CN102488514A - Method for analyzing relativity between electroencephalograph and myoelectricity based on autonomous and stimulation movement modalities - Google Patents

Abstract

A method for analyzing relativity between electroencephalograph and myoelectricity based on autonomous and stimulation movement modalities comprises the steps of performing system setup, and using a LabVIEW 8.6 to generate synchronizing pulse signals; respectively collecting electroencephalograph signals and electromyography signals including electroencephalograph signals and electromyography signals in autonomous movement modalities and in stimulation movement modalities; analyzing electroencephalograph and electromyography time-domain signals in the autonomous movement and stimulation movement modalities according to time domain pictures of electroencephalograph and electromyography signals of a subject in the autonomous movement and stimulation movement modalities; removing noise of the electromyography signals in the stimulation modality; performing time-frequency analysis on electroencephalograph signals based on Morlet wavelet transformation; and performing coherence analysis. The method can obtain activating or restraining information of electroencephalograph in different time frequency in initiative and passive states to be used for guiding and feeding back recovery indexes of physical disability patients of apoplexy patients and the like, thereby enabling recovery to be a quantitative process instead of a qualitative definition.

Description

Based on analytical method autonomous, that stimulate the brain myoelectricity dependency under the action mode

Technical field

The present invention relates to a kind of analytical method of brain myoelectricity dependency.Particularly relate to a kind of can make people understand the cerebral nerve activity how to control muscular movement based on analytical method autonomous, that stimulate the brain myoelectricity dependency under the action mode.

Background technology

At present; Some international bodies and scholar begin to pay close attention to and be devoted to the research of the mutual relation of brain-myoelectricity; Carried out research like U.S. Univ State Cleveland and Cleveland hospital, disclosed and subtract reduction gradually by the functional epidermis muscle coupling performance due to the muscle fatigue to the EEG-EMG dependency under the single task; Myoelectricity coherence when New South Wales,Australia university moves to brain-myoelectricity dependency and both hands simultaneously between two handss studies, and inquires into the relation of signal source and frequency from the frequency range difference; Roma Univ. is through inquiring into the judgement balance to the research of elite athlete, ordinary movement person and non athlete's brain-myoelectricity dependency; The spinal function variation with advancing age of human body is inquired into by the London University to brain-myoelectricity correlation research of all ages and classes stage people; Freiburg, Germany university is mainly studied is the myoelectricity that produces under the different grip size and the coherence of brain electricity.In addition, also have some universities and research institution also the brain under the different condition-myoelectricity dependency to be studied abroad, domestic still very limited to research in this respect.

Up to now, most researchs are inquired into the autonomous action aspect that all concentrates on the experimenter to the relation of brain-myoelectricity.But for disability patient's athletic rehabilitation, except autonomous action, the stimulation action brain that mode caused is electric, the myoelectricity rule is noticeable equally, and domestic and international application neuromuscular electricity irritation improvement disability patient moving function has obtained better curative effect.Following content shows this point:

The research of Germany Rutgers university shows, after patient's femoral nerve injury, uses low-frequency electrostimulating can accelerate functional rehabilitation; The research of The Hong Kong Polytechnic University shows, when functional electric stimulation is applied to healthy subjects and makes it produce hand exercise, can cause the activation of brain corresponding sports district and sensory region on one's body; People's such as Liu Huihua research has also proved this point through the discussion to SEP and Motion Evoked Potential; After Kimberley etc. act on the patients with cerebral apoplexy limbs with functional electric stimulation, find that the cerebral cortex signal of telecommunication obviously increases, patient's limb function also has clear improvement.Correlational study shows that the neuromuscular electricity irritation can help the patient to accomplish joint motion, feels to pass to brain to correct joint motions sensation and muscle contraction, promotes the reorganization of brain function and activate idle nervous pathway to substitute function of nervous system in damaged condition.

Summary of the invention

Technical problem to be solved by this invention is; Provide a kind of and can access initiatively and the activation or the inhibition information of passive hypencephalon electricity different frequency range, instruct thus and feed back physical disabilities patient such as apoplexy the rehabilitation index based on analytical method autonomous, that stimulate the brain myoelectricity dependency under the action mode.

The technical scheme that the present invention adopted is: a kind of based on analytical method autonomous, that stimulate the brain myoelectricity dependency under the action mode, include following steps:

1) carries out system's setting, that is, use LabVIEW8.6 to produce synchronization pulse;

2) carry out eeg signal acquisition and electromyographic signal collection respectively, comprising: independently move EEG signals and electromyographic signal under the mode, stimulate EEG signals and electromyographic signal under the action mode;

3) according to the experimenter under the autonomous action mode and stimulate brain electromyographic signal time-domain diagram under the action mode independently to move under the mode and stimulate the brain myoelectricity time-domain signal analysis under the action mode;

4) to stimulating the electromyographic signal under the mode to carry out denoising;

5) carry out time frequency analysis based on the EEG signals of wavelet transformation, described time frequency analysis is the wavelet transformation that adopts based on Morlet;

6) carry out coherent analysis

Coherence factor is to be used for a parameter of gauge signal dependency, and he is defined as:

${\mathrm{<figure-callout\; id="1"\; label="Coh\; c"\; filenames="HDA0000117956250000011.png,HDA0000117956250000022.png"\; state="\{\{state\}\}">Coh</figure-callout>}}_c\left(f\right)=\frac{{\left|{\mathrm{<figure-callout\; id="1"\; label="S\; c"\; filenames="HDA0000117956250000011.png,HDA0000117956250000022.png"\; state="\{\{state\}\}">S</figure-callout>}}_c\left(f\right)\right|}^2}{\left|{\mathrm{SP}}_{c1}\left(f\right)\right|\&times;\left|{\mathrm{SP}}_{c2}\left(f\right)\right|}$

Wherein, S _{C1, c2}(f) expression c1 leads the signal that connects the place and c2 and leads and connect the signal the located cross-spectrum at given frequency f place, specifically is the cross-spectrum of EEG signals and electromyographic signal among the present invention, it be defined as:

${\mathrm{<figure-callout\; id="1"\; label="S\; c"\; filenames="HDA0000117956250000011.png,HDA0000117956250000022.png"\; state="\{\{state\}\}">S</figure-callout>}}_c\left(f\right)=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{C1}_i\left(f\right){\mathrm{<figure-callout\; id="2"\; label="C"\; filenames="HDA0000117956250000011.png,HDA0000117956250000062.png"\; state="\{\{state\}\}">C</figure-callout>}2}_2^{*}\left(f\right)$

The coherence factor value is between 0 to 1, and it is good more to be worth big more dependency.

The described use of step 1 LabVIEW8.6 produces synchronization pulse and comprises following process:

(1) sample rate and sampling configuration are set;

(2) generate the monocycle analog waveform;

(3) begin output;

(4) judge: whether the remainder of acquisition time/10 is to continue after the light-off to judge again, otherwise continue behind the bright lamp to judge again greater than 2.

The described autonomous action mode of step 2 is specifically: the experimenter had a rest for 10 seconds; Open LabVIEW, the generation cycle is that 10s, dutycycle are 0.2 square-wave pulse, make display lamp trigger and lighted at high level, and lasting 2s, display lamp is closed 8s then, and when display lamp was bright, the experimenter did the action of right hand middle finger flexing fast; Write down 60 seconds of C3 among the brain electric conductance couplet figure, C4 place EEG signals, flexor digitorum superficialis place electromyographic signal, square-wave pulse signal simultaneously.

The described stimulation of step 2 is moved mode specifically: the experimenter had a rest for 10 seconds, closed LabVIEW, opened electric pulse stimulator; And boost pulse is adjusted to stimulus frequency 0.1Hz, width 100 μ S, initial current 5mA; Stimulation location is experimenter's right arm median nerve, and stimulation point is the right upper extremity far-end, near canalis carpi portion; And according to experimenter's middle finger action degree adjustment stimulator current intensity, to experimenter's middle finger can stimulate have obvious flexing to move down till; Write down 60 seconds of C3 among the stimulation state hypencephalon electric conductance couplet figure, C4 place EEG signals, flexor digitorum superficialis place electromyographic signal simultaneously.

Step 4 is described to be to adopt 2 crest threshold detection algorithm to stimulating electromyographic signal under the mode to carry out denoising: at first, and in the original electromyographic signal input computer under the stimulation action mode of gathering; Computer begins to read data; Program is calculated the absolute value of the electromyographic signal in this segment data and is got maximum; This maximum corresponding stimulate the amplitude of peak value, program is provided with two initial threshold subsequently: one is high level (HT), for detected maxima of waves peak value divided by 2; One is low level (LT), is 1/20 of maxima of waves peak value;

Computer when running into first low level the time, writes down this point in the process that detects; Continue scan-data, when running into high level, continue scanning, when running into second high level and second low level, write down this segment data, and the data between two low levels all are set to 0;

Perhaps in the process that computer is detecting, when running into first low level, continue scanning, next data value does not surpass high level, but has run into second low level, and the signal that obtain this moment is useful electromyographic signal, skips this segment data, continues scanning.

Of the present invention based on analytical method autonomous, that stimulate the brain myoelectricity dependency under the action mode; Under autonomous action mode, when action produces, at the low-frequency component of EEG signals; Be that α frequency range (8-13Hz) has produced that incident is relevant to desynchronize, i.e. relevant (ERD) phenomenon that desynchronizes of incident; Then, make offside brain electricity produce incident relevant desynchronize phenomenon, i.e. incident related synchronization (ERS) phenomenon about a second in β frequency range (14-30).And stimulating under the action mode, because the influence of the electric pulse that exists makes that the low frequency signal of brain electricity is comparatively active always always, but when the action beginning, the existence of ERD phenomenon is arranged still, there is the energy liter of β frequency range then in the second.The present invention obtain initiatively and passive hypencephalon electricity different frequency range activation or inhibition information; Be used for instructing and feeding back physical disabilities patients' such as apoplexy rehabilitation index thus; Make that rehabilitation no longer is a notion qualitatively, has more moved towards a quantitative process.

Description of drawings

Fig. 1 is based on independently, stimulates the brain myoelectricity correlation analysis block diagram of action;

Fig. 2 is Labview 8.6 lock-out pulse FB(flow block)s;

Fig. 3 is that the brain electric conductance joins sketch map;

Fig. 4 (a) is the brain electromyographic signal time-domain diagram of experimenter under autonomous action mode;

Fig. 4 (b) is that the experimenter is at the brain electromyographic signal time-domain diagram that stimulates under the action mode;

Fig. 5 is based on the program control flow chart of 2 crest threshold detection algorithm;

Fig. 6 is based on the electromyographic signal comparison diagram under the stimulation mode of 2 crest threshold test;

Fig. 7 (a) is the frequency spectrum design sketch of the EEG signals of experimenter under autonomous action mode;

Fig. 7 (b) is that the experimenter is at the frequency spectrum design sketch that stimulates the EEG signals under the action mode;

Fig. 8 (a) is the brain myoelectricity coherence result of experimenter under autonomous action mode a design sketch;

Fig. 8 (b) is that the experimenter is at the design sketch that stimulates the brain myoelectricity coherence result under the action mode;

Fig. 9 is coherence the count relation of experimenter under two kinds of actions of different-waveband mode,

Wherein: the left side is independently to move under the mode, and the right is to stimulate under the action mode.

Among the figure:

1: cerebral cortex 2: muscle of upper extremity

3: surface electrode 4: surface electrode

5: independently move 6: stimulate action

7: eeg amplifier 8: myoelectricity amplifier

9: the digitized bio electricity gathers 10: date processing

The specific embodiment

Below in conjunction with embodiment and accompanying drawing to of the present invention based on autonomous, stimulate the analytical method of the brain myoelectricity dependency under the action mode to make detailed description.

Of the present invention based on analytical method autonomous, that stimulate the brain myoelectricity dependency under the action mode, include following steps:

1) carries out system's setting, that is, use LabVIEW8.6 to produce synchronization pulse;

Eeg amplifier shown in Figure 1 and myoelectricity amplifier are to be integrated on the same instrument---the digital brain myoelectricity of the four-way analyser (Micromed Brain Quick EEG) that Micromed company produces, and what surface electrode was chosen is the Ag-AgCl electrode; In whole experiment, experimenter's boost pulse is produced by electric pulse stimulation instrument (being provided by the digital brain myoelectricity of four-way analyser), and this instrument can produce the electric pulse of frequency range at 0.1Hz-10Hz; Lock-out pulse is produced by virtual instrument of LabVIEW 8.6.

Described LabVIEW (Laboratory Virtual Instrument Engineering Workbench) is a kind of development environment of patterned programming language; It is accepted by industrial quarters, academia and research laboratory widely, is regarded as the data acquisition and the instrument control software of a standard.LabVIEW is integrated and the repertoire that satisfies GPIB, VXI, RS-232 and RS-485 protocol with hardware and data collecting card communication.It is also built-in is convenient to use the built-in function of software standards such as TCP/IP, ActiveX.This is a powerful and software flexibly.Utilize it can set up the virtual instrument of oneself easily, feasible programming in its patterned interface and use be vivid and interesting all.

In the present invention, the concrete operations of using LabVIEW8.6 to produce synchronization pulse comprise following process:

(1) sample rate and sampling configuration are set;

(2) generate the monocycle analog waveform;

(3) begin output;

(4) judge: whether the remainder of acquisition time/10 is to continue after the light-off to judge again, otherwise continue behind the bright lamp to judge again greater than 2.

2) carry out eeg signal acquisition and electromyographic signal collection respectively, comprising: independently move EEG signals and electromyographic signal under the mode, stimulate EEG signals and electromyographic signal under the action mode;

The collection of the EEG signals 10-20 electrode of adopting international standards is placed standard, through electrode cap electrode is linked to each other with scalp.Because the moving region of brain control human body is apparent in view in C3, C4 zone, so the EEG signal is gathered at C3, C4 place.Adopt the single-stage method of leading, brain electricity reference electrode A1, A2 lead and be connected respectively to left and right sides ear-lobe and use as indifferent electrode, and be as shown in Figure 3.

Electromyographic signal collection is under different action patterns, requires experimenter's middle finger initiatively or passive action (passive the counting on one's fingers after promptly being upset), need to transfer and the target muscle of participating in be flexor digitorum superficialis (flexor digitorum superficialis, FDS).

Eeg signal acquisition detailed process under the described autonomous action mode is: the experimenter had a rest for 10 seconds; Open LabVIEW, the generation cycle is that 10s, dutycycle are 0.2 square-wave pulse, make display lamp trigger and lighted at high level, and lasting 2s, display lamp is closed 8s then, and when display lamp was bright, the experimenter did the action of right hand middle finger flexing fast; Write down among the brain electric conductance couplet figure C3, C4 place EEG signals (shown in Fig. 3), flexor digitorum superficialis (FDS) simultaneously and locate 60 seconds of electromyographic signal, square-wave pulse signal.

Eeg signal acquisition under the described stimulation action mode is specifically: the experimenter had a rest for 10 seconds, closed LabVIEW, opened electric pulse stimulator; And boost pulse is adjusted to stimulus frequency 0.1Hz, width 100 μ S, initial current 5mA; Stimulation location is experimenter's right arm median nerve, and stimulation point is the right upper extremity far-end, near canalis carpi portion; And according to experimenter's middle finger action degree adjustment stimulator current intensity, to experimenter's middle finger can stimulate have obvious flexing to move down till; Write down among the stimulation state hypencephalon electric conductance couplet figure C3, C4 place EEG signals, flexor digitorum superficialis (FDS) simultaneously and locate 60 seconds of electromyographic signal.

3) according to the experimenter under the autonomous action mode and stimulate brain electromyographic signal time-domain diagram under the action mode independently to move under the mode and stimulate the brain myoelectricity time-domain signal analysis under the action mode;

What Fig. 4 provided is that the experimenter is at the brain myoelectricity time-domain signal under the autonomous action mode, under the stimulation action mode; In Fig. 4 (a), can see electromyographic signal in whole course of action, amplitude has obvious variation; Explanation is when muscle contraction or diastole, and the muscle electrical activity will enliven during than tranquillization.In Fig. 4 (b), expression be the experimenter at the brain electromyographic signal time-domain diagram that stimulates under the action mode.In this figure, can find out that under the stimulation of electric pulse experimenter's electromyographic signal amplitude is higher, why produces this phenomenon, be because electric pulse has polluted electromyographic signal, therefore need carry out denoising to stimulating the electromyographic signal under the mode.

4) to stimulating the electromyographic signal under the mode to carry out denoising;

Stimulate under the action mode, because have the interference of stimulus signal, especially stimulation on muscle, to take place simultaneously with inducing myoelectric potential, and stimulating electrode is close with the recording electrode position, and gathering purified electromyographic signal just has certain difficulty.Generally speaking, the more common electromyographic signal of the amplitude of stimulus signal is higher, and the output of powerful stimulus signal can be infected comparatively responsive myoelectricity acquisition system, causes the stimulation interference problem.Therefore, the present invention has adopted " 2 crest threshold detection algorithm " to weaken the interference of boost pulse.

Set the high level and the low level of crest among the present invention according to the absolute value of electromyographic signal data, thereby detected positive and negative stimulation crest, and then the filtering stimulus signal has stayed complete electromyographic signal simultaneously.Because boost pulse big spike waveform normally, its amplitude and time constant are controlled by some factors jointly, comprise the factors such as setting, electrode position, stimulus modelity of stimulation output current, amplifier.If the position of electromyographic signal collection electrode enough away from stimulating electrode, stimulates interference and inducing myoelectric potential signal can not produce aliasing so.

Described as shown in Figure 5 to stimulating electromyographic signal under the mode to carry out denoising: at first, in the original electromyographic signal input computer under the stimulation action mode of gathering; Computer begins to read data; Program is calculated the absolute value of the electromyographic signal in this segment data and is got maximum; This maximum corresponding stimulate the amplitude of peak value, program is provided with two initial threshold subsequently: one is high level (HT), for detected maxima of waves peak value divided by 2; One is low level (LT), is 1/20 of maxima of waves peak value;

Computer when running into first low level the time, writes down this point in the process that detects; Continue scan-data, when running into high level, continue scanning, when running into second high level and second low level, write down this segment data, and the data between two low levels all are set to 0;

Perhaps in the process that computer is detecting, when running into first low level, continue scanning, next data value does not surpass high level, but has run into second low level, and the signal that obtain this moment is useful electromyographic signal, skips this segment data, continues scanning.

In this program, whenever detect the just filtering thereupon of a crest, therefore can filtering stimulate interference waveform.

At last, through the electromyographic signal denoising, obtain the electromyographic signal under the comparatively purified stimulation mode shown in Fig. 6.

5) carry out time frequency analysis based on the EEG signals of wavelet transformation, described time frequency analysis is the wavelet transformation that adopts based on Morlet;

Said Morlet wavelet transformation is a kind of of continuous wavelet transform, and basic thought is: (t f) carries out convolution, thereby obtains time dependent time-frequency Energy distribution, promptly with Morlet small echo w successive time signal s (t)

TF(t，f)＝|w(t，f) ^*s(t)| ²

(t is a kind of synthetic Gaussian function that duplicates f), in time domain (standard deviation to Morlet small echo w _t) and frequency domain (standard deviation _f) on all have Gauss distribution, for certain frequency f, its expression formula is:

$w(t,f)=\mathrm{Aexp}(-{\mathrm{<figure-callout\; id="2"\; label="t"\; filenames="HDA0000117956250000011.png,HDA0000117956250000062.png"\; state="\{\{state\}\}">t</figure-callout>}}^2/2{\mathrm{\&sigma;}}_t^2)\exp \left(2\mathrm{i\&pi;ft}\right)$

Wherein, σ _t=1/2 π σ _f, $A={\left({\mathrm{\&sigma;}}_t{\mathrm{\&pi;}}^{\frac{1}{2}}\right)}^{-\frac{1}{2}}$

A is a normalization factor, its objective is that the energy that will guarantee wavelet basis itself is 1.

Morlet small echo family has constant ratio f/ σ _f(general value is greater than-5 in practical application), so the pairing σ of different frequency f _fAnd σ _tBe different, promptly it has variable time frequency resolution on whole time-frequency plane: at high frequency region high temporal resolution can be provided, at low frequency range high frequency discrimination can be provided.

As shown in Figure 7, provided the experimenter at spectrogram autonomous, that stimulate the EEG signals under the action mode.

From Fig. 7 (a), Fig. 7 (b) two width of cloth figure, can clear and definite finding out, under autonomous action mode, when action produced, at the low-frequency component of EEG signals, promptly α frequency range (8-13Hz) had produced that incident is relevant to desynchronize, i.e. the ERD phenomenon; Then, make offside brain electricity produce incident related synchronization phenomenon, i.e. ERS phenomenon about a second in β frequency range (14-30).And stimulating under the action mode, because the influence of the electric pulse that exists makes that the low frequency signal of brain electricity is comparatively active always always, but when the action beginning, the existence of ERD phenomenon is arranged still, exist the energy of β frequency range to raise then in the second.

6) carry out coherent analysis

Coherence factor is to be used for a parameter of gauge signal dependency, and he is defined as:

${\mathrm{<figure-callout\; id="1"\; label="Coh\; c"\; filenames="HDA0000117956250000011.png,HDA0000117956250000022.png"\; state="\{\{state\}\}">Coh</figure-callout>}}_c\left(f\right)=\frac{{\left|{\mathrm{<figure-callout\; id="1"\; label="S\; c"\; filenames="HDA0000117956250000011.png,HDA0000117956250000022.png"\; state="\{\{state\}\}">S</figure-callout>}}_c\left(f\right)\right|}^2}{\left|{\mathrm{SP}}_{c1}\left(f\right)\right|\&times;\left|{\mathrm{SP}}_{c2}\left(f\right)\right|}$

Wherein, S _{C1, c2}(f) expression c1 leads the signal that connects the place and c2 and leads and connect the signal the located cross-spectrum at given frequency f place, specifically is the cross-spectrum of EEG signals and electromyographic signal in the present invention, it be defined as:

${\mathrm{<figure-callout\; id="1"\; label="S\; c"\; filenames="HDA0000117956250000011.png,HDA0000117956250000022.png"\; state="\{\{state\}\}">S</figure-callout>}}_c\left(f\right)=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{C1}_i\left(f\right){\mathrm{<figure-callout\; id="2"\; label="C"\; filenames="HDA0000117956250000011.png,HDA0000117956250000062.png"\; state="\{\{state\}\}">C</figure-callout>}2}_2^{*}\left(f\right)$

The coherence factor value is between 0 to 1, and it is good more to be worth big more dependency.

Among Fig. 8, Fig. 8 (a) is the brain myoelectricity coherence result of experimenter under autonomous action mode, and Fig. 8 (b) is that the experimenter is the brain myoelectricity coherence result who stimulates under the action mode.

Fig. 9 has provided the coherence's relation of counting under two kinds of actions of different-waveband mode.

Claims (5)

1. one kind based on analytical method autonomous, that stimulate the brain myoelectricity dependency under the action mode, it is characterized in that, includes following steps:

1) carries out system's setting, that is, use LabVIEW8.6 to produce synchronization pulse;

2) carry out eeg signal acquisition and electromyographic signal collection respectively, comprising: independently move EEG signals and electromyographic signal under the mode, stimulate EEG signals and electromyographic signal under the action mode;

3) according to the experimenter under the autonomous action mode and stimulate brain electromyographic signal time-domain diagram under the action mode independently to move under the mode and stimulate the brain myoelectricity time-domain signal analysis under the action mode;

4) to stimulating the electromyographic signal under the mode to carry out denoising;

5) carry out time frequency analysis based on the EEG signals of wavelet transformation, described time frequency analysis is the wavelet transformation that adopts based on Morlet;

6) carry out coherent analysis

Coherence factor is to be used for a parameter of gauge signal dependency, and he is defined as:

${\mathrm{Coh}}_{c1,c2}\left(f\right)=\frac{{\left|S_{c1,c2}\left(f\right)\right|}^2}{\left|{\mathrm{SP}}_{c1}\left(f\right)\right|\&times;\left|{\mathrm{SP}}_{c2}\left(f\right)\right|}$

Wherein, S _{C1, c2}(f) expression c1 leads the signal that connects the place and c2 and leads and connect the signal the located cross-spectrum at given frequency f place, specifically is the cross-spectrum of EEG signals and electromyographic signal among the present invention, it be defined as:

$S_{c1,c2}\left(f\right)=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{C1}_i\left(f\right){C2}_2^{*}\left(f\right)$

The coherence factor value is between 0 to 1, and it is good more to be worth big more dependency.

2. according to claim 1 based on analytical method autonomous, that stimulate the brain myoelectricity dependency under the action mode, it is characterized in that the described use of step 1 LabVIEW8.6 produces synchronization pulse and comprises following process:

(1) sample rate and sampling configuration are set;

(2) generate the monocycle analog waveform;

(3) begin output;

(4) judge: whether the remainder of acquisition time/10 is to continue after the light-off to judge again, otherwise continue behind the bright lamp to judge again greater than 2.

3. according to claim 1ly it is characterized in that based on analytical method autonomous, that stimulate the brain myoelectricity dependency under the action mode, the described autonomous action mode of step 2 specifically: the experimenter had a rest for 10 seconds; Open LabVIEW, the generation cycle is that 10s, dutycycle are 0.2 square-wave pulse, make display lamp trigger and lighted at high level, and lasting 2s, display lamp is closed 8s then, and when display lamp was bright, the experimenter did the action of right hand middle finger flexing fast; Write down 60 seconds of C3 among the brain electric conductance couplet figure, C4 place EEG signals, flexor digitorum superficialis place electromyographic signal, square-wave pulse signal simultaneously.

4. according to claim 1 based on analytical method autonomous, that stimulate the brain myoelectricity dependency under the action mode, it is characterized in that the described stimulation of step 2 is moved mode specifically: the experimenter had a rest for 10 seconds; Close LabVIEW; Open electric pulse stimulator, and boost pulse is adjusted to stimulus frequency 0.1Hz, width 100 μ S, initial current 5mA, stimulation location is experimenter's right arm median nerve; Stimulation point is the right upper extremity far-end; Near canalis carpi portion, and according to experimenter's middle finger action degree adjustment stimulator current intensity, to experimenter's middle finger can stimulate have obvious flexing to move down till; Write down 60 seconds of C3 among the stimulation state hypencephalon electric conductance couplet figure, C4 place EEG signals, flexor digitorum superficialis place electromyographic signal simultaneously.

5. according to claim 1 based on analytical method autonomous, that stimulate the brain myoelectricity dependency under the action mode; It is characterized in that; Step 4 is described to be to adopt 2 crest threshold detection algorithm to stimulating electromyographic signal under the mode to carry out denoising: at first, and in the original electromyographic signal input computer under the stimulation action mode of gathering; Computer begins to read data; Program is calculated the absolute value of the electromyographic signal in this segment data and is got maximum; This maximum corresponding stimulate the amplitude of peak value, program is provided with two initial threshold subsequently: one is high level (HT), for detected maxima of waves peak value divided by 2; One is low level (LT), is 1/20 of maxima of waves peak value;

Computer when running into first low level the time, writes down this point in the process that detects; Continue scan-data, when running into high level, continue scanning, when running into second high level and second low level, write down this segment data, and the data between two low levels all are set to 0;

Perhaps in the process that computer is detecting, when running into first low level, continue scanning, next data value does not surpass high level, but has run into second low level, and the signal that obtain this moment is useful electromyographic signal, skips this segment data, continues scanning.

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