CN107411741A - Multichannel myoelectricity Coupling Characteristics method based on coherence-Non-negative Matrix Factorization - Google Patents
Multichannel myoelectricity Coupling Characteristics method based on coherence-Non-negative Matrix Factorization Download PDFInfo
- Publication number
- CN107411741A CN107411741A CN201710588631.3A CN201710588631A CN107411741A CN 107411741 A CN107411741 A CN 107411741A CN 201710588631 A CN201710588631 A CN 201710588631A CN 107411741 A CN107411741 A CN 107411741A
- Authority
- CN
- China
- Prior art keywords
- mrow
- msub
- matrix
- multichannel
- coherence
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/389—Electromyography [EMG]
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
Abstract
The invention discloses the multichannel myoelectricity Coupling Characteristics method based on coherence-Non-negative Matrix Factorization, traditional coherent analysis and Non-negative Matrix Factorization are combined, synchronous acquisition multichannel electromyographic signal and it is pre-processed first, multichannel electromyographic signal coherence is calculated again, finally by function connects intensity between each frequency range flesh of Non-negative Matrix Factorization acquisition.Function connects intensity between quantitative analysis multichannel myoelectricity coupled characteristic of the present invention and each frequency range flesh, effective Observations Means are provided for further investigation central nervous system motion control mechanism, there is important application value in medical science of recovery therapy field.
Description
Technical field
It is especially a kind of to be based on coherence-nonnegative matrix the present invention relates to nervous system motion control Mechanism Study field
The multichannel myoelectricity Coupling Characteristics method of decomposition.
Background technology
Coupling is interrelated and mutually coordinated effect of the limbs in motion process between different muscle between flesh.Pass through research
The coupled characteristic of each characteristic spectra between multi-channel surface myoelectric signal (surface electromyography, sEMG), can be with
Obtain the functional cohesion between multichannel muscle and central nervous system dominates execution and the coordination mode mechanism of limb motion.In recent years
Come, deployed in succession based on the coupled characteristic between muscle during traditional coherent analysis technique study limb motion.There is scholar
Normalization of the cross-spectral density of two electromyographic signals to signal auto spectral density function is calculated using consistency analysis method, with reflection
Coupled relation of the electromyographic signal in frequency domain.But consistency analysis method can only reflect being concerned with frequency domain between traditional flesh
Property, characteristic information of the electromyographic signal under different time-frequency yardsticks can not be extracted, and function between the muscle of each frequency range can not be reflected
Bonding strength.In addition, during limb motion, polylith muscle acts simultaneously, causes single passage or two passage myoelectricities
Signal can not reflect the functional coupling relationship between muscle during limb motion comprehensively.
The content of the invention
Present invention aims at provide a kind of coupled characteristic that can obtain between multichannel electromyographic signal, can also reflect each frequency
The multichannel myoelectricity Coupling Characteristics method based on coherence-Non-negative Matrix Factorization of function connects intensity between the muscle of section.
To achieve the above object, following technical scheme is employed：The method of the invention is by coherent analysis and non-negative square
Battle array is decomposed and is combined, and is comprised the following steps that：
Step 1, synchronous acquisition multichannel electromyographic signal and it is pre-processed；
Step 2, multichannel electromyographic signal coherence is calculated；
Step 3, function connects intensity between each frequency range flesh is obtained by Non-negative Matrix Factorization.
Further, in step 1, when gathering multichannel electromyographic signal, Delsys companies of the U.S. are utilized
TrignoTMWireless EMG collecting devices, resolution ratio are set to 16bit, sample rate 2000Hz；Before gathering signal, subject is quiet
Seat naturally droops upper arm, and ancon is fixed on support with bandage, ensures that posture is constant in experimentation, adjusting bracket makes forearm
Parallel to the ground, the angle of forearm and upper arm is about 90°, while gather the myoelectricity letter of the polylith muscle under the preceding action of forearm rotation
Number.
Further, in step 1, when being pre-processed to electromyographic signal, adaptive 50Hz notch filters wave filter is utilized
Electromyographic signal is handled, removes Hz noise；From the rank band logical FIR filter of Butterworth three to electromyographic signal at
Reason, makes electromyographic signal be concentrated mainly between 5-200Hz.
Further, in step 3, the particular content of Coupling Characteristics method is as follows between each frequency range flesh：
Multichannel electromyographic signal is subjected to coherent analysis first, then using non-negative matrix factorization method by multichannel flesh
Coherence's result between electric signal is decomposed, and obtains the coherence of each frequency range, and then coupling between quantitative analysis multichannel muscle
Close characteristic；
Coherence property embodies degree of correlation of two time serieses on frequency domain, if X and Y is two groups of time serieses, two letters
Number coherence calculation formula is as follows：
In formula, SXY(f) it is cross-spectral densities of the X and Y on frequency f, SXX(f)、SYY(f) be respectively X and Y auto spectral density；
CXYFor X and Y coherence, its span is 0-1；If CXY(f)=1, illustrate that X and Y are fairly linear related on frequency f；
If CXY(f)=0, illustrate that X and Y are completely independent on frequency f；If CXY(f) value illustrates X and Y in frequency between 0 to 1
Rate f upper parts are linearly related, it is understood that there may be non-linear relation；
The basic thought of non-negative matrix factorization method is：For any given nonnegative matrix Vi×j, Non-negative Matrix Factorization side
Method can find a nonnegative matrix Wi×pWith a nonnegative matrix Hp×jSo that meet
V≈WH (2)
Or
In formula, matrix Vi×jFor connection matrix, matrix Wi×pFor basic matrix, matrix Hp×jFor coefficient matrix；
In Non-negative Matrix Factorization, object function is used for weighing the approximation ratio of decomposition result；
For the rule of the iteration of the object function of Euclidean distance
Object function | | V-WH | |2It is dull, but is not increasing function, and | | V-WH | |2It is matrix to keep constant condition
W and H are fixed；
For the rule of the iteration of the object function of K-L divergences
Object function D (V | | WH) is dull, but is not increasing function, and it is matrix W that D (V | | WH), which keeps constant condition,
Fixed with H；
The value and electromyographic signal channel number i, the length of time series j of signal for cooperateing with number p meet
(i+j) × p ＜ i × j (8)
Non- negative element is only included due to decomposing in front and rear matrix, therefore, original matrix V column vector can be construed to base
The weighted sum of all column vectors in matrix W, and weight coefficient is the element in H in corresponding column vector.
Compared with prior art, the invention has the advantages that：One kind is proposed to grind for multichannel myoelectricity coupled characteristic
The new method studied carefully, quantitatively portray Function Coupling feature of the electromyographic signal on different time-frequency yardsticks, and then quantitative analysis multichannel
Function connects intensity between coupled characteristic and each frequency range muscle between electromyographic signal, it is to carry out coupled characteristic between characteristic spectra flesh
The effective ways of analysis, effective Observations Means are provided for further investigation central nervous system motion control mechanism, while also carried
For a kind of research method of multichannel electromyographic signal.
Brief description of the drawings
Fig. 1 is the flow chart of the inventive method.
Fig. 2 is the electromyographic signal collection location drawing of the inventive method.
Fig. 3 is the front and rear comparison diagram of electromyographic signal pretreatment.
Fig. 4 is multichannel electromyographic signal coherence-Non-negative Matrix Factorization result figure of subject.
Embodiment
The present invention will be further described below in conjunction with the accompanying drawings：
Human body electromyographic signal changes with limb motion state change, and each action has the participation of polylith muscle simultaneously
Wherein.Therefore, the motion control Mechanism Study of the coupled characteristic Central nervous system between multichannel electromyographic signal is analyzed particularly
It is important.Traditional coherent analysis method and Non-negative Matrix Factorization are combined by the present invention, as shown in figure 1, specifically including multichannel flesh
Electric signal synchronous acquisition, electromyographic signal pretreatment, multichannel electromyographic signal coherence calculation, Non-negative Matrix Factorization, each frequency range flesh
Between Coupling Characteristics, Function Coupling evaluating characteristics.Specific method includes electromyographic signal collection part and signal processing：
Electromyographic signal collection, utilize U.S. Delsys company's Ts rignoTMWireless EMG collecting devices, resolution ratio are set to
16bit, sample rate 2000Hz.Before gathering signal, subject is sat quietly and naturally droops upper arm, and ancon is fixed on support with bandage
On, to ensure that posture is constant in experimentation, adjusting bracket makes forearm parallel to the ground, and the angle of forearm and upper arm is about 90 °,
The surface of skin is tested before placement electrode with 75% alcohol wipe, removes skin surface grease and scurf, the rotation of synchronous acquisition forearm
The electromyographic signal of polylith muscle under preceding action, as shown in Fig. 2 specifically including musculus flexor digitorum sublimis (FDS), the ulnar side wrist of subject right arm
Extensor (ECU), extensor muscle of fingers (ED), extensor carpi radialis muscle (ECR), musculus flexor carpi radialis (FCR), musculus palmaris longus (PL), the bicipital muscle of arm (BB)
With brachioradialis (B).
Signal processing includes Coupling Characteristics between pretreatment and each frequency range flesh
Signal Pretreatment：Electromyographic signal is a kind of small-signal, is easily disturbed by noise, and the multichannel collected is former
Beginning electromyographic signal is needed to be pre-processed, and electromyographic signal is handled using adaptive 50Hz notch filters wave filter, is removed
Hz noise；Electromyographic signal is handled from the rank band logical FIR filter of Butterworth three, is concentrated mainly on electromyographic signal
Between 5-200Hz.The front and rear contrast of Signal Pretreatment is as shown in Figure 3.From figure 3, it can be seen that pretreatment has effectively filtered out original flesh
50Hz Hz noises and its frequency multiplication interference in electric signal.
Coupling Characteristics between each frequency range flesh
The present invention carries out coherent analysis to each passage electromyographic signal first, then will using non-negative matrix factorization method
Coherence's result is decomposed between flesh, obtains the coherence of each frequency range, and then coupled characteristic between quantitative analysis muscle.
Coherence can embody degree of correlation of two time serieses on frequency domain, if X and Y is two groups of time serieses, two
Signal coherency calculation formula is as follows：
In formula, SXY(f) it is cross-spectral densities of the X and Y on frequency f, SXX(f)、SYY(f) be respectively X and Y auto spectral density.
CXYFor X and Y coherence, its span is 0-1；If CXY(f)=1, illustrate that X and Y are fairly linear related on frequency f；
If CXY(f)=0, illustrate that X and Y are completely independent on frequency f；If CXY(f) value illustrates X and Y in frequency between 0 to 1
Rate f upper parts are linearly related, it is understood that there may be non-linear relation.
After carrying out coherent analysis, Non-negative Matrix Factorization is carried out to coherence's result, and then analyze each frequency of multichannel
Coupled characteristic between the flesh of section.
NMF basic thought can simply be described as：For any given nonnegative matrix Vi×j, NMF can find one
Individual nonnegative matrix Wi×pWith a nonnegative matrix Hp×jSo that meet
Vi×j≈Wi×pHp×j (2)
Or
In formula, matrix Vi×jFor connection matrix, matrix Wi×pFor basic matrix, matrix Hp×jFor coefficient matrix.
In Non-negative Matrix Factorization, the selection of object function is most important, and be utilized to measurement decomposition result approaches journey
Degree.The selection of Algorithms of Non-Negative Matrix Factorization object function has many, wherein more conventional is to be based on K-L divergences (Kullback-
Leibler divergence) and Euclidean distance (Eulidean distance) object function.
Object function based on Euclidean distance is：
In formula, object function | | V-WH | |2The condition for obtaining minimum value is V=WH, and minimum value is 0.
Object function based on K-L divergences is：
In formula, the condition that object function D (V | | WH) obtains minimum value is V=WH, and minimum value is 0.
In optimization problem, matrix W and H are variables, no matter which kind of object function selected, matrix W and H are not convex
Function, thus it is relatively difficult to try to achieve its optimal solution.Therefore, taking following rule of iteration, arithmetic speed is both can guarantee that, and can is conveniently
Computing.
For the rule of iteration of the object function of Euclidean distance
Object function | | V-WH | |2It is dull, but is not increasing function, and | | V-WH | |2It is matrix to keep constant condition
W and H are fixed.
For the rule of iteration of the object function of K-L divergences
Object function D (V | | WH) is dull, but is not increasing function, and it is matrix W that D (V | | WH), which keeps constant condition,
Fixed with H.
Collaboration number p value typically carries out strict selection with the method being adapted to, and with electromyographic signal channel number i,
The length of time series j of signal meets
(i+j) × p ＜ i × j (10)
Non- negative element is only included due to decomposing in front and rear matrix, therefore, original matrix V column vector can be construed to base
The weighted sum of all column vectors in matrix W, and weight coefficient is the element in H in corresponding column vector.It is this to be based on base vector
The representation of combination has very intuitively semantic interpretation, and it reflects in human thinking the concept of " local form overall ".
To verify a kind of multichannel myoelectricity coupled characteristic point based on coherence-Non-negative Matrix Factorization of the present invention
Analysis method, gathers the upper limbs sEMG of 6 Healthy subjects (age be (25 ± 3) year), and subject relevant information is as shown in table 1.
Table 1 is tested relevant information
It is required that it is good without muscular fatigue phenomenon, the state of mind before subject experiment, and it is familiar with experiment flow.According to institute of the present invention
The multichannel electromyographic signal collection and processing procedure stated, gather the upper limbs surface electromyogram signal of Healthy subjects, and analyze multichannel
The coupled characteristic of myoelectricity, and then study the motion control mechanism of central nervous system.
Fig. 4 is subject multichannel electromyographic signal coherence-(different colours represent different couplings to Non-negative Matrix Factorization result figure
Close intensity).In Fig. 4, left hand view abscissa is electromyographic signal frequency, ordinate is signal spectrum, and right part of flg is horizontal, ordinate is quilt
Try 8 pieces of muscle of upper limbs；Can intuitively it be found out by Fig. 4 (a)~(d) figures, the sEMG signals for being tested upper limbs are broken down into 4 frequency ranges
It is interior, and the stiffness of coupling size between different muscle is represented by the difference of grid color, embody the difference of stiffness of coupling between flesh.
Therefore, multichannel electromyographic signal can be obtained between flesh corresponding to different frequency range electromyographic signal by coherence-Non-negative Matrix Factorization
Coupled characteristic intensity, effective observed data is provided for further investigation central nervous system motion control mechanism.
Embodiment described above is only that the preferred embodiment of the present invention is described, not to the model of the present invention
Enclose and be defined, on the premise of design spirit of the present invention is not departed from, technical side of the those of ordinary skill in the art to the present invention
The various modifications and improvement that case is made, it all should fall into the protection domain of claims of the present invention determination.
Claims (4)
1. one kind is based on the multichannel myoelectricity Coupling Characteristics method of coherence-Non-negative Matrix Factorization, it is characterised in that institute
State method to be combined coherent analysis and Non-negative Matrix Factorization, comprise the following steps that：
Step 1, synchronous acquisition multichannel electromyographic signal and it is pre-processed；
Step 2, multichannel electromyographic signal coherence is calculated；
Step 3, function connects intensity between each frequency range flesh is obtained by Non-negative Matrix Factorization.
2. the multichannel myoelectricity Coupling Characteristics side according to claim 1 based on coherence-Non-negative Matrix Factorization
Method, it is characterised in that：In step 1, when gathering multichannel electromyographic signal, U.S. Delsys company's Ts rigno is utilizedTMWireless
EMG collecting devices, resolution ratio are set to 16bit, sample rate 2000Hz；Before gathering signal, subject is sat quietly and naturally droops upper arm,
Ancon is fixed on support with bandage, ensure experimentation in posture it is constant, adjusting bracket makes forearm parallel to the ground, forearm with
The angle of upper arm is about 90 °, while gathers the electromyographic signal of the polylith muscle under the preceding action of forearm rotation.
3. the multichannel myoelectricity Coupling Characteristics side according to claim 1 based on coherence-Non-negative Matrix Factorization
Method, it is characterised in that：In step 1, when being pre-processed to electromyographic signal, using adaptive 50Hz notch filters wave filter to flesh
Electric signal is handled, and removes Hz noise；Electromyographic signal is handled from the rank band logical FIR filter of Butterworth three,
Electromyographic signal is set to be concentrated mainly between 5-200Hz.
4. the multichannel myoelectricity Coupling Characteristics side according to claim 1 based on coherence-Non-negative Matrix Factorization
Method, it is characterised in that in step 3, the particular content of Coupling Characteristics method is as follows between each frequency range flesh：
Multichannel electromyographic signal is subjected to coherent analysis first, then believed multichannel myoelectricity using non-negative matrix factorization method
Coherence's result between number is decomposed, and obtains the coherence of each frequency range, and then is coupled between quantitative analysis multichannel muscle special
Property；
Coherence property embodies degree of correlation of two time serieses on frequency domain, if X and Y is two groups of time serieses, two signal phases
Dryness calculation formula is as follows：
<mrow>
<msubsup>
<mi>C</mi>
<mrow>
<mi>X</mi>
<mi>Y</mi>
</mrow>
<mn>2</mn>
</msubsup>
<mrow>
<mo>(</mo>
<mi>f</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfrac>
<mrow>
<mo>|</mo>
<msub>
<mi>S</mi>
<mrow>
<mi>X</mi>
<mi>Y</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>f</mi>
<mo>)</mo>
</mrow>
<msup>
<mo>|</mo>
<mn>2</mn>
</msup>
</mrow>
<mrow>
<msub>
<mi>S</mi>
<mrow>
<mi>X</mi>
<mi>X</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>f</mi>
<mo>)</mo>
</mrow>
<mo>*</mo>
<msub>
<mi>S</mi>
<mrow>
<mi>Y</mi>
<mi>Y</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>f</mi>
<mo>)</mo>
</mrow>
</mrow>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula, SXY(f) it is cross-spectral densities of the X and Y on frequency f, SXX(f)、SYY(f) be respectively X and Y auto spectral density；CXYFor
X and Y coherence, its span are 0-1；If CXY(f)=1, illustrate that X and Y are fairly linear related on frequency f；If
CXY(f)=0, illustrate that X and Y are completely independent on frequency f；If CXY(f) value illustrates X and Y on frequency f between 0 to 1
Partial linear is related, it is understood that there may be non-linear relation；
The basic thought of non-negative matrix factorization method is：For any given nonnegative matrix Vi×j, non-negative matrix factorization method energy
Enough find a nonnegative matrix Wi×pWith a nonnegative matrix Hp×jSo that meet
V≈WH (2)
Or
<mrow>
<msub>
<mi>V</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>&ap;</mo>
<msub>
<mrow>
<mo>(</mo>
<mi>W</mi>
<mi>H</mi>
<mo>)</mo>
</mrow>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>r</mi>
</munderover>
<msub>
<mi>W</mi>
<mrow>
<mi>i</mi>
<mi>k</mi>
</mrow>
</msub>
<msub>
<mi>H</mi>
<mrow>
<mi>k</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>3</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula, matrix Vi×jFor connection matrix, matrix Wi×pFor basic matrix, matrix Hp×jFor coefficient matrix；
In Non-negative Matrix Factorization, object function is used for weighing the approximation ratio of decomposition result；
For the rule of the iteration of the object function of Euclidean distance
<mrow>
<msub>
<mi>W</mi>
<mrow>
<mi>i</mi>
<mi>p</mi>
</mrow>
</msub>
<mo>&LeftArrow;</mo>
<msub>
<mi>W</mi>
<mrow>
<mi>i</mi>
<mi>p</mi>
</mrow>
</msub>
<mfrac>
<msub>
<mrow>
<mo>(</mo>
<msup>
<mi>VH</mi>
<mi>T</mi>
</msup>
<mo>)</mo>
</mrow>
<mrow>
<mi>i</mi>
<mi>p</mi>
</mrow>
</msub>
<msub>
<mrow>
<mo>(</mo>
<msup>
<mi>WHH</mi>
<mi>T</mi>
</msup>
<mo>)</mo>
</mrow>
<mrow>
<mi>i</mi>
<mi>p</mi>
</mrow>
</msub>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>4</mn>
<mo>)</mo>
</mrow>
</mrow>
1
<mrow>
<msub>
<mi>H</mi>
<mrow>
<mi>p</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>&LeftArrow;</mo>
<msub>
<mi>H</mi>
<mrow>
<mi>p</mi>
<mi>j</mi>
</mrow>
</msub>
<mfrac>
<msub>
<mrow>
<mo>(</mo>
<msup>
<mi>W</mi>
<mi>T</mi>
</msup>
<mi>V</mi>
<mo>)</mo>
</mrow>
<mrow>
<mi>p</mi>
<mi>j</mi>
</mrow>
</msub>
<msub>
<mrow>
<mo>(</mo>
<msup>
<mi>W</mi>
<mi>T</mi>
</msup>
<mi>W</mi>
<mi>H</mi>
<mo>)</mo>
</mrow>
<mrow>
<mi>p</mi>
<mi>j</mi>
</mrow>
</msub>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>5</mn>
<mo>)</mo>
</mrow>
</mrow>
Object function | | V-WH | |2It is dull, but is not increasing function, and | | V-WH | |2It is matrix W and H to keep constant condition
It is fixed；
For the rule of the iteration of the object function of K-L divergences
<mrow>
<msub>
<mi>W</mi>
<mrow>
<mi>i</mi>
<mi>p</mi>
</mrow>
</msub>
<mo>&LeftArrow;</mo>
<msub>
<mi>W</mi>
<mrow>
<mi>i</mi>
<mi>p</mi>
</mrow>
</msub>
<mfrac>
<mrow>
<munder>
<mo>&Sigma;</mo>
<mi>j</mi>
</munder>
<msub>
<mi>H</mi>
<mrow>
<mi>p</mi>
<mi>j</mi>
</mrow>
</msub>
<msub>
<mi>V</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>/</mo>
<msub>
<mrow>
<mo>(</mo>
<mi>W</mi>
<mi>H</mi>
<mo>)</mo>
</mrow>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
</mrow>
<mrow>
<munder>
<mo>&Sigma;</mo>
<mi>j</mi>
</munder>
<msub>
<mi>H</mi>
<mrow>
<mi>p</mi>
<mi>j</mi>
</mrow>
</msub>
</mrow>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>6</mn>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msub>
<mi>H</mi>
<mrow>
<mi>p</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>&LeftArrow;</mo>
<msub>
<mi>H</mi>
<mrow>
<mi>p</mi>
<mi>j</mi>
</mrow>
</msub>
<mfrac>
<mrow>
<munder>
<mo>&Sigma;</mo>
<mi>i</mi>
</munder>
<msub>
<mi>W</mi>
<mrow>
<mi>i</mi>
<mi>p</mi>
</mrow>
</msub>
<msub>
<mi>V</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>/</mo>
<msub>
<mrow>
<mo>(</mo>
<mi>W</mi>
<mi>H</mi>
<mo>)</mo>
</mrow>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
</mrow>
<mrow>
<munder>
<mo>&Sigma;</mo>
<mi>i</mi>
</munder>
<msub>
<mi>W</mi>
<mrow>
<mi>i</mi>
<mi>p</mi>
</mrow>
</msub>
</mrow>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>7</mn>
<mo>)</mo>
</mrow>
</mrow>
Object function D (V | | WH) is dull, but is not increasing function, and it is matrix W and H that D (V | | WH), which keeps constant condition,
It is fixed；
The value and electromyographic signal channel number i, the length of time series j of signal for cooperateing with number p meet
(i+j) × p ＜ i × j (8)
Non- negative element is only included due to decomposing in front and rear matrix, therefore, original matrix V column vector can be construed to basic matrix
The weighted sum of all column vectors in W, and weight coefficient is the element in H in corresponding column vector.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710588631.3A CN107411741A (en) | 2017-07-19 | 2017-07-19 | Multichannel myoelectricity Coupling Characteristics method based on coherence-Non-negative Matrix Factorization |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710588631.3A CN107411741A (en) | 2017-07-19 | 2017-07-19 | Multichannel myoelectricity Coupling Characteristics method based on coherence-Non-negative Matrix Factorization |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107411741A true CN107411741A (en) | 2017-12-01 |
Family
ID=60430023
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710588631.3A Pending CN107411741A (en) | 2017-07-19 | 2017-07-19 | Multichannel myoelectricity Coupling Characteristics method based on coherence-Non-negative Matrix Factorization |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107411741A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109620651A (en) * | 2018-11-16 | 2019-04-16 | 中国科学技术大学 | Intelligent recovering aid equipment based on synchronous brain myoelectricity |
| CN109674445A (en) * | 2018-11-06 | 2019-04-26 | 杭州电子科技大学 | Coupling analytical method between a kind of combination Non-negative Matrix Factorization and the flesh of complex network |
| CN109758144A (en) * | 2018-12-13 | 2019-05-17 | 新绎健康科技有限公司 | A method of brain function variation tendency is determined based on EEG signals |
| CN109805929A (en) * | 2019-02-14 | 2019-05-28 | 燕山大学 | A kind of Coupling Characteristics method between the flesh based on WAVELET PACKET DECOMPOSITION and n:m consistency analysis |
| CN112932508A (en) * | 2021-01-29 | 2021-06-11 | 电子科技大学 | Finger activity recognition system based on arm electromyography network |
| CN115474945A (en) * | 2022-09-15 | 2022-12-16 | 燕山大学 | Multi-element global synchronization index method for multi-channel electroencephalogram and electromyographic coupling analysis |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030130709A1 (en) * | 2001-06-26 | 2003-07-10 | D.C. Constance Haber | Therapeutic methods using electromagnetic radiation |
| US20090062680A1 (en) * | 2007-09-04 | 2009-03-05 | Brain Train | Artifact detection and correction system for electroencephalograph neurofeedback training methodology |
| CN105457164A (en) * | 2016-01-06 | 2016-04-06 | 电子科技大学 | Multichannel functional electrical stimulation method and multichannel functional electrical stimulation system in muscle synergy mode |
| CN106295690A (en) * | 2016-08-03 | 2017-01-04 | 哈尔滨工业大学深圳研究生院 | Time series data clustering method based on Non-negative Matrix Factorization and system |
| CN106725509A (en) * | 2016-12-15 | 2017-05-31 | 佛山科学技术学院 | Motor function comprehensive estimation method based on patients with cerebral apoplexy |
-
2017
- 2017-07-19 CN CN201710588631.3A patent/CN107411741A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030130709A1 (en) * | 2001-06-26 | 2003-07-10 | D.C. Constance Haber | Therapeutic methods using electromagnetic radiation |
| US20090062680A1 (en) * | 2007-09-04 | 2009-03-05 | Brain Train | Artifact detection and correction system for electroencephalograph neurofeedback training methodology |
| CN105457164A (en) * | 2016-01-06 | 2016-04-06 | 电子科技大学 | Multichannel functional electrical stimulation method and multichannel functional electrical stimulation system in muscle synergy mode |
| CN106295690A (en) * | 2016-08-03 | 2017-01-04 | 哈尔滨工业大学深圳研究生院 | Time series data clustering method based on Non-negative Matrix Factorization and system |
| CN106725509A (en) * | 2016-12-15 | 2017-05-31 | 佛山科学技术学院 | Motor function comprehensive estimation method based on patients with cerebral apoplexy |
Non-Patent Citations (3)
| Title |
|---|
| 李飞: "基于表面肌电信号的小儿脑瘫步态肌肉协同分析", 《中国优秀硕士学位论文全文数据库医药卫生科技辑》 * |
| 杜义浩等: "基于变分模态分解-相干分析的肌间耦合特性", 《物理学报》 * |
| 谢平等: "基于表面肌电非负矩阵分解与一致性的肌间协同-耦合关系研究", 《中国生物医学工程学报》 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109674445A (en) * | 2018-11-06 | 2019-04-26 | 杭州电子科技大学 | Coupling analytical method between a kind of combination Non-negative Matrix Factorization and the flesh of complex network |
| CN109674445B (en) * | 2018-11-06 | 2021-10-08 | 杭州电子科技大学 | Inter-muscle coupling analysis method combining non-negative matrix factorization and complex network |
| CN109620651A (en) * | 2018-11-16 | 2019-04-16 | 中国科学技术大学 | Intelligent recovering aid equipment based on synchronous brain myoelectricity |
| CN109620651B (en) * | 2018-11-16 | 2020-03-31 | 中国科学技术大学 | Intelligent auxiliary rehabilitation equipment based on synchronous brain and muscle electricity |
| CN109758144A (en) * | 2018-12-13 | 2019-05-17 | 新绎健康科技有限公司 | A method of brain function variation tendency is determined based on EEG signals |
| CN109805929A (en) * | 2019-02-14 | 2019-05-28 | 燕山大学 | A kind of Coupling Characteristics method between the flesh based on WAVELET PACKET DECOMPOSITION and n:m consistency analysis |
| CN112932508A (en) * | 2021-01-29 | 2021-06-11 | 电子科技大学 | Finger activity recognition system based on arm electromyography network |
| CN115474945A (en) * | 2022-09-15 | 2022-12-16 | 燕山大学 | Multi-element global synchronization index method for multi-channel electroencephalogram and electromyographic coupling analysis |
| CN115474945B (en) * | 2022-09-15 | 2024-04-12 | 燕山大学 | Multi-channel brain myoelectricity coupling analysis-oriented multi-element global synchronization index method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107411741A (en) | Multichannel myoelectricity Coupling Characteristics method based on coherence-Non-negative Matrix Factorization | |
| CN106709469B (en) | Automatic sleep staging method based on electroencephalogram and myoelectricity multiple characteristics | |
| CN102426651B (en) | Human body forearm surface electromyogram signal acquisition and pattern recognition system | |
| Cifrek et al. | Surface EMG based muscle fatigue evaluation in biomechanics | |
| CN102697493B (en) | Method for rapidly and automatically identifying and removing ocular artifacts in electroencephalogram signal | |
| Karlsson et al. | Time-frequency analysis of myoelectric signals during dynamic contractions: a comparative study | |
| CN102697495B (en) | Second-generation wavelet electromyographic signal noise eliminating method based on ensemble empirical mode decomposition | |
| CN107260166A (en) | A kind of electric artefact elimination method of practical online brain | |
| Guo et al. | Single channel blind source separation based local mean decomposition for biomedical applications | |
| CN106096579A (en) | A kind of method of ECG signal processing | |
| CN103610461A (en) | EEG noise elimination method based on dual-density wavelet neighborhood related threshold processing | |
| CN102178524B (en) | Synchronization likehood-based electroencephalograph and electromyography synergistic analyzing method | |
| CN107822629B (en) | Method for detecting myoelectric axes on surfaces of limbs | |
| CN108742613A (en) | Orient coupling analytical method between the flesh of coherence partially based on transfer entropy and broad sense | |
| CN105342605A (en) | Method for removing myoelectricity artifacts from brain electrical signals | |
| CN105286860A (en) | Motor imagery brain electrical signal recognition method based on dual-tree complex wavelet energy difference | |
| Al-kadi et al. | Compatibility of mother wavelet functions with the electroencephalographic signal | |
| CN112998704A (en) | Wearable device blood oxygen saturation calculation method | |
| CN107320097B (en) | Method and device for extracting muscle fatigue features by using electromyographic signal marginal spectrum entropy | |
| Koppolu et al. | Automatic selection of IMFs to denoise the sEMG signals using EMD | |
| Tan et al. | Study on wavelet transform in the processing for ECG signals | |
| Ai et al. | Gestures recognition based on wavelet and LLE | |
| CN109805929A (en) | A kind of Coupling Characteristics method between the flesh based on WAVELET PACKET DECOMPOSITION and n:m consistency analysis | |
| Li et al. | sEMG signal filtering study using synchrosqueezing wavelet transform with differential evolution optimized threshold | |
| Altamirano | EMG pattern prediction for upper limb movements based on wavelet and Hilbert-Huang transform |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20171201 |