CN101810479A - Phase characteristic extraction method for brain waves of compound imaginary movements of lower limbs - Google Patents

Abstract

The invention belongs to the field of bioengineering and computers, relating to a phase characteristic extraction method for brain waves of compound imaginary movements of lower limbs. The method comprises the following steps of: 1, collecting and preprocessing signals of brain waves of compound imaginary movements of lower limbs; 2, decomposing oscillation mode of brain waves of compound imaginary movements of lower limbs; 3, identifying characteristic oscillation mode; 4, extracting and identifying coordinate characteristic in functional areas; and 5, identifying mode identification. The method takes the unstability of the brain wave signals into consideration adequately, and has the maximum rate of identification of 87.8% which is obviously enhanced compared with 82.3% of the traditional method.

Description

The phase property extracting method of composite lower limb imaginary movement EEG

Technical field

The invention belongs to biomedical engineering and computer realm, relate to a kind of phase property extracting method of composite lower limb imaginary movement EEG.

Background technology

Brain-computer interface (Brain-Computer Interface, BCI) being to set up a kind of direct information that does not rely on conventional brain output channel (peripheral nervous and muscular tissue) to exchange and control channel between human brain and computer or other electronic equipments, is a kind of brand-new man-machine interaction system.The EEG signals that is applied to the brain-computer interface system the earliest mainly is the spontaneous brain electricity signal, such as the alpha in the brain electricity (α) ripple.But this class EEG signals pattern is single, can't really accomplish " consciousness control action ", has seriously restricted the development of brain-computer interface system.In recent years, the various countries scholar progressively carries out the research of EEG signals under the different mental awareness, and this has brought new dawn for the development of brain-computer interface.

There are some researches show: in the imagination but when not implementing the action of limbs or other body parts, similar electric physiological responses when the brain motor cortex zone relevant with this action also can take place to this action enforcement is called as imagination action potential.On microcosmic, imagination action potential shows as the change of state of cortex motorium neuron pool electrical activity, (functional magnetic resonance image, fMRI) method of observation brain local blood figure is confirmed by Functional MRI clinically.On macroscopic view, imagination action potential shows as the modulation phenomenon that EEG signals is subjected to some rhythm and pace of moving things.People such as Jasper have found the amplitude modulation(PAM) phenomenon of imagination action potential the earliest, promptly when the people is making the actual action or the imagination and moves, can cause the energy changing characteristics of EEG signals under some rhythm and pace of moving things.Up in recent years, brain Cognitive Study expert finds, imagination action potential shows as the modulation phenomenon that EEG signals is subjected to some rhythm and pace of moving things and not only shows the amplitude aspect, shows as the Phase synchronization feature simultaneously.

The phase place modulation phenomenon of imagination action potential is found later, Varela and colleague thereof the sectional cooperation of each cortex motor function and finding when integrating in the research brain, when the people when carrying out the limb action of the reality or the imagination, between the neuron of subregion self inside, and the EEG signals between each subregion reveals significant synchronization phenomenon by the phase place modulomenter.Although up to the present, the physiological Mechanism of synchronization phenomenon also is not very clear, but during the synchronization phenomenon that phase place modulation that it is generally acknowledged imagination action potential causes balance the cerebral activity of dispersed and distributed on anatomical structure and functional structure, thereby conforming behavior and cognitive activities have been realized, synchronization is that brain is as a whole specific cortex and the integration of cortex lower area, thus the main mechanism of consummatory behavior and cognitive function.Up to the present, the Phase synchronization feature the when experts and scholars in neural engineering field have successfully extracted the upper limb action imagination between miscellaneous function district and the primary motor area, and be used for the brain-computer interface system.The application of synchronization feature in the brain-computer interface system progressively paid attention to and grown up.But the Phase synchronization feature extraction of simple lower limb imaginary action potential is difficulty very, its main cause is: the cortical functional district that lower extremity movement shone upon for the head ditch return in narrower and small zone, the discrimination of its space structure has been very limited, the EEG signals of scalp electrode extraction exists very big dispersivity and aliasing in addition, be unfavorable for that very source signal obtains and discerns, this factor has caused equally by simple lower limb movement imagination brain electricity and has extracted very difficulty of energy feature.Therefore, wish to show one's talent, progressively become brain-computer interface and realize one of rehabilitation walk help systematic research emphasis by the systematic research of composite lower limb imaginary movement EEG realization brain-computer interface.Through development in recent years, this research direction has been considered to the only way that brain-computer interface further develops, so far, part scholar has carried out the correlational study of the energy feature of composite lower limb imaginary movement EEG, and from phase angle, the phase property of research composite lower limb action imagination brain electricity, being expected provides new opportunity for the development of the brain-computer interface of realizing being used for the lower limb rehabilitation walk help.

Summary of the invention

Purport of the present invention is the energy feature extraction method that proposes a kind of composite lower limb imaginary movement EEG, solve based on the basic problem in the lower limb rehabilitation walk help system of brain-computer interface with this: realize that paralytic patient Autonomous Control lower limb movement realizes the key operations of normal walking, about the Autonomous Control of taking a step to move.The energy feature that the invention solves composite lower limb imaginary movement EEG accurately extracts problem, thereby be correct identification composite lower limb action pattern, effectively be converted to the control command that is applied to lower limb rehabilitation walk help system, the autonomous walking of realization paralytic patient provides and has provided powerful support for.

Purport of the present invention is the phase property new method for extracting that proposes a kind of composite lower limb imaginary movement EEG, solve based on the key problem in the lower limb rehabilitation walk help system of brain-computer interface with this: 3 key operations that realize the normal walking of paralytic patient Autonomous Control lower limb movement realization, stand left and right Autonomous Control of taking a step to move.When the present invention can effectively extract the action of the composite lower limb imagination, mutual cooperation relation between the supplementary motor area of cortex and the primary motor area, thereby be correct identification composite lower limb action pattern, effectively be converted to the control command that is applied to lower limb rehabilitation walk help system, the autonomous walking of realization paralytic patient provides and has provided powerful support for.

A kind of phase property extracting method of composite lower limb imaginary movement EEG comprises the following steps:

1. utilize the collection of brain electric conductance connection electrode to stand, the lower limb co-operating of a left hand left side, the right lower limb co-operating of the right hand 3 class composite lower limb imaginary movement EEG signals;

2. the EEG signals that is positioned at primary motor area and miscellaneous function district that is collected is carried out space filtering, improve the signal to noise ratio of EEG signals;

3. through step after 2. handling, choose and be positioned at the EEG signals of leading in a left side/right primary motor area and supplementary motor area center, carry out empirical modal and decompose, it is decomposed into frequency each natural oscillation mode component from high to low respectively.

4. the natural oscillation mode component rated output spectrum density of the brain electricity that respectively leads that 3. step is obtained according to the frequency distribution scope of its power spectral density, is determined the natural oscillation mode component of the brain electrical feature rhythm and pace of moving things-alhpa rhythm and pace of moving things correspondence, obtains the characteristic oscillation pattern.

5. define the Phase synchronization parameter-phase place lock value of reflection function district cooperation relation, $\mathrm{PLV}=\left|{<e^{j{\mathrm{\&phi;}}_{\mathrm{xy}}\left(t\right)}>}_t\right|,$ Wherein＜ _tBe illustrated in certain time period and average, || expression is to signal delivery, φ _Xy(t) two characteristic oscillation pattern x of expression (t) are poor with the instantaneous phase of y (t), when calculating the 3 class composite lower limbs action imagination, and the phase place lock value between a left side/right primary motor area and the supplementary motor area characteristic of correspondence oscillation mode;

6. select 5. to obtain to represent the Phase synchronization feature of functional areas cooperation relation to import step, realize the pattern recognition of 3 class composite lower limbs imagination action as grader based on the support vector base grader of basic kernel function radially.

As preferred implementation, the energy feature extraction method of composite lower limb imaginary movement EEG of the present invention, the EEG signals that 3. step is handled comprises C3, C4, FCz lead signals; Carry out the characteristic oscillation pattern respectively and obtain being positioned at the EEG signals of leading in a left side/right primary motor area and supplementary motor area center, then calculate the 4-5 second during action is imagined successively, 5-6 second, 6-7 second, under 7-8 second 4 time periods, represent the phase place lock value of 3 mutual cooperation relations in functional areas, constitute phase place lock value coefficient vector, constitute 12 dimensional feature vectors through the splicing back according to lead position and time sequencing:

PLV wherein _C3-FCz ¹..., PLV _C3-FCz ⁴Be followed successively by the phase place lock value between the characteristic oscillation pattern of lead under 4 time periods C3 and FCz; PLV _C4-FCz ¹..., PLV _C4-FCz ⁴Be followed successively by the phase place lock value between the characteristic oscillation pattern of lead under 4 time periods C4 and FCz; PLV _C3-C4 ¹..., PLV _C3-C4 ⁴Be followed successively by the phase place lock value between the characteristic oscillation pattern of lead under 4 time periods C3 and C4; The relevant desynchronization coefficient vector of the incident that 5. step is obtained

As grader input sample, the grader formula is y _i=sgn (W _iF+b _i) i=1,2,3., W _iBe weight function, b _iThreshold value obtains by training sample, y _iBe two sorting result labellings (± 1), classification results is decided by the cumulative voting result of support vector machine (SVM) grader of 3 " one to one " two classification modes.

The present invention obtains alpha rhythm and pace of moving things energy changing characteristics by empirical mode decomposition method in conjunction with power spectral-density analysis and Hilbert transform method, the highest discrimination of this method is 87.8%, be significantly increased with respect to 82.3% of traditional method, it is advantageous that and fully take into account the non-stationary of EEG signals, and the incident related synchronization/feature that desynchronizes based on action imagination brain electricity is the conclusion that is produced by colony's neuron synchronized oscillation, therefore will have more wide application prospect in the brain-computer interface system based on compound limb action, this will provide for the development based on the lower limb rehabilitation walk help system of brain-computer interface and provide powerful support for.

The present invention is devoted to solve the key issue based in the lower limb rehabilitation walk help system of brain-computer interface, and promptly the energy changing characteristics of composite lower limb imaginary movement EEG is extracted problem.Effective extraction of the energy feature of lower limb imaginary movement EEG, be realize real fully by the core technology of the lower limb rehabilitation walk help system of the will control of quadriplegia patient or paralytic patient: by extracting the EEG signals that the patient imagines lower limb movement, be converted to the external control order of corresponding lower limb rehabilitation walk help system, help the patient to walk and muscular irritation recovers with this.This fully by the system of the autonomous will of patient control, not only can realize this part disabled patient recurrence of orthobiosis, and can promote patient's self-confidence, therefore be with a wide range of applications and great social significance.

Description of drawings

The Phase synchronization characteristic extraction procedure of Fig. 1 characteristic oscillation pattern;

The compound limbs imagination of Fig. 2 action experiment period distribution diagram;

During Fig. 3 composite lower limb action imagination, the electrode laying method of the interval synchronization research of primary motor area and assisted movement;

During Fig. 4 left hand left side lower limb co-operating imagination, based on bandpass signal (8～14Hz) Phase synchronization fractional analysis;

During Fig. 5 left hand left side lower limb co-operating imagination in the EEG signals of leading position C4;

The empirical modal decomposition result of Fig. 6 left hand left side lower limb co-operating imagination brain electricity (C4 leads);

During Fig. 7 left hand left side lower limb co-operating imagination, the C4 power spectral density of natural oscillation pattern of the EEG signals power spectral density of (a) first natural oscillation pattern (main frequency band 18-24Hz) (b) power spectral density of the second natural oscillation pattern (main frequency band 8-13Hz belongs to the alpha rhythm and pace of moving things) (c) power spectral density of the 3rd natural oscillation pattern (main frequency band 4-10Hz) (d) power spectral density of the 4th natural oscillation pattern distribute (main frequency band 2-5Hz) that distributes that distributes that distributes that distributes of leading;

During Fig. 8 left hand left side lower limb co-operating imagination, C3, C4, FCz lead ear under the alpha rhythm and pace of moving things with reference to and the characteristic oscillation mode signal waveform of FCz reference;

The electric left lower limb co-operating imagination right lower limb co-operating imagination of (b) right hand of Phase synchronization curve (a) left hand of brain (c) of the sub-rhythm and pace of moving things of alpha frequency range feature stands to move and imagines during Fig. 9 three class composite lower limbs action imagination;

The phase place lock value of composite lower limb imagination action potential under Figure 10 alpha rhythm and pace of moving things.

The specific embodiment

Levy the extraction difficult point at composite lower limb imaginary movement EEG: the cortical functional district that lower extremity movement shone upon for the head ditch return in narrower and small zone, the discrimination of its space structure is very limited, the action of the composite lower limb imagination relates to a plurality of brain domains in addition, the pattern complexity has further been given prominence to the non-stationary property of EEG signals.This has just caused, and traditional the signal processing method of stationary hypothesis is limited in the suitability aspect the feature extraction of lower limb imaginary movement EEG in short-term based on EEG signals.For this reason, the present invention is directed to the non-stationary property of EEG signals, and when imagining based on limb action, mutual cooperation relation between miscellaneous function district and the primary motor area is presented as the Phase synchronization feature between colony's neuron synchronized oscillation signal, the characteristic oscillation pattern that application experience mode decomposition method extracts composite lower limb imaginary movement EEG is proposed, and then obtain the instantaneous phase of miscellaneous function district and primary motor area characteristic oscillation pattern, the Phase synchronization feature between the abstraction function district then, with this input parameter, realize effective identification of 3 class composite lower limbs imagination action as the EEG signals pattern recognition.

Divide several aspects that EEG signals extracting method of the present invention is elaborated below.

1 empirical modal decomposes (Emprical Mode Decomposition, EMD) the Li Lun proposition and the suitability thereof

In traditional Fourier analyzes, frequency be defined as having in the whole analytical data length certain amplitude just, cosine function.Be subjected to the influence of this intrinsic notion, people are in understanding and accept the meaning of instantaneous frequency and during notion, always from just, the relevant angle of cosine function analyzes.Like this when people define the local frequencies value just need more than one-period just, cosine fluctuation, based on this logic, the signal that is less than one-period length can't provide the definition of its frequency.And for non-linear and non-stationary signal, become when its principal character frequency is, promptly only be in a certain local time, to exist or once at a time occurred, to close over time and fasten describing frequency, the Fourier conversion is obviously powerless.In order to remedy the deficiency that Fourier transfer pair time varying signal is analyzed, people are to the primary signal windowing, think that the signal in " arrowband " is stably or is similar to stably at certain, and then the signal in the window analyzed, as short time discrete Fourier transform, wavelet analysis etc., these methods are described non-linear and time variation non-stationary signal in varying degrees, have improved the deficiency of Fourier conversion greatly.But owing to be subjected to the restriction of Heisenberg uncertainty principle, it is minimum that the resolution on time and frequency can not reach simultaneously, and therefore, the result of gained is the average result of signal in the window, does not equally also break away from the limitation of Fourier conversion.

For signal Spectrum Analysis being accurate on each time point, the Frequency point, US National Aeronautics and Space Administration, people such as U.S. academician of the Chinese Academy of Engineering Huang N E have proposed empirical modal decomposition (Emprical Mode Decomposition, EMD) method.1996, Huang proposed this imagination one of the new method that non-stationary signal analyzes that is suitable for first based on empirical pattern decomposition method in international academic conference once.Huang thinks that for transient state and non-stationary phenomenon, frequency and energy generally all are the functions of time, therefore need provide the definition of instantaneous frequency and instantaneous energy.The signal transient concept of energy is widely accepted at present, but the notion of instantaneous frequency and meaning but have dispute always.After the Hilbert conversion that can make data parsingization occurs, the people's function that can express initial data amplitude and phase place fully that conversion provides according to Hilbert, provided the unified Definition of instantaneous frequency, instantaneous frequency is the monotropic function of time from defining as can be seen, promptly only has an oscillation mode at any time.So when using this notion of instantaneous frequency, corresponding data have been subjected to certain restriction.This is main because any one moment, may comprise a plurality of oscillation modes in the data, this moment, the Hilbert conversion can not provide this signal frequency content completely, and resulting result is the average effect of a plurality of oscillation modes, thereby the meaning of instantaneous frequency thickens.In order from sophisticated signal, to obtain significant instantaneous frequency, Huang is according to the essential condition on the instantaneous frequency physical significance, proposing the data decomposition that contains a plurality of oscillation modes is to satisfy the linear superposition of a plurality of single oscillation mode component of certain condition, each single oscillation mode component is called a basic model component again, and has proposed a kind of based on empirical pattern decomposition method.Each single-mode component all satisfies the essential condition of Hilbert conversion, makes the instantaneous frequency of finding the solution signal with the Hilbert conversion become possibility.

The meaning of Empirical mode decomposition is: the definition of signal frequency is based on the local feature and the instantaneous feature of waveform in signal analysis, it can be on each time point of signal data, provide the instantaneous frequency value from variation characteristic between points, rather than need the waveform of a plurality of cycles of oscillation just can provide a frequency values.If there is a frequency, the information of only representing this frequency correspondence existed in a certain local time or once at a time occurred during Empirical mode decomposition was analyzed.So no matter from conceptive still on signal analysis essence, this analytical method has been broken legacy frequencies thought, has provided a brand-new frequency concept.Empirical mode decomposition is significant to signal analysis, and also handling for non-stationary signal simultaneously provides new approaches, has opened up new way.

2 empirical modal decomposition algorithms

In order to resolve into the inherent feature oscillation mode to general data, Huang N E has proposed the method that empirical modal decomposes.Different with in the past nearly all decomposition method, this new method be intuitively, direct, posterior and adaptive, the basic function of its decomposition is based on data and derives from data itself.

Empirical mode decomposition method is to be based upon on the following hypothesis: (1) signal has two extreme points at least, a maximum and a minima; (2) characteristic time scale is to define by the interval between two extreme points; (3) if data deficiency extreme point but deformation point is arranged then can once or several times obtain extreme point by the data differential, and then obtain decomposition result by integration.

The essence of this method is that the empirical features time scale by data obtains its intrinsic oscillation mode, then decomposition data in view of the above.According to the experience of Drazin, the first step of data analysis is the manual observation data, and the two kinds of methods that can directly distinguish the different scale oscillation mode are arranged: observe alternately occur successively greatly, the interval between minimum point; With the interval of observing the zero crossing that occurs successively.Alternative Local Extremum and zero crossing have formed complicated data: a fluctuation rides in another fluctuation, they ride over again in other the fluctuation simultaneously, the rest may be inferred, and each fluctuation has all defined a characteristic dimension of data, and this characteristic dimension is intrinsic.Take the time scale of the interval of the extreme point that occurs successively as oscillation mode, because this method not only has higher resolution to oscillation mode, and can be applied to the data of Non-zero Mean, and for example there is not zero crossing, the total data point is positive or minus data.For various oscillation modes are come out from extracting data successively, use a kind of method of system, i.e. empirical mode decomposition method, or be referred to as the process of " screening " visually, carry out the empirical modal step of decomposition to real signal s (t) and be:

1) determines all maximum and the minimum of s (t);

2) make the envelope up and down that the cubic spline difference is constructed s (t) according to maximum and minimum;

3), calculate local mean value (going up the meansigma methods of the lower envelope) m of s (t) according to envelope up and down ₁And s (t) and m (t), ₁(t) difference h ₁(t)=s (t)-m ₁(t);

4) with h ₁(t) replace primary signal s (t), repeat above three steps k time, (h till the average envelope of gained goes to zero _{1, (k-1)}(t) and h _{1, k}(t) variance between is less than setting value), promptly think h _{1, k}(t) be an IMF component, note c ₁(t)=h _{1, k}(t), r ₁(t)=s (t)-c ₁(t), s (t)=r ₁(t); First IMF component is represented the component of highest frequency in the initial data.Original data sequence s (t) is deducted first IMF component c ₁(t), can obtain a difference data sequence r who removes high fdrequency component ₁(t).

5) to r ₁(t) repeat above four step tranquilization processing procedures, can obtain second IMF component c ₂(t), so repeat down sequence of differences r to the last _n(t) (r till undecomposable _n(t) less than a setting value, when perhaps becoming a monotonic function), the empirical modal of primary signal decomposes end, and the breakdown that obtains s (t) is as follows:

$s\left(t\right)=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{c}_i\left(t\right)+r_n\left(t\right)---\left(1\right)$

R wherein _n(t) be remaining function, represent the trend or the average of initial data,

Because each IMF component all is a data sequence of representing a stack features yardstick, so the processing procedure of this tranquilization is actually the stack that original data sequence is decomposed into the different characteristic fluctuation.Need to prove, each IMF component both can be linear also can be non-linear.

The empirical modal algorithm is actual to be the process of a screening, at first the highest composition of signal intermediate frequency rate is screened, and then from original signal this composition is removed, and selects the highest composition of frequency again from new signal, and the rest may be inferred, till signal is undecomposable.This process can be seen a series of filter bank as, and from the above description of algorithm as can be known, signal can be finished decomposition by limited screening step.During each the screening, the maximum of new signal, the number of minima are all reducing.In order to reduce the screening step of extracting IMF, the parameter S of knowing clearly surely D:

$\mathrm{SD}=\underset{0}{\overset{T}{\mathrm{\&Sigma;}}}\frac{{|{\mathrm{<figure-callout\; id="1"\; label="h"\; filenames="H2009102448191E00011.png,H2009102448191E00031.png"\; state="\{\{state\}\}">h</figure-callout>}}_{1,(k-1)}\left(t\right)-h_{1,k}\left(t\right)|}^2}{{\mathrm{<figure-callout\; id="1"\; label="h"\; filenames="H2009102448191E00011.png,H2009102448191E00031.png"\; state="\{\{state\}\}">h</figure-callout>}}_{1,(k-1)}{\left(t\right)}^2}---\left(2\right)$

When SD stops screening during less than a certain constant, the value of general SD is between 0.2 to 0.3.In addition in screening process be cubic spline interpolation because this algorithm adopts, so when the maximum of signal or minimizing number less than 2 the time, stop to screen.These limited IMF are the instantaneous energy with practical significance and the instantaneous frequency of parameter with time through having produced after the Hilbert transform.Can effectively analyze signal simultaneously in time domain and frequency domain like this, this specific character is to be that the based signal analytical method does not have with the Fourier transform in the past.

Because the empirical modal method is the Time Domain Decomposition that the time-domain information according to data itself carries out, the common number of the IMF that obtains is limited and stably, and be narrow band signal with practical significance, its result of Hilbert conversion who carries out based on these IMF components has reflected real physical message, and amplitude and the frequency of each IMF that obtains based on the Hilbert conversion of empirical modal be time dependent, eliminated the simple harmonic wave of the unnecessary no physics meaning non-linear for reflecting in the classical Spectral Analysis Method, that non-stationary process is introduced.Therefore, its Hilbert spectrum also can accurately reflect signal energy, the distribution of frequency on space or time scale.The empirical modal method is based on that the local feature time scale of signal realize to decompose.Compare with wavelet analysis method, empirical modal-Hilbert method has whole advantages of wavelet analysis, and overcome the non-self-adapting of wavelet transformation, therefore this Hilbert frequency spectrum analysis method based on empirical modal has very high using value in the analysis of non-linear and non-stationary process.

In essence, empirical modal is the pretreatment that data were done before carrying out the Hilbert conversion.By empirical modal, data are broken down into the set of some intrinsic mode functions (IMF), and each IMF has portrayed a simple oscillation pattern of signal.From expression-form, IMF is similar to Fourier and decomposes a simple harmonic oscillation in the expression formula, and still, it is than simple harmonic oscillation vague generalization more.Though the proposition of empirical mode decomposition method, for the analysis of non-linear non-stationary algorithm provides strong instrument, but this new method also is in developmental stage, runs into a lot of problems in actual applications, and existing at present a large amount of scholars are devoted to the research and the application of this method.

The Phase synchronization analysis of 3 characteristic oscillation patterns of the present invention

In order to obtain the Phase synchronization feature of characteristic oscillation pattern, we must extract the instantaneous phase of each characteristic oscillation pattern at first respectively, then compare the time varying characteristic of phase-delay quantity in twos.

" time " and " frequency " is two the most frequently used physical quantitys in the Digital Signal Analysis and Processing.To periodic signal, its frequency is defined as the inverse of period T, i.e. f=1/T, and it represents that this signal repeats the number of times that changes in the unit interval.To nonperiodic signal, we can be decomposed into it the stack of infinite multicycle signal simply, and the cycle of these subsignals is continually varyings, and its inverse is exactly the continually varying frequency naturally.In both cases, " frequency " speech all closely links to each other with the Fourier conversion.In fact, Fourier conversion

$X\left(\mathrm{j\&Omega;}\right)=\underset{-\&infin;}{\overset{+\&infin;}{\&Integral;}}x\left(t\right)e^{-\mathrm{j\&Omega;t}}\mathrm{dt}=<x\left(t\right),e^{-\mathrm{j\&Omega;t}}>---\left(1\right)$

Can be regarded as x (t) at basic function e ^{J Ω t}On projection since Ω can from-∞～+ ∞, so (1) formula is that x (t) is projected on the basic function of an infinite dimension space.Thus, the frequency that is drawn by (1) formula is called " Fourier frequency ", and it is that x (t) is in the resulting frequency of whole time shaft upper integral.But in real world and engineering reality, also exist another frequency, be called " instantaneous frequency ".

If x (t) is a complex signal, we always be write it as analytical form so long, promptly And instantaneous frequency Ω _i(t) be defined as

To the derivative of t, promptly

Or

Here i representative is instantaneous.Obviously instantaneous frequency is relative analytic signal, and it is the derivative of analytic signal phase place.

As x (t) when being real signal, can be by finding out its complex conjugate y (t) to generate analytic signal z (t):

z(t)＝x(t)+iy(t)＝a(t)e ^jφ(t)???????????????????????????(3)

A (t)=[x wherein ²(t)+y ²(t)] ^1/2, φ (t)=arctan (y (t)/x (t)) (4)

In theory, the method for infinite multiple definable analytic signal z (t) imaginary part is arranged, but can make the define method of formula (3) imaginary part unique by the Hilbert conversion, the imaginary part of analytic signal is the Hilbert conversion of signal s (t), is calculated as follows:

$y\left(t\right)=\frac{1}{\mathrm{\&pi;}}\underset{-\&infin;}{\overset{\&infin;}{\&Integral;}}\frac{x\left(\mathrm{\&tau;}\right)}{t-\mathrm{\&tau;}}\mathrm{d\&tau;}=x\left(t\right)*\frac{1}{\mathrm{\&pi;t}}---\left(5\right)$

The physical significance of following formula shows that the Hilbert conversion is the convolution of the 1/t reciprocal of signal x (t) and time t in the expression formula of time domain, and therefore, the local characteristics of signal x (t) has been emphasized in the Hilbert conversion.

After obtaining the instantaneous phase of characteristic oscillation pattern by (4) formula, we can carry out second step of Phase synchronization fractional analysis method, and phase-locked value is found the solution and quantized.That is, at first obtain the analytic signal of signal x (t), to obtain x (t) the instantaneous phase φ of t (t) at any time by the Hilbert conversion.It is poor with the instantaneous phase of y (t) then can to obtain two signal x (t):

φ _xy(t)＝φ _x(t)-φ _y(t)??????????????????????????????????(6)

Thus can according to following defined formula calculate phase place lock value between two signals (phase lockingvalue, PLV):

$\mathrm{PLV}=\left|{<e^{j{\mathrm{\&phi;}}_{\mathrm{xy}}\left(t\right)}>}_t\right|---\left(7\right)$

＜ _tBe illustrated in certain time period and average, || expression is to the signal delivery.The span of PLV is [01], and x (t) and y (t) are complete when asynchronous, and the instantaneous phase difference is obeyed uniform distribution, PLV=0; When x (t) and y (t) were synchronous fully, the instantaneous phase difference was a constant, PLV=1.

4 phase property extraction algorithms

The phase property extraction algorithm flow process of composite lower limb imaginary movement EEG of the present invention is described below:

1. utilize brain electric conductance connection electrode to gather composite lower limb imaginary movement EEG;

2. the EEG signals that is positioned at primary motor area and miscellaneous function district that is collected is carried out space filtering, improve the signal to noise ratio of EEG signals;

After 3. 2. handling through step, choose and be positioned at the lead EEG signals of position of a left side/right primary motor area and supplementary motor area, carrying out empirical modal and decompose, is frequency each natural oscillation mode component from high to low with the composite lower limb imaginary movement EEG signal decomposition of position, 3 functional areas.

4. be positioned at the lead natural oscillation mode component rated output spectrum density of EEG signals of position of a left side/right primary motor area and supplementary motor area to what 3. step obtained, frequency distribution scope according to its power spectral density, determine the natural oscillation mode component of the brain electrical feature rhythm and pace of moving things-alhpa rhythm and pace of moving things correspondence, i.e. the characteristic oscillation pattern that the present invention will obtain.

5. define the Phase synchronization parameter-phase place lock value of reflection function district cooperation relation, $\mathrm{PLV}=\left|{<e^{j{\mathrm{\&phi;}}_{\mathrm{xy}}\left(t\right)}>}_t\right|,$ Wherein＜ _tBe illustrated in certain time period and average, || expression is to signal delivery, φ _Xy(t) two characteristic oscillation pattern x of expression (t) are poor with the instantaneous phase of y (t), when calculating the 3 class composite lower limbs action imagination, and the phase place lock value between a left side/right primary motor area and the supplementary motor area characteristic of correspondence oscillation mode.

6. select based on the support vector base grader of basic kernel function radially, 5. obtain to represent the Phase synchronization feature of functional areas cooperation relation to import step as grader, realize the pattern recognition of 3 class composite lower limbs imagination action (standing the lower limb co-operating of a left hand left side, the right lower limb co-operating of the right hand).

The acquisition process of the Phase synchronization feature of characteristic oscillation pattern can be represented with Fig. 1.

5 embodiment

The 128 conducts digital eeg recording instrument that the present invention adopts Austrian EMSPHOENIX company to produce are gathered the equipment of eeg data.The experimenter experimentizes in a room that electromagnetic shielding is good, sound insulation is good, and the background noise in the room is about 31dB, and background illumination is 2cd/m ²The experimenter to feel comfortable but the posture that does not influence data acquisition sit in an armchair.Dead ahead apart from about 1 meter of experimental subject is 19 inches display, is used to show that experimental subject carries out the prompt of the composite lower limb action imagination.The sub-Therapy lasted of each composite lower limb imagination action 10 seconds.First period, screen was not for having the blank screen state that shows, 2 seconds persistent period in order to loosen the stage.Second period was a stage of preparation, and this period screen centre shows a cross prompt, and the prompting experimenter is ready, this window duration 2 seconds.Phase III is the imagination action phase, and this window duration 4 seconds shows left or arrow orientation prompt to the right on the computer screen at random, requires the experimenter to carry out corresponding left lower limb and left hand and moves the imagination or the right hand and right lower limb simultaneously and move the imagination simultaneously.The quadravalence section is convalescent period, and display remains in the period does not have the blank screen state that shows.Require the experimenter to keep relaxation state in the experimentation, do not allow any actual act, and for avoiding the experimenter owing to the brain wave that visual stimulus causes is moving, display is with the mode display reminding symbol of blank screen ash word.Whole experimental program requires each experimenter to finish 3 groups of experiments, and each group experiment (run) comprises each 30 second sons experiment (trail) of 2 class composite lower limbs imagination action.Between per two groups of experiments, leave the sufficiently long time of having a rest and be used for the experimenter and carry out fatigue recovery.As shown in Figure 2.The experimenter has participated in the training that the EEG signals energy feature extracts experiment, can reach to imagine action brain electricity experiment effect preferably.

International 10/20 system standard is adopted in the placement of electrode, as shown in Figure 3, writes down near 41 EEG data of leading of body major beat functional areas simultaneously.This 41 leads brain electric conductance connection and comprises common accepted standard 19 and lead brain electric conductance connection, all the other 22 lead be according to the present invention in the brain wave acquisition purpose, cover the function map section of human limb action, obtain in the compound limb action imagination process more meticulous brain electrical feature with this.The present invention is devoted to obtain the cooperation relation feature between both sides primary motor area and the supplementary motor area, so the synchronization analytical calculation is the phase place lock value that C3/C4 leads and FCz leads and makes up in twos.Specifically as shown in Figure 3.

Electrode adopts the Ag/AgCI electrode, and with left ear-lobe (A1) as with reference to level, auris dextra hangs down (A2) as with reference to ground, the electric sample frequency of brain is 256Hz, filter pass band is 0.5～35Hz.Electrode impedance is less than 5000 ohm.

After data acquisition finished, for improving the later stage accuracy of pattern recognition, we carried out space filtering by the linear combination of a plurality of data of leading, to improve the signal to noise ratio of EEG signals.At first adopted the datum of removing EEG signals altogether with reference to averaging method of Hjorth proposition, promptly deduct the average of all electrode signals from primary signal, its computational methods are as follows:

$V_i^{\mathrm{CAR}}=V_i^{\mathrm{ER}}-\frac{1}{n}\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{V}_i^{\mathrm{ER}}---\left(8\right)$

Wherein n is the electrode sum, V _i ^ERIt is the primary signal that collects on the electrode, by altogether with reference to average computation, space low frequency composition total in most of electrode will be removed, therefore altogether with reference to averaging method act as the high pass space filtering, can give prominence to the electric composition of brain of high concentration on spatial distribution.Then will still exist the sub-experimentation (trail) of more myoelectricity interference or eye electrical interference to remove in the gained signal.

We at first adopt traditional based on the band filter method, analyze the cooperation relation between primary motor area and the supplementary motor area.Fig. 4 has provided the synchronization analytical method that adopts bandpass signal, when a left leftward lower limb co-operating of experimenter JJN is imagined, the analysis result of cooperation relation between primary motor area under the alpha rhythm and pace of moving things and the supplementary motor area, the frequency band range that is used to calculate phase place lock value value is 8～14Hz.As can be seen from the figure, there is not the synchronization phenomenon between the primary motor area on left side and right side, the Phase synchronization phenomenon occurs between primary motor area (C3/C4) and miscellaneous function district (FCz), but can't judge that the primary motor area of which side and the synchronization degree between the miscellaneous function district are stronger.

Above-mentioned in order to solve based on the problem in the synchronization analytical method of passband signal, we introduce empirical modal analysis method, by obtaining the characteristic oscillation pattern of miscellaneous function district and primary motor area brain electricity, then carry out the Phase synchronization fractional analysis based on the characteristic oscillation pattern.When Fig. 5 has provided left hand left side lower limb and has moved the imagination simultaneously, in the EEG signals of the single experiment of the C4 record that leads.

We carry out empirical modal and decompose signal shown in Figure 5.Take empirical modal to decompose to the C4 shown in Figure 5 EEG signals of leading, 8 natural oscillation patterns (IMF) of acquisition and a residual components (res), as shown in Figure 6.

In order to obtain the characteristic oscillation pattern of the brain electrical feature rhythm and pace of moving things alpha rhythm and pace of moving things, the natural oscillation pattern of Fig. 6 gained is carried out power spectral-density analysis.Fig. 7 has provided 4 power spectral density plot of preceding 4 natural oscillation patterns (IMF) of leading EEG signals.As can be seen from the figure, the characteristic oscillation pattern of the alpha rhythm and pace of moving things is present on the second natural oscillation pattern imf2, and promptly imf2 is the characteristic oscillation pattern under the alpha rhythm and pace of moving things.

Utilize above-mentioned steps, obtain the C3 that leads successively, the characteristic oscillation pattern of FCz under the alpha rhythm and pace of moving things then obtained the instantaneous phase of each characteristic oscillation pattern, carries out the Phase synchronization fractional analysis.

Among the present invention, by EMD method obtained respectively to lead characteristic oscillation pattern under the alpha rhythm and pace of moving things, the characteristic oscillation pattern is the result of each functional areas neuron pool synchronous discharge, and the synchronization phenomenon of each functional areas will inevitably appear between the characteristic oscillation pattern that correspondence leads.

Fig. 8 has provided the alpha prosodic feature oscillation mode waveform of the single experiment of experimenter JJN under imagination left hand left side lower limb co-operating imagination task, comprised under the alpha rhythm and pace of moving things lead characteristic oscillation pattern and be the C3 of reference, the characteristic oscillation pattern that C4 leads (difference of C3 and FCz, and the difference of C4 and FCz) of C3, C4, FCz with FCz.As can be seen, the difference of relative C3 with the difference of FCz of C4 and FCz is littler, and promptly C4 leads and FCz leads and has more significant dependency between the characteristic oscillation pattern.When this had also illustrated the left hand left side lower limb co-operating imagination, right side primary motor area and supplementary motor area had cooperation relation more closely.

Fig. 8 has illustrated that also it is feasible carrying out Phase synchronization research based on the characteristic oscillation pattern.For all kinds of different composite lower limbs imagination action potentials, exist different mutual cooperation relations between each functional areas.This research is attempted its Phase synchronization feature is extracted based on the phase analysis of characteristic oscillation pattern.

Fig. 9 (a) and (b), (c) have provided under the sub-rhythm and pace of moving things of alpha frequency range feature, when typical case experimenter JJN imagines this 3 class composite lower limb action imagination with the lower limb co-operating imagination, the right hand and the lower limb co-operating imagination, the action that stands leftward, the Phase synchronization curve in a left side/right elementary motor function district and miscellaneous function district.

According to said method, 10 experimenters are analyzed in the Phase synchronization feature of the alpha rhythm and pace of moving things, Figure 10 has provided the statistic analysis result of 3 class composite lower limbs imagination action phase place lock value of (4～8 seconds) during whole imagination action.As can be seen from the figure, for the characteristic oscillation pattern of the alpha rhythm and pace of moving things, the action of the 3 class composite lower limbs imagination all shows as the primary motor area synchronization phenomenon different with supplementary motor area.The scope of synchronized phase place lock value is 0.27～0.67.The synchronization phenomenon of 3 class composite moves shows as respectively: when the left hand and the homonymy list lower limb co-operating imagination, the synchronization degree of the C4 of offside and FCz all is higher than homonymy C3 and FCz, and the phase place lock value of C3 and C4 is respectively 0.33 and 0.39 under the alpha rhythm and pace of moving things and the beta rhythm and pace of moving things, and promptly there is not tangible synchronization phenomenon in the both sides motor cortex; When the right hand and the homonymy list lower limb co-operating imagination, the synchronization degree of the C3 of offside and FCz all is higher than homonymy C4 and FCz, and the phase place lock value of C3 and C4 is respectively 0.34 and 0.36 under the alpha rhythm and pace of moving things and the beta rhythm and pace of moving things, and promptly there is not tangible synchronization phenomenon in the both sides motor cortex yet; When standing the action imagination, the Phase synchronization scope that C3, C4 and FCz make up in twos is 0.26～0.34, illustrates in the course of action that stands, and left side primary motor area, right side primary motor area and supplementary motor area do not have remarkable synchronization phenomenon.

According to abovementioned steps, key to a left side/right primary motor area and miscellaneous function zone position lead (being C3, C4, FCz) carry out the characteristic oscillation pattern respectively and obtain, then calculate 4 time periods during the action imagination (4-5 second successively, 5-6 second, 6-7 second, 7-8 second) under, represents the phase place lock value of 3 mutual cooperation relations in functional areas.Constitute phase place lock value coefficient vector according to lead position and time sequencing, constitute 12 dimensional feature vectors through the splicing back:

PLV in the formula _C3-FCz ¹..., PLV _C3-FCz ⁴Be followed successively by the phase place lock value between the characteristic oscillation pattern of lead under 4 time periods C3 and FCz; PLV _C4-FCz ¹..., PLV _C4-FCz ⁴Be followed successively by the phase place lock value between the characteristic oscillation pattern of lead under 4 time periods C4 and FCz; PLV _C3-C4 ¹..., PLV _C3-C4 ⁴Be followed successively by the phase place lock value between the characteristic oscillation pattern of lead under 4 time periods C3 and C4.

By above-mentioned steps, obtain the Phase synchronization feature of characteristic oscillation pattern after, further investigate the feasibility and the effectiveness of this method.We will (8～14Hz) the Phase synchronization features of obtaining be carried out the identification of 3 classification modes respectively based on the Phase synchronization feature of characteristic oscillation pattern and based on traditional bandpass signal that passes through.The phase place lock value of selecting primary motor area and supplementary motor area feature to lead between the oscillation mode is imported as grader, and employing (Support Vector Machine, SVM) method is as grader based on the support vector base of basic kernel function radially.

Three classification graders of composite lower limb imagination action potential can be expressed as:

y _i＝sgn(W _i·f+b _i)，i＝1，2，3.????????????????(10)

W _iBe weight function, b _iThreshold value obtains by training sample.y _iBe two sorting result labellings (± 1), classification results is decided by the cumulative voting result of support vector machine (SVM) grader of 3 " one to one " two classification modes.The ballot decision rule is as shown in table 1.

Table 1 composite lower limb imagination action potential three classification decision rules

SVM1 (the left hand left side lower limb-right lower limb of the right hand)	SVM2 (left hand left side lower limb-stand)	SVM3 (the right lower limb of the right hand-stand)	Classification results
				??+1	??+1	??-1	Left hand left side lower limb
??+1	??+1	??+1	Left hand left side lower limb
				??-1	??+1	??+1	The right lower limb of the right hand
??-1	??-1	??+1	The right lower limb of the right hand
				??+1	??-1	??-1	Stand
??-1	??-1	??-1	Stand
				??+1	??-1	??+1	??---
??-1	??+1	??-1	??---

Table 2 has provided the classification results contrast of two kinds of method correspondences, and training data is chosen as total sample number purpose 10% to 90%.As can be seen from Table 2, with the Phase synchronization feature of characteristic oscillation pattern as the pattern recognition parameter, its result obviously is better than the Phase synchronization fractional analysis method based on the logical mode of traditional band, the average accuracy of the pattern recognition of the inventive method has reached 81.98%, and the average accuracy of traditional method only has 72.84%

The recognition correct rate contrast of two kinds of methods of table 2

The experimenter	Phase synchronization fractional analysis based on bandpass signal	Phase synchronization fractional analysis based on the characteristic oscillation pattern
			??LXH	??80.78±0.43	??88.93±0.48
??ZPF	??69.85±1.24	??78.58±0.60
			??GCW	??59.86±0.65	??69.84±1.14
??WK	??74.68±1.19	??83.63±1.19
			??JJN	??76.59±1.02	??86.36±1.65
??LWZ	??73.59±1.54	??83.36±0.65
			??ZYH	??73.56±1.26	??81.64±0.69
??XQJ	??74.68±0.35	??87.69±0.80
			??LYW	??62.56±0.68	??73.26±1.35
??NWY	??73.25±1.34	??86.56±1.62
			Average	??72.84	??81.98

 

Claims (4)

1. the phase property extracting method of a composite lower limb imaginary movement EEG comprises the following steps:

1. utilize the collection of brain electric conductance connection electrode to stand, the lower limb co-operating of a left hand left side, the right lower limb co-operating of the right hand 3 class composite lower limb imaginary movement EEG signals;

2. the EEG signals that is positioned at primary motor area and miscellaneous function district that is collected is carried out space filtering, improve the signal to noise ratio of EEG signals;

3. through step after 2. handling, choose and be positioned at the EEG signals of leading in a left side/right primary motor area and supplementary motor area center, carry out empirical modal and decompose, it is decomposed into frequency each natural oscillation mode component from high to low respectively.

4. the natural oscillation mode component rated output spectrum density of the brain electricity that respectively leads that 3. step is obtained according to the frequency distribution scope of its power spectral density, is determined the natural oscillation mode component of the brain electrical feature rhythm and pace of moving things-alhpa rhythm and pace of moving things correspondence, obtains the characteristic oscillation pattern.

5. define the Phase synchronization parameter-phase place lock value of reflection function district cooperation relation, $\mathrm{PLV}=\left|{\&lang;e^{j{\mathrm{\&phi;}}_{\mathrm{xy}}\left(t\right)}\&rang;}_t\right|,$ Wherein＜ _tBe illustrated in certain time period and average, || expression is to signal delivery, φ _Xy(t) two characteristic oscillation pattern x of expression (t) are poor with the instantaneous phase of y (t), when calculating the 3 class composite lower limbs action imagination, and the phase place lock value between a left side/right primary motor area and the supplementary motor area characteristic of correspondence oscillation mode;

6. select 5. to obtain to represent the Phase synchronization feature of functional areas cooperation relation to import step, realize the pattern recognition of 3 class composite lower limbs imagination action as grader based on the support vector base grader of basic kernel function radially.

2. the energy feature extraction method of composite lower limb imaginary movement EEG according to claim 1 is characterized in that, the EEG signals that 3. step is handled comprises C3, C4, FCz lead signals.

3. composite lower limb imaginary movement EEG according to claim 1 and 2 can feature extracting method, it is characterized in that, carry out the characteristic oscillation pattern respectively and obtain being positioned at the EEG signals of leading in a left side/right primary motor area and supplementary motor area center, then calculate the 4-5 second during action is imagined successively, 5-6 second, 6-7 second, under 7-8 second 4 time periods, represent the phase place lock value of 3 mutual cooperation relations in functional areas, constitute phase place lock value coefficient vector according to lead position and time sequencing, constitute 12 dimensional feature vectors through the splicing back:

PLV wherein _C3-FCz ¹..., PLV _C3-FCz ⁴Be followed successively by the phase place lock value between the characteristic oscillation pattern of lead under 4 time periods C3 and FCz; PLV _C4-FCz ¹..., PLV _C4-FCz ⁴Be followed successively by the phase place lock value between the characteristic oscillation pattern of lead under 4 time periods C4 and FCz; PLV _C3-C4 ¹..., PLV _C3-C4 ⁴Be followed successively by the phase place lock value between the characteristic oscillation pattern of lead under 4 time periods C3 and C4.

4. according to the phase property extracting method of the described composite lower limb imaginary movement EEG of claim 1 to 3, it is characterized in that the relevant desynchronization coefficient vector of the incident that 5. step is obtained

As grader input sample, the grader formula is y _i=sgn (W _iF+b _i) i=1,2,3., W _iBe weight function, b _iThreshold value obtains by training sample, y _iBe two sorting result labellings (± 1), classification results is decided by the cumulative voting result of support vector machine (SVM) grader of 3 " one to one " two classification modes.

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