals, it can be seen that have obvious baseline drift phenomenon, (b) figure is to have used low pass filter (0-0.5HZ) to filter
EEG signals waveform afterwards, it can be seen that baseline drift phenomenon is removed, waveform has obtained linear correction well.Remove base
The EEG signals of line drift also need to remove the electric noise signal such as artefact and EMG of corresponding eye with Independent Component Analysis Algorithm,
So as to obtain preferable EEG signals.
As shown in Figure 7：Because EEG signal is time-varying non-stationary signal, select a kind of suitable method preferably to obtain
The effective information of reflection brain activity and physiological status is an important prerequisite for carrying out EEG analyses.The present invention uses wavelet packet
Decomposition technique carries out feature extraction to signal.This method is the popularization of wavelet decomposition, with it is any multiple dimensioned the characteristics of, and gram
The defect that frequency resolution can be reduced with the rise of signal frequency in wavelet decomposition has been taken, signal can have been carried out finer
Analysis.The present invention carries out 3 layers of WAVELET PACKET DECOMPOSITION to the EEG signals of pretreatment, finally obtains 8 subband frequency ranges.
As shown in Figure 8 and Figure 9, the brain electricity of normal person is mainly made up of four kinds of frequency contents：α ripples：8-13HZ, β ripple：14-
30HZ, θ ripple：4-8HZ, δ ripple：0.5-4HZ.α ripples are a kind of slow waves, are to determine the reference wave of the electric speed of brain, are generally present in flat
In the state of quiet, eye closing is clear-headed, when eye opening, sleepy or sleep, α ripples can be reduced；The cerebral cortex that β wave tables are leted others have a look at is in emerging
Put forth energy state, the fast wave belonged in EEG signals；θ ripples and δ ripples belong to slow wave, occur when human body is in sleep state.Typically
Think that fast wave is gradually decreased, and slow wave gradually increases from during Normal Conscious state changes to fatigue state.In the present invention two
In the experiment of the different difficulty of group, from regaining consciousness under fatigue state, α, β, the energy of θ wave bands changes, wherein ratio parameter F values
The overall variation block diagram of (α/β) and R values ((α+θ)/β) in Different brain region can be obtained in figure.The energy value of three wave bands can
Drawn by following algorithm：
Source signal is represented with f (t), after wavelet decomposition, 2 are obtained in the i-th decomposition layeriIndividual sub-band, therefore source signal f
(t) it is represented by:
In formula：J=0,1 ... .2i-1,fi,j(tj) it is reconstruction signal of the WAVELET PACKET DECOMPOSITION on the i-th node layer (i, j).
Can be calculated according to Parseval theorems and formula (1) obtain signal f (t) WAVELET PACKET DECOMPOSITIONs energy spectrum it is as follows：
In formula：Ei,j(tj) for the frequency band energy in f (t) WAVELET PACKET DECOMPOSITIONs to node (i, j)；xj,k(j=0,1,
2....2i- 1, k=1,2....m) it is reconstruction signal fi,j(tj) discrete point amplitude；M counts for signal sampling.
The frequency band energy value of each node is obtained, wavelet packet entropy just can be calculated：
Wherein PjFor each node energy value ratio value shared in energy summation, WE is the small echo entropy of each frequency band.
The present invention also using the different difficulty of two groups of Sample Entropy Algorithm Analysis real in addition to using wavelet packet decomposition algorithm
In testing, the change of electric complexity from regaining consciousness fatigue state hypencephalon.Sample Entropy is a kind of new time sequence proposed by Richman
Row complexity measure method, is a kind of innovatory algorithm of approximate entropy, and less data volume is needed than approximate entropy.Specific algorithm is realized
Step is as follows：
1. given one-dimensional discrete time series is set, one group of m n dimensional vector n is configured to, from Xm(1) X is arrivedm(N-m+1), wherein：
Xm(i)=[ui,ui+1,ui+2....ui+m-1] (i=1~N-m+1)
2. defining the distance between any two m dimensional vectors is：
d[Xm(i),Xm(j)]=max | ui+k-uj+k|,0≤k≤m-1；I, j=1~N-m+1, i ≠ j
3. given threshold value r, to each i values, counts d [X (i), X (j)]<R numberThen itself and distance sum are calculated
(N-m) ratio (being referred to as template matches number), note：
Wherein, SD is the standard deviation of one-dimensional discrete time series；
4. calculate：
5. for m+1 point vectors, by above step, it can obtain：
6. the sample entropy of this sequence is in theory：SampEn (m, r)=lim {-ln [Bm+1(r)/Bm(r) sequence] }, is worked as
When length is finite value, its sample Entropy estimate is：SampEn (m, r)=- ln [Bm+1(r)/Bm(r)]
Wherein parameter m is previously selected pattern dimension, and r is previously selected similar tolerance limit.
Specific sample value changes are referring to following table：
Low difficulty group Sample Entropy mean variation
Highly difficult group of Sample Entropy mean variation
Upper two tables show sample changes of entropy of the clear-headed state to tired state in the two groups of experiments of height difficulty, it can be seen that
The electric complexity change situation of the brain of subject.
The present invention is analyzed in two groups of different difficulty experiments from different perspectives, from the tired brain Electrical change under fatigue of regaining consciousness
Trend：It can be seen that, compared to more highly difficult group experiment, low difficulty group experiment is more easy to induce the generation of human-body fatigue, increase experiment is difficult
Degree can suppress the generation of fatigue to a certain extent.Though the different difficulty that the present invention take into account single task role are set, at present
Only there is provided two grade of difficulty, are not provided with multiple grade of difficulty, subsequent experimental can take into full account multiple grade of difficulty to fatigue
The influence that brain electricity induces.
Although the present invention is disclosed as above with preferred embodiment, embodiment is not for limiting the present invention's.Not
In the spirit and scope for departing from the present invention, any equivalent transformation done or retouching also belong to the protection domain of the present invention.Cause
The content that this protection scope of the present invention should be defined using claims hereof is standard.
Claims (7)
1. a kind of method of the tired brain electrical feature research based on different experiments difficulty, it is characterised in that comprise the following steps：
(1) scalp electrode of each brain area is placed：The brain electricity of corresponding brain area is gathered using each position electrode of brain scalp is placed in
Signal, and it is amplified, analog-to-digital conversion, with data signal storage in a computer；
(2) subject completes related key task in visual stimulus experiment, record in different experiments difficulty subject from clear
The EEG signals waken up under fatigue state, and corresponding subjective data and behavioral data；
(3) subjective data and behavioral data to record is averaging and paired-samples T-test, and the EEG signals collected are located in advance
Reason, baseline drift is removed using low pass filter, and independent component analysis ICA removes the electric artefact of eye；
(4) WAVELET PACKET DECOMPOSITION is carried out to pretreated EEG signals, calculates the energy of each frequency range, and analyze its ratio parameter F
With R values；
(5) Sample Entropy analysis is carried out to pretreated EEG signals, calculates the brain from EEG signals of regaining consciousness under fatigue state
Electric complexity variation tendency；
(6) subjective data, behavioral data and F values (α/β), R values ((α+θ)/β) after analyzing and processing, sample entropy, to analyze
The influence that just two kinds of different experiment difficulty are induced fatigue, analyzes its corresponding rule.
2. according to the method described in claim 1, it is characterised in that the specific placement side of each brain area scalp electrode in step (1)
Method is：The 10-20 electrode lead localization criterias that can be demarcated using international electroencephalography, are hung down connection with ears, record lead FP1,
FP2, F7, F8, T7, T8, C3, C4, O1, O2, wherein reference electrode choose M1, M2, and sample frequency is 125HZ, each passage lead
Impedance is respectively less than 5k Ω；EEG signals are gathered using Neuroscan64 equipment and are amplified and analog-to-digital conversion, then are input to meter
In calculation machine.
3. according to the method described in claim 1, it is characterised in that the visual stimulus experimental design strategy in step (2) is：Make
With visual stimulus experiment-Stroop experiments of the different difficulty of E-Prime Software for Design, subject carries out the test of two groups of experiments,
Record its subjective data and respective behavior data；
Described visual stimulus experiment is mainly：Human body is induced by designing the Stroop stimulation tests of two groups of different difficulty
Tired brain electricity.Low difficulty experiment is rolled for the card of the bluish-green four kinds of colors of reddish yellow on screen, and the color and the meaning of word of font are kept
Unanimously；Highly difficult group of experiment is that the bluish-green four kinds of color cards of reddish yellow that font color and the meaning of word are contradicted are rolled on screen.Quilt
Examination person by the card that is rolled on view screen, pressed respectively according to the color of font the keyboard corresponding with four kinds of colors by
Key.
4. according to the method described in claim 1, it is characterised in that the data analysis specific method in step (3) is：Use
SPSS13.0 softwares are averaging to subjective data and behavioral data and carry out paired t-test, and the original EEG signals of collection are entered
Row LPF removes baseline drift, linear correction, and independent component analysis removes the electric artefact of eye.
5. according to the method described in claim 1, it is characterised in that pretreated EEG signals carry out small echo in step (4)
Bag is decomposed, and specific decomposition algorithm is as follows：
Source signal is represented with f (t), after wavelet decomposition, 2 are obtained in the i-th decomposition layeriIndividual sub-band, therefore source signal f (t) can table
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In formula：J=0,1 ... .2i-1,fi,j(tj) it is reconstruction signal of the WAVELET PACKET DECOMPOSITION on the i-th node layer (i, j)；According to
Parseval theorems and formula (1) can calculate obtain signal f (t) WAVELET PACKET DECOMPOSITIONs energy spectrum it is as follows：
<mrow>
<msub>
<mi>E</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>t</mi>
<mi>j</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>=</mo>
<munder>
<mo>&Integral;</mo>
<mi>&Gamma;</mi>
</munder>
<mo>|</mo>
<msub>
<mi>f</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>t</mi>
<mi>j</mi>
</msub>
<mo>)</mo>
</mrow>
<msup>
<mo>|</mo>
<mn>2</mn>
</msup>
<mi>d</mi>
<mi>t</mi>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>m</mi>
</munderover>
<mo>|</mo>
<msub>
<mi>x</mi>
<mrow>
<mi>j</mi>
<mo>.</mo>
<mi>k</mi>
</mrow>
</msub>
<msup>
<mo>|</mo>
<mn>2</mn>
</msup>
</mrow>
In formula：Ei,j(tj) for the frequency band energy in f (t) WAVELET PACKET DECOMPOSITIONs to node (i, j)；xj,k(j=0,1,2....2i-1,k
=1,2....m) it is reconstruction signal fi,j(tj) discrete point amplitude；M counts for signal sampling；
The frequency band energy value of each node is obtained, wavelet packet entropy just can be calculated：
<mrow>
<msub>
<mi>P</mi>
<mi>j</mi>
</msub>
<mo>=</mo>
<mfrac>
<msub>
<mi>E</mi>
<mi>j</mi>
</msub>
<mi>E</mi>
</mfrac>
</mrow>
<mrow>
<mi>W</mi>
<mi>E</mi>
<mo>=</mo>
<mo>-</mo>
<munder>
<mo>&Sigma;</mo>
<mi>j</mi>
</munder>
<msub>
<mi>P</mi>
<mi>j</mi>
</msub>
<msub>
<mi>log</mi>
<mn>2</mn>
</msub>
<msub>
<mi>P</mi>
<mi>j</mi>
</msub>
</mrow>
Wherein PjFor each node energy value ratio value shared in energy summation, WE is the small echo entropy of each frequency band.
6. according to the method described in claim 1, it is characterised in that the brain of EEG signals is replied by cable miscellaneous after being pre-processed in step (5)
Degree is drawn by following Sample Entropy algorithm：
1. given one-dimensional discrete time series is set, one group of m n dimensional vector n is configured to, from Xm(1) X is arrivedm(N-m+1), wherein：
Xm(i)=[ui,ui+1,ui+2....ui+m-1] (i=1~N-m+1)
2. defining the distance between any two m dimensional vectors is：
d[Xm(i),Xm(j)]=max | ui+k-uj+k|,0≤k≤m-1；I, j=1~N-m+1, i ≠ j
3. given threshold value r, to each i values, counts d [X (i), X (j)]<R numberThen itself and distance sum (N- are calculated
M) ratio (being referred to as template matches number), note：
Wherein, SD is the standard deviation of one-dimensional discrete time series；
4. calculate：
5. for m+1 point vectors, by above step, it can obtain：
<mrow>
<msup>
<mi>B</mi>
<mrow>
<mi>m</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msup>
<mrow>
<mo>(</mo>
<mi>r</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mrow>
<mi>N</mi>
<mo>-</mo>
<mi>m</mi>
</mrow>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mi>N</mi>
<mo>-</mo>
<mi>m</mi>
</mrow>
</munderover>
<msubsup>
<mi>B</mi>
<mi>i</mi>
<mrow>
<mi>m</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>r</mi>
<mo>)</mo>
</mrow>
</mrow>
6. the sample entropy of this sequence is in theory：SampEn (m, r)=lim {-ln [Bm+1(r)/Bm(r) sequence length] }, is worked as
During for finite value, its sample Entropy estimate is：SampEn (m, r)=- ln [Bm+1(r)/Bm(r)]
Wherein parameter m is previously selected pattern dimension, and r is previously selected similar tolerance limit.
7. according to the method described in claim 1, it is characterised in that in step (6) under two groups of experiments each item data to score
Analysis：Subjective data, behavioral data, F values, R values and sample changes of entropy trend under analysis two kinds of different experiments difficulty of height,
It can be seen that：The generation of the low lower human-body fatigue of difficulty experiment is faster than the generation of human-body fatigue under highly difficult experiment, tests carrying for difficulty
Height can suppress the generation of fatigue to a certain extent.
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