CN102488515B - Conjoint analysis method for electroencephalograph and electromyography signals based on autonomous movement and imagination movement - Google Patents

Conjoint analysis method for electroencephalograph and electromyography signals based on autonomous movement and imagination movement Download PDF

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CN102488515B
CN102488515B CN 201110410414 CN201110410414A CN102488515B CN 102488515 B CN102488515 B CN 102488515B CN 201110410414 CN201110410414 CN 201110410414 CN 201110410414 A CN201110410414 A CN 201110410414A CN 102488515 B CN102488515 B CN 102488515B
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imagination
signal
action
electromyographic signal
movement
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CN102488515A (en
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明东
袁丁
徐瑞
任玥
王悟夷
綦宏志
万柏坤
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Zhongdian Yunnao (Tianjin) Technology Co., Ltd.
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Tianjin University
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Abstract

A conjoint analysis method for electroencephalograph and electromyography signals based on autonomous movement and imagination movement comprises the steps of performing system setup, and using a LabVIEW 8.6 to generate square wave pulse signals; respectively collecting electroencephalograph signals and electromyography signals including electroencephalograph signals and electromyography signals in autonomous movement modalities and in imagination movement modalities; performing noise removal pretreatment on collected original data; performing electroencephalograph and electromyography time-domain signal analysis in the autonomous movement and imagination movement modalities on electroencephalograph and electromyography signal time-domain pictures which are performed with noise removal pretreatment in the autonomous movement and imagination movement modalities; performing time-frequency signal analysis on electroencephalograph and electromyography signals performed with noise removal pretreatment and in the autonomous movement and imagination movement modalities based on Morlet wavelet transformation; and performing partial directional coherence analysis, and in particular adopting granger causality to perform the partial directional coherence analysis. The conjoint analysis method provides new evaluation parameters for monitoring recovery auxiliary equipment and assessing organism movement level.

Description

Based on autonomous action, imagination action hypencephalon electromyographic signal conjoint analysis method
Technical field
The present invention relates to a kind of.Particularly relate to a kind of EEG signals and electromyographic signal by the relevant range under the different action of the synchronous acquisition mode, the brain electromyographic signal of two kinds of actions under the mode carried out date processing and oriented phase dry analysis partially, obtain the brain electromyographic signal when action takes place causality and the flow direction property of information based on autonomous action, imagination action hypencephalon electromyographic signal conjoint analysis method.
Background technology
In nineteen twenty-four, the German Jena Spirit of University professor Hans Berger of section (1873-1941) doctor has at first write down human scalp EEG signals, and for the first time with brain electrical acti called after electroencephalogram (EEG).But because the scholar of the central nervous system of social research electrical activity was less at that time, the achievement of Berger is not admitted by most physiologist and neuropathist.Up to 1933, the famous physiologist E.D.Adrain (promise shellfish prize winner in 1934) of Britain studied electroencephalogram with B.Mathews in Cambridge University Physiologic Studies chamber, has affirmed the relevant research of Berger.Afterwards, EEG research just is able to fast development, and is accepted by the whole world.And sEMG (Surface Electromyogram Signal), it is surface electromyogram signal, it is the active one dimension time series signal of noting by electrode from muscle surface of relevant neuromuscular, its amplitude changes with to participate in factors such as active motor unit quantity, motor unit activity pattern and metabolism state relevant, can be in real time, accurately and reflect musculation state and functional status under noninvasive state.Therefore can reflect nervimuscular activity to a certain extent, and in the diagnosis of clinical medical neuromuscular disease, in the Ergonomy analysis of the muscular work in ergonomics field, judge in the muscle function evaluation and the fatigue in sports science in rehabilitation medicine field, the motor technique analysis on its rationality.Begin brain electricity, myoelectricity to be joined together to consider and the research beginning analytical model that people use always has following three kinds from researcher, scholars:
(1) brain myoelectricity dependency is analyzed in autonomous action (task difference).For example the A.A.Abdul-latif of University of Melbourne is in research right-hand man in 2004 when independently moving, the variation of brain electricity dependency, domination of brain homonymy motor cortex and contribution situation when pointing out the homonymy limb motion; Calendar year 2001, the Li Yan of Jilin University studied the function nuclear magnetic resonance research of normal person's brain active region when three kinds of autonomous finger movement patterns in its academic dissertation, the simple motion domination that shows handedness is mainly at offside brain SM1, and the SM1 of bilateral has participated in the simple motion of non-handedness.Random movement participates in the zone of action more than simple motion, and bilateral SMA all participates in; Mainly arrange during the imagination action by SMA, PMA.
(2) imagination action model analysis EEG signals feature.The existing motion imagination therapy that studies show that can effectively be improved cerebral infarction hemiplegic patient's motor function.This mainly is based on the plasticity theory of brain.For paresis, if will produce voluntary movement, also must be that the motion idea is arranged earlier, muscle contraction and limb motion are just arranged then.One of effect of rehabilitation is exactly to strengthen this proper motion control model from brain to muscle group repeatedly, can promote the formation of this proper motion reflex arc effectively based on the motion idea of imagination action.In the research of Gerardin etc., allow 8 routine dextromanual normal persons carry out the right finger flexion and extension imagination, find and actual motion has similarly activated bilateral movement proparea, top, basal nuclei and cerebellum through MIR.And after British Nikhil Sharma confirmed for patients with cerebral apoplexy, can use the situation that " motion the imagination " improves the limb movement disturbance after the apoplexy.Domestic also have the scholar to do similar work, supported above viewpoint.These studies show that motor function disability patient can use the nervus motorius network of " the motion imagination " part activation damage.
(3) autonomous action combines down the dependency of analyzing the brain myoelectricity with imagination action.YasunariH has inquired into the brain electricity-myoelectricity dependency of lower limb muscles under positive action and imagination action in its research in 2010, obtain the moving imagination and actual act are drawn eclipsed corticocerebral reflection; The same year, the Nan Liang of Hiroshima University is based on the action of TMS (transcranial magnetic stimulation) the research contralateral limbs imagination time, the irritability of also pointing out the motor cortex that caused by allocinesi can be suppressed by the homonymy limb motion or disturb, and this may be by due to the callosal depression effect; The Andrea Zimmer of University of Zurich has bigger effect in the physiotherapy that can compare to other for paralytic's applied imagination therapy that studies show that in 2008.
With respect to independently signal analysis, the causal analysis of brain electromyographic signal is the contact between reflected signal more directly and accurately, this feasible research based on brain electricity myoelectricity correlation analysis is with a wide range of applications: can understand the pathomechanism of some dyskinetic disorder (for example Parkinson's disease) in depth, be function reparation after being ill and alternative effective foundation and the new way of providing; Can effectively improve the method for rehabilitation of motor type injury recovery phase; Can improve the evaluation studies means of human motion level and balanced capacity.
Summary of the invention
Technical problem to be solved by this invention is, provide a kind of and utilize that inclined to one side orientation is relevant carries out brain electricity different modalities under and the causality analysis of electromyographic signal, by electricity of the brain under the synchronous stimulation different modalities and myoelectricity data, analyze all band information of EEG signals and dynamoelectric signal on the whole, on frequency domain, provided causality and flow of information tropism between the two, thus for rehabilitation provide a kind of evaluating and locomotory mechanism evaluating based on autonomous action, imagination action hypencephalon electromyographic signal conjoint analysis method.
The technical solution adopted in the present invention is: a kind of based on autonomous action, imagination action hypencephalon electromyographic signal conjoint analysis method, include following steps:
1) carry out system's setting, that is, using the LabVIEW8.6 cycle that produces is 0.2 square-wave pulse signal as 10s, dutycycle;
2) carry out eeg signal acquisition and electromyographic signal collection respectively, comprising: independently move EEG signals and electromyographic signal under the mode, EEG signals and electromyographic signal under the imagination action mode;
3) initial data of gathering is carried out the denoising pretreatment;
4) under the pretreated autonomous action mode of denoising and the brain electromyographic signal time-domain diagram under the imagination action mode independently moves under the mode and brain myoelectricity time-domain signal analysis under the imagination action mode;
5) to the brain electromyographic signal under the pretreated autonomous action of denoising, the imagination action mode carry out the time frequency signal analysis, described time frequency signal analysis is the wavelet transformation that adopts based on Morlet;
6) carrying out inclined to one side oriented phase dry analysis, specifically is to adopt the Granger causality to carry out inclined to one side oriented phase dry analysis.
The described LabVIEW8.6 of use of step 1 cycle that produces is that 0.2 square-wave pulse signal comprises following process as 10s, dutycycle:
(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 connection figure, C4 place EEG signals, flexor digitorum superficialis place electromyographic signal, square-wave pulse signal simultaneously.
The described imagination of step 2 is moved mode 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.When display lamp was bright, the experimenter did the action of right hand middle finger flexing fast; Write down C3, C4 place EEG signals, flexor digitorum superficialis (FDS) simultaneously and locate 60 seconds of electromyographic signal, synchronization pulse.
Step 3 is described carries out the denoising pretreatment to the initial data of gathering, be to use Butterworth three rank band filters (containing 50Hz power frequency trap) respectively EEG signals and electromyographic signal to be carried out Filtering Processing, according to effective frequency range feature of signal, choose EEG signals cut-off frequency: 0.5Hz and 40Hz; Electromyographic signal cut-off frequency: 0.5Hz and 200Hz.Afterwards, because the sample rate of initial data is very high, respectively EEG signals and electromyographic signal are carried out down-sampled the processing to 512Hz.
Of the present invention based on autonomous action, imagination action hypencephalon electromyographic signal conjoint analysis method, the method that provides a kind of inclined to one side orientation that is used for analysis EEG signals and electromyographic signal under difference action mode to be concerned with, at brain electricity and brain, relation between brain electricity and the electromyographic signal, coherence's judgement and analysis have been carried out, obtained tangible result, thereby for rehabilitation accessory monitors and body movement proficiency assessment provide new evaluating, in the rehabilitation engineering field and the locomotory mechanism research field practical application prospect is all arranged.
Description of drawings
Fig. 1 is based on autonomous action, imagination action hypencephalon electromyographic signal conjoint analysis block diagram;
Fig. 2 is Labview 8.6 lock-out pulse FB(flow block)s;
Fig. 3 is a brain electric conductance connection sketch map;
Fig. 4 (a) is the brain electromyographic signal time-domain diagram of experimenter under autonomous action mode;
Fig. 4 (b) is the brain electromyographic signal time-domain diagram of experimenter under imagination action mode;
The spectrogram of the EEG signals of Fig. 5 (a) experimenter under autonomous action mode;
The spectrogram of the EEG signals of Fig. 5 (b) experimenter under imagination action mode;
The brain myoelectricity inclined to one side oriented phase dry analysis of Fig. 6 (a) experimenter under autonomous action mode
Fig. 6 (b) experimenter send the brain myoelectricity of doing under the mode inclined to one side oriented phase dry analysis in the imagination
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
Make a detailed description based on autonomous action, imagination action hypencephalon electromyographic signal conjoint analysis method of the present invention below in conjunction with embodiment and accompanying drawing.
Of the present inventionly be based on the Granger cause effect relation, time-domain information be mapped to the method for a kind of brain myoelectricity conjoint analysis on the frequency domain based on autonomous action, imagination action hypencephalon electromyographic signal conjoint analysis method.The design belongs to disability rehabilitation's technical field.Its techniqueflow is: by the EEG signals and the electromyographic signal of the relevant range under the different action of the synchronous acquisition mode, the brain electromyographic signal of two kinds of actions under the mode carried out date processing and oriented phase dry analysis partially, obtain the brain electromyographic signal when action takes place causality and the flow direction property of information.
Of the present invention based on autonomous action, imagination action hypencephalon electromyographic signal conjoint analysis method, include following steps:
1) carry out system's setting, that is, using the LabVIEW8.6 cycle that produces is 0.2 square-wave pulse signal as 10s, dutycycle.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 considered 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.
The described LabVIEW8.6 of the use cycle that produces is that 0.2 square-wave pulse signal comprises following process as 10s, dutycycle:
(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, EEG signals and electromyographic signal under the imagination action mode;
The collection of the EEG signals 10-20 electrode of adopting international standards is placed standard, by 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, A1, A2 lead and are connected respectively to left and right sides ear-lobe and use as indifferent electrode, as shown in Figure 3.
This experiment requires experimenter's middle finger initiatively or passive action under different action patterns, need to transfer and participate in target muscle be flexor digitorum superficialis ( Flexor Digitorum Superficialis) (FDS).
Described autonomous action mode 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 connection figure, C4 place EEG signals, flexor digitorum superficialis place electromyographic signal, square-wave pulse signal simultaneously.
The described imagination is moved mode 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.When display lamp was bright, the experimenter did the action of right hand middle finger flexing fast; Write down C3, C4 place EEG signals, flexor digitorum superficialis (FDS) simultaneously and locate 60 seconds of electromyographic signal, synchronization pulse.
3) initial data of gathering is carried out the denoising pretreatment;
Because the initial data of gathering is mixed with a large amount of background noises, before data analysis, need carry out pretreatment to initial data, described initial data to collection carries out the denoising pretreatment, be to use Butterworth three rank band filters (containing 50Hz power frequency trap) respectively EEG signals and electromyographic signal to be carried out Filtering Processing, according to effective frequency range feature of signal, choose EEG signals cut-off frequency: 0.5Hz and 40Hz; Electromyographic signal cut-off frequency: 0.5Hz and 200Hz.Afterwards, because the sample rate of initial data is very high, respectively EEG signals and electromyographic signal are carried out down-sampled the processing to 512Hz.
4) under the pretreated autonomous action mode of denoising and the brain electromyographic signal time-domain diagram under the imagination action mode independently moves under the mode and brain myoelectricity time-domain signal analysis under the imagination 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 imagination 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), the brain electromyographic signal time-domain diagram that is the experimenter under imagination action mode of expression.Because the electromyographic signal under the imagination action almost is stably, therefore, in the time-domain diagram of electromyographic signal, can't see the clearly variation of amplitude.
5) to the brain electromyographic signal under the pretreated autonomous action of denoising, the imagination action mode carry out the time frequency signal analysis, described time frequency signal analysis is the wavelet transformation that adopts based on Morlet;
The 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 ) = Aexp ( - t 2 / 2 σ t 2 ) exp ( 2 iπft )
Wherein, σ t=1/2 π σ f, A = ( σ t π 1 2 ) - 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 in actual applications value is greater than-5), so the pairing σ of different frequency f fAnd σ tBe different, promptly it has variable time frequency resolution on whole time-frequency plane: can provide high temporal resolution at high frequency region, can provide high frequency discrimination at low frequency range.
Shown in Fig. 5 (a), Fig. 5 (b), 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 relevant desynchronize phenomenon, i.e. ERS phenomenon about a second in β frequency range (14-30).And after the experimenter begins the imagination, the energy that can find EEG signals C3 place begins to reduce (Fig. 5 (a)), be embodied in α wave band and beta band, this also is when imagination action, the general phenomenon that brain wave can occur, we are referred to as relevant the desynchronizing of incident is the ERD phenomenon, and this phenomenon appears at C3 brain electric conductance connection place, and this has also illustrated the offside domination principle of brain to limb action.
6) carrying out inclined to one side oriented phase dry analysis, specifically is to adopt the Granger causality to carry out inclined to one side oriented phase dry analysis.
In neuroscience, studying cognitive function that different cortexes zone comprised and the contact of estimating between each zone is two core problem.And be the contact of research between each brain district, scholars usually adopt some mathematical measures to assess interaction between each neural cluster.Such as: relevant, relevant and Phase synchronization etc.Yet these methods can not be distinguished the direction or perhaps the cause effect relation of flow of information between each brain district, thus, Granger causality 1969 with causal concept formulation, and be applied to the linear session series model.Nowadays the Granger cause effect relation is widely used in many fields such as economy, physiology, computer neuroscience, is used for studying the internal relation between each class variable.
A) Granger causality
The causal basic thought of Granger is: if first time series numerical value instantly is that estimated by the numerical value in first time series past and the numerical value in second time series past and forecast error variance when only being estimated by the numerical value in first time series past reduces, then claim second time series be first seasonal effect in time series because of, otherwise be not.Time is a very important key element in the Granger cause effect relation, occur in the front be because of, occur in the fruit of back.For the time series data of two dimension, can be directly with the internal relation between the Granger cause effect relation analytical data.For multidimensional time series data, because the interaction between the variable, can not directly come situational variables directly to interact, can utilize the partial correlation cause effect relation of introducing previously to study intrinsic contacting directly between these variablees with the Granger cause effect relation.
The step of Granger cause effect relation check is as follows:
(1) current y is done recurrence to all hysteresis item y and other variable (if any), promptly y is to the hysteresis item y of y T-1, y T-2..., y T-qAnd the recurrence of dependent variable, but in this recurrence, an x that lags behind is not included, this is an affined recurrence.From then on return then and obtain affined residual sum of squares (RSS) RSSR.
(2) do a recurrence that contains an x that lags behind, promptly add an x that lags behind in the regression equation in front, this is a unconfined recurrence, returns thus and obtains unconfined residual sum of squares (RSS) RSSUR.
(3) null hypothesis is H0: α 1=α 2=...=α q=0, an x that promptly lags behind does not belong to this recurrence.
(4) in order to check this hypothesis, with the F check, promptly
F = ( RSS R - RSS UR ) / q RSS UR / ( n - k )
It follows degree of freedom is q and F-distribution (n-k).
(5) if the critical F α of the F value value of calculating on selected significance level α is then refused null hypothesis, the x item that lags behind so just belongs to this and returns, and shows that x is the reason of y.
(6) same, whether be the reason of x in order to check y, can be with variable y and x mutual alternative, repeating step (1)~(5).
And it is well-known, in many application of signal processing, usually need it is carried out frequency-domain analysis, especially be similar to the unconspicuous signal of temporal signatures, EEG signals for example, or the obtaining of target velocity information in the radar system, have the analysis of noise signal in the sonar system, and the living analysis of spectrum in the speech processing system or the like, these have all related to frequency-domain analysis, and one of frequency-domain analysis most important parameter study method that is it.Thereby the Granger cause effect relation has been generalized to the frequency domain space, thus the further cause effect relation that between the frequency domain variable, exists of research, its resultant result has more practical significance.
The causal definition of Granger derives from economics at first, and it is to describe in the multivariate processing procedure a very important instrument of directed dynamic relationship between each element, so be applied to the research [18] to neuroscience in recent years.The cause effect relation of Ti Chuing is the influence on time domain the earliest, develops the time structure that signal based on such idea, has defined the cause effect relation of predicahle.In linear structure, Granger cause effect relation and VAR model have confidential relation.Specific as follows: order, x=(x (t)) t ∈ Z, wherein: x (t)=(x 1(t), Λ, x n(t)) ' be that an average is 0 stable n dimension time series.Brief p rank VAR model VAR[p so] be:
x ( t ) = Σ r = 1 p a ( r ) x ( t - r ) + ϵ ( t ) - - - ( 1 )
Wherein a (r) is that the n * n of model maintains matrix number, and ε (t) is the multivariate white Gaussian noise.The covariance matrix of noise process is represented with Σ.In order to guarantee the stable hypothesis of model:
det(I-a(1)z-...-a(p)z p)≠0 (2)
Wherein, have for all z ∈ C | z|≤1.
Coefficient a in this model Ij(r) x has been described iFor past x jThe linear degree of dependence of each element.If in the autoregression of (1) is expressed for all r=1, A, p, a Ij(r) all be 0, so just say x in whole process jTo x iThere is not the Granger causal connection.In other words, if predict x by the value in past iDuring (t+1) value, use x jEach variable can not improve x i(t+1) prediction, then x jTo x iThere is not the Granger causal connection.Notice that the VAR model method is merely able to describe the linear relationship between variable, therefore, strict say that what relate to is linear Granger cause effect relation.
B) oriented phase dry analysis partially
In order to obtain describing the frequency domain describing method of Granger causal connection, Baccala and Sameshima have proposed the relevant notion of inclined to one side orientation in calendar year 2001, order:
A ( ω ) = I - Σ r = 1 p a ( r ) e - iωr - - - ( 3 )
It represents the difference between the Fourier transformation of n dimension unit matrix I and coefficient sequence.Relevant for the inclined to one side orientation of the autoregressive process on p rank so | π I ← j(ω) | be defined as follows:
| π i ← j ( ω ) | = | A ij ( ω ) | Σ k | A kj ( ω ) | 2 - - - ( 4 )
Condition (2) has guaranteed that this denominator is positive all the time, thus this equation to can be good at defining inclined to one side orientation relevant.From definition as can be seen, and if only if all coefficient a IjWhen (r) being zero, for all frequencies omega | π I ← j(ω) | just do not exist, so x jTo there not being the Granger causal connection.This has just illustrated that inclined to one side orientation is relevant | π I ← j(ω) | provide one in frequency domain ω, to measure the right sex method of direct line.And because equation (4) is normalized form, therefore directed partially relevant value is on [0,1].It has contrasted x in the past jFor x iInfluence and x in the past jTo with the influence of its dependent variable, therefore, for a given signal source, the oriented phase dry analysis can be arranged variable by the intensity of influence partially.
Shown in Fig. 6 (a), three width of cloth figure on the diagonal (1,5,9) represent the power spectrum of signal, and the black curve among other figure is represented to have significantly directed partially relevant, and dotted line is represented the confidence level of p=0.05.Wherein abscissa is represented the source, and vertical coordinate is represented target, in last figure, can find out that electromyographic signal and C3 EEG signals and the C4 EEG signals of leading of leading all has the coherence, and C3 leads place's EEG signals will be a little more than the C4 place of leading to the coherence of electromyographic signal.And between the brain electric conductance connection tangible coherence is arranged.
Shown in Fig. 6 (b), under imagination action mode, three width of cloth figure on the diagonal (1,5,9) represent the power spectrum of signal, and the black curve among other figure is represented to have significantly directed partially relevant, and dotted line is represented the confidence level of p=0.05.Wherein abscissa is represented the source, and vertical coordinate is represented target, though also can show this point, and can find out clearly that the C3 influence that C4 is led of leading surpasses the influence that C4 leads C3 is led.Explanation is under imagination action mode, and EEG signals still is very active.
Of the present invention based on autonomous action, imagination action hypencephalon electromyographic signal conjoint analysis method, preliminary identification be concerned with practical prospect in the conjoint analysis of brain electromyographic signal of inclined to one side orientation, analyzed the dependency of EEG signals and electromyographic signal and EEG signals and EEG signals, and obtained and significantly tested effect, for further being applied to practical field, as aspects such as monitoring of motion auxiliary equipment and human motion proficiency assessments, provide good scientific basis and application foundation.

Claims (2)

1. one kind based on autonomous action, imagination action hypencephalon electromyographic signal conjoint analysis method, it is characterized in that, includes following steps:
1) carry out system's setting, that is, using the LabVIEW8.6 cycle that produces is 0.2 square-wave pulse signal as 10s, dutycycle;
2) carry out eeg signal acquisition and electromyographic signal collection respectively, comprising: independently move EEG signals and electromyographic signal under the mode, EEG signals and electromyographic signal under the imagination action mode;
Described autonomous action mode 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 connection figure, C4 place EEG signals, flexor digitorum superficialis place electromyographic signal, square-wave pulse signal simultaneously;
The described imagination is moved mode 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 C3, C4 place EEG signals, flexor digitorum superficialis (FDS) simultaneously and locate 60 seconds of electromyographic signal, synchronization pulse;
3) initial data of gathering is carried out the denoising pretreatment;
Described initial data to collection carries out the denoising pretreatment, be to use the Butterworth three rank band filters that contain 50Hz power frequency trap respectively EEG signals and electromyographic signal to be carried out Filtering Processing, according to effective frequency range feature of signal, choose EEG signals cut-off frequency: 0.5Hz and 40Hz; Electromyographic signal cut-off frequency: 0.5Hz and 200Hz afterwards, because the sample rate of initial data is very high, carry out down-sampled the processing to 512Hz to EEG signals and electromyographic signal respectively;
4) under the pretreated autonomous action mode of denoising and the brain electromyographic signal time-domain diagram under the imagination action mode independently moves under the mode and brain myoelectricity time-domain signal analysis under the imagination action mode;
5) to the brain electromyographic signal under the pretreated autonomous action of denoising, the imagination action mode carry out the time frequency signal analysis, described time frequency signal analysis is the wavelet transformation that adopts based on Morlet;
6) carrying out inclined to one side oriented phase dry analysis, specifically is to adopt the Granger causality to carry out inclined to one side oriented phase dry analysis.
2. according to claim 1 based on autonomous action, imagination action hypencephalon electromyographic signal conjoint analysis method, it is characterized in that the described LabVIEW8.6 of use of the step 1) cycle that produces is that 0.2 square-wave pulse signal comprises following process as 10s, dutycycle:
(1) sample rate and sampling configuration are set;
(2) generate the monocycle analog waveform;
(3) begin output;
(4) judge: whether acquisition time greater than 2, is to continue after the light-off to judge again, otherwise continue behind the bright lamp to judge again divided by 10 remainder.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2002102190A (en) * 2000-10-03 2002-04-09 Japan Science & Technology Corp Vital function measuring system
WO2006121455A1 (en) * 2005-05-10 2006-11-16 The Salk Institute For Biological Studies Dynamic signal processing
US7630757B2 (en) * 1997-01-06 2009-12-08 Flint Hills Scientific Llc System for the prediction, rapid detection, warning, prevention, or control of changes in activity states in the brain of a subject
CN102178524A (en) * 2011-05-23 2011-09-14 天津大学 Synchronization likehood-based electroencephalograph and electromyography synergistic analyzing method

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7630757B2 (en) * 1997-01-06 2009-12-08 Flint Hills Scientific Llc System for the prediction, rapid detection, warning, prevention, or control of changes in activity states in the brain of a subject
JP2002102190A (en) * 2000-10-03 2002-04-09 Japan Science & Technology Corp Vital function measuring system
WO2006121455A1 (en) * 2005-05-10 2006-11-16 The Salk Institute For Biological Studies Dynamic signal processing
CN102178524A (en) * 2011-05-23 2011-09-14 天津大学 Synchronization likehood-based electroencephalograph and electromyography synergistic analyzing method

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
Correlation between EEG-EMG coherence during isometric contraction and its imaginary execution;Y.Hashimoto et al.;《ACTA Neurobiologiae Esperimentalis》;20101231;第76-85页 *
Qi Yang et al..Single-Trial EEG-EMG coherence analysis reveals muscle fatigue-related progressive alterations in corticomuscular coupling.《Transactions on neural systems and rehabilitation engineering》.2010,第18卷(第2期),第97-106页.
Single-Trial EEG-EMG coherence analysis reveals muscle fatigue-related progressive alterations in corticomuscular coupling;Qi Yang et al.;《Transactions on neural systems and rehabilitation engineering》;20100430;第18卷(第2期);第97-106页 *
Y.Hashimoto et al..Correlation between EEG-EMG coherence during isometric contraction and its imaginary execution.《ACTA Neurobiologiae Esperimentalis》.2010,第76-85页.

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