CN113741691A - System and method for synchronizing brain and muscle electrical information captured accurately by movement intention - Google Patents
System and method for synchronizing brain and muscle electrical information captured accurately by movement intention Download PDFInfo
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Abstract
The invention discloses a brain-muscle-electricity information synchronization system and method for accurately capturing movement intention, which comprises a steady-state force acquisition module for acquiring the lower limbs of a subject to determine a target force output interval, a brain-muscle-electricity acquisition module for synchronously acquiring brain signals and muscle-electricity signals of a user and having a signal wireless transmission function, a signal preprocessing module for performing artifact filtering on the brain signals and the muscle-electricity signals to improve the signal to noise ratio of the brain-muscle-electricity signals, and a brain-muscle-electricity information synchronization module for adopting a multi-element model to simulate brain-muscle-electricity data and performing brain-muscle-electricity coherence analysis and brain-muscle-electricity time delay quantization. The method utilizes multivariate models to simulate the brain-muscle-electricity data, and carries out time delay quantization on the brain-muscle-electricity movement intention information based on the brain-muscle-electricity coherence analysis, thereby realizing the movement intention information synchronization of the brain-muscle-electricity signal by removing time delay, laying a firm theoretical foundation for the brain-muscle-electricity fusion, and improving the effectiveness of the method for recognizing the human body movement intention by the brain-muscle-electricity fusion and the robustness of an intention recognition system.
Description
Technical Field
The invention belongs to the field of bioelectricity fusion, and relates to a system and a method for synchronizing brain and muscle electrical information captured accurately by movement intention.
Background
The existing perception technology of human lower limb movement intention is mainly divided into three categories according to different signal source types: a recognition method based on mechanical signals, a recognition method based on bioelectrical signals and a recognition method based on mixed signal sources. The identification method based on the mixed signal source can better solve the problems of low robustness and accuracy and the like in the identification process of a single signal source due to the introduction of various signal sources for identification. In various signal sources, mechanical signals have obvious lag and cannot truly reflect the human motion intention; the electroencephalogram signal is directly reflected by the brain nerve center on the scalp, and has the characteristics of weakness, aliasing, low signal-to-noise ratio, predictability and the like; the electromyographic signals are closely related to motion information and have the characteristics of high signal-to-noise ratio, high spatial resolution, unobvious predictability and the like. As the characteristics of the brain and muscle electrical signals have obvious complementation, the brain and muscle electrical fusion is adopted to realize the perception of the human motion intention, thereby not only making up the defect of single use of brain and muscle electrical signal identification, but also fusing the characteristics of the brain and muscle electrical signals to improve the identification accuracy and realizing high accuracy and quick response of the perception of the human lower limb motion intention.
However, most of the current researches on mixed BCI (brain-muscle and muscle fusion) around brain-muscle and muscle fusion are developed aiming at brain-muscle and muscle fusion levels, and the quality of human movement intentions is identified by brain-muscle and muscle fusion by emphasizing the research on characteristic levels or decision levels. However, in the process of generating human movement intentions, due to the inconsistency of conduction paths, the movement intention information contained in the brain and muscle electricity is asynchronous, the movement intention in the brain and muscle electricity is always in advance of the muscle electricity, and the time delay causes the theoretical defect of fusion identification by directly utilizing the brain and muscle electricity acquired synchronously, and the accurate capture of the human movement intention cannot be realized. The asynchronization of the information of the brain and the muscle can reduce the effectiveness of a method for identifying the human motion intention based on the fusion of the brain and the muscle and the reliability of a system, greatly limit the development of the technology for identifying the fusion of the brain and the muscle, and urgently wait for the breakthrough of the method for synchronizing the information of the brain and the muscle to improve the synchronism of the brain and the muscle. At present, a large number of researches prove that a strong electroencephalogram and electromyography coherence phenomenon exists in steady-state force contraction or equidistant contraction of muscles, and the electroencephalogram and electromyography synchronism can be realized by quantitatively evaluating the correlation of the electroencephalogram and electromyography by using synchronously acquired electroencephalogram and electromyography data.
Disclosure of Invention
The invention aims to solve the problems in the prior art and provides a system and a method for synchronizing brain and muscle electrical information with accurately captured movement intention.
In order to achieve the purpose, the invention adopts the following technical scheme to realize the purpose:
a method for synchronizing brain and muscle electrical information captured accurately by motor intentions comprises the following steps:
acquiring a plantar MVC of a subject, calculating a plantar target force according to the plantar MVC, and confirming a target lower limb pressure output interval according to the plantar MVC;
collecting electroencephalogram signals and electromyogram signals of a subject;
preprocessing the acquired electroencephalogram signals and the acquired electromyogram signals to obtain preprocessed electroencephalogram and electromyogram data;
and carrying out coherence analysis on the brain and muscle electricity data to obtain the time delay of the movement intention information between the brain and muscle electricity of the testee.
The invention is further improved in that:
the method for acquiring the MVC of the vola of the subject comprises the following steps: collecting for a plurality of times, and obtaining the MVC of the lower limb soles of the left leg and the right leg by taking an average value;
the target lower limb pressure output interval is 0.8MVC +/-10N.
The specific method for preprocessing the received electroencephalogram signal and the received electromyogram signal comprises the following steps:
performing baseline calibration on the electroencephalogram signals, removing power frequency interference by adopting self-adaptive filtering, and removing ocular artifacts by using an independent component analysis algorithm; and performing band-pass filtering on the electromyographic signals, and performing self-adaptive filtering to remove power frequency interference so as to improve the signal-to-noise ratio of the electroencephalogram and the electromyographic signals.
The specific method for analyzing the coherence of the brain and muscle electricity comprises the following steps:
reconstructing the brain-muscle electrical data into high-dimensional data, and fitting the brain-muscle electrical data by adopting a multivariate model;
determining the order of a multivariate model fitting the brain and muscle electrical data;
after the order of the model is determined, estimating a model coefficient fitting the electroencephalogram and electromyogram data;
and performing electroencephalogram and electromyogram coherence analysis by using the model with the determined parameters, determining an electroencephalogram and electromyogram coherence frequency band, and performing electroencephalogram and electromyogram time delay quantization on the result.
The method for reconstructing the brain and muscle electrical data into high-dimensional data and adopting a multivariate model to simulate the brain and muscle electrical data comprises the following specific steps:
wherein, YtThe reconstructed high-dimensional brain and muscle electrical data; t is a brain myoelectricity data sampling point, and T is 1,2, … T; k is the sum of the number of the brain and muscle electricity data channels; m is the total number of trials of the subject; p is the model order; a. thet-iIs a K x K dimensional model coefficient; etThe post-fitting residual noise with covariance ∑.
The specific calculation method for determining the order of the multivariate model of the brain and muscle electrical data comprises the following steps:
wherein p is the model order; m is the number of experimental trials; k is the sum of the number of the brain and muscle electricity data channels, N is ML, and L is the length of the time window.
The specific method for estimating the fitting brain-muscle electrical data model coefficient comprises the following steps:
determining a model coefficient matrix Θ:
Θ＝(A1,A2...AP) (6)
wherein p is the model order, and the calculation method of A is shown as formula (3);
determining a model data matrix Wn：
Wn＝(Yn-1,Yn-2...Yn-p) (7)
Wherein, Yn-iThe reconstructed brain and muscle electrical data shown in the formula (2) is shown in the specification, and n is a current sampling point;
wherein Z isnFor the instantaneous error estimation matrix, giving the desired output YnError from the expected output; n is the current sampling point; enTo accumulate errors, c is a forgetting factor, 0<c<1, ensuring that the brain and muscle electrical data far away from the current moment are forgotten;
recursive algorithm iteratively calculates Θ | min (E)n) Determining a final model coefficient matrix theta, and calculating a noise covariance matrix sigma according to the model instantaneous error estimation matrix, as shown in the following formula:
wherein c is a forgetting factor; n is the current sampling point; m is the number of experimental trials;
the calculation method of the power spectrum matrix comprises the following steps:
wherein f is the current electroencephalogram and electromyography coherent frequency; f. ofsThe brain and muscle electrical sampling frequency; a is the coefficient of the mvAR model; sigma is fitting noise covariance, and an iterative calculation method is shown as a formula (10);
according to the formula (11), the coherence of each channel of the brain and muscle electricity can be calculated as follows:
wherein f is the current electroencephalogram and electromyography coherent frequency; i, j are corresponding brain-muscle electrical channels;
determining the central frequency f of the occurrence of significant coherence of the brain and muscle electricity after model fittingκAnd time t0And outputting the result to a brain-muscle electric time delay quantization unit.
The specific method for performing time delay quantization comprises the following steps:
and (3) performing artificial translation on the delayed data of the central frequency and the brain and muscle electricity at the moment of the appearance of the subject, wherein the artificial translation is expressed by the formula (13):
δ＝max(Cohij(fκ)) (13)
wherein δ is the finally quantized brain-muscle electrical time delay; cohijCoherence of the i, j channel of the brain myoelectricity; f. ofκA center frequency that is typical of coherence of the brain and muscle;
after a plurality of tests are carried out, the average value is obtained, the movement intention information time delay between the corresponding myoelectric channel of the testee and the brain electricity is obtained, and the brain myoelectric movement intention information synchronization is completed.
A brain-muscle-electricity information synchronization system capable of accurately capturing movement intentions comprises a steady-state force acquisition module, a brain-muscle-electricity acquisition module, a signal preprocessing module and a brain-muscle-electricity information synchronization module;
the steady-state force acquisition module is used for acquiring the MVC of the sole of the subject, calculating the target force of the sole according to the MVC of the sole and confirming the target lower limb pressure output interval according to the MVC of the sole;
the brain-myoelectricity acquisition module is used for acquiring electroencephalogram signals and myoelectricity signals of a subject;
the signal preprocessing module is used for preprocessing the acquired electroencephalogram signals and the acquired electromyogram signals to obtain preprocessed electroencephalogram and electromyogram data;
the brain-muscle and electric-power information synchronization module is used for performing coherence analysis on the brain-muscle and electric-power data to obtain the time delay of movement intention information between the brain and the electric power of the testee.
Furthermore, the brain-muscle-electricity information synchronization module comprises a brain-muscle-electricity coherence analysis unit and a brain-muscle-electricity time delay quantization unit;
the electroencephalogram and electromyogram coherence analysis unit determines the order of the fitting model and estimates the coefficient of the fitting model, the electroencephalogram and electromyogram coherence analysis is carried out by utilizing the model with determined parameters, and after the electroencephalogram and electromyogram coherence frequency band and time are determined, the result is output to the electroencephalogram and electromyogram delay quantization unit for electroencephalogram and electromyogram delay quantization;
the electroencephalogram and electromyogram time delay quantization unit is used for carrying out time delay quantization on the electroencephalogram and electromyogram data, carrying out artificial translation on the hysteresis electromyogram data, testing for a plurality of times and then averaging to obtain intention information time delay quantities of a brain area and a muscle corresponding to an electroencephalogram and electromyogram channel of a subject, and the electroencephalogram and electromyogram data after time delay offset are used for capturing human body movement intention.
Compared with the prior art, the invention has the following beneficial effects:
the invention discloses a brain-muscle-electricity information synchronization system and method capable of accurately capturing movement intention, which can effectively determine the central frequency and the time of brain and muscle information exchange when the steady force of lower limbs is output by adopting a brain-muscle-electricity coherence analysis method based on multivariate model fitting, estimate the time delay between brain-muscle-electricity signals by using the central frequency and the time, simultaneously cover the coupling between brain-muscle-electricity channels by the fitting of the multivariate model, consider cognition as a whole and better accord with the physiological basis of the human body cognition process. The time delay estimation of the brain-muscle-electricity movement intention is realized based on the brain-muscle-electricity coherence analysis and the multivariate model fitting, the brain-muscle-electricity after the time delay offset has the capability of capturing the movement intention of the human body more accurately, although the time delay is not large, the influence caused by the time delay is increased under the muscle or mental fatigue state, the performance improvement caused by eliminating the time difference between the brain-muscle-electricity and the human body is amplified, the brain-muscle-electricity after the information synchronization contains the same movement intention, the method can be used for realizing the accurate capturing of the movement intention of the human body, the effectiveness of the method for recognizing the movement intention by brain-muscle-electricity fusion is powerfully established, the robustness and the reliability of the brain-muscle-electricity fusion recognition system are improved, the logic of the method for recognizing the movement intention by brain-muscle-electricity fusion is also improved, and the popularization and the development of the living-computer point integration technology are promoted.
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In order to more clearly explain the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention, and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a schematic diagram of the system of the present invention;
FIG. 2 is a flow chart of the method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
In the description of the embodiments of the present invention, it should be noted that if the terms "upper", "lower", "horizontal", "inner", etc. are used for indicating the orientation or positional relationship based on the orientation or positional relationship shown in the drawings or the orientation or positional relationship which is usually arranged when the product of the present invention is used, the description is merely for convenience and simplicity, and the indication or suggestion that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and thus, cannot be understood as limiting the present invention. Furthermore, the terms "first," "second," and the like are used merely to distinguish one description from another, and are not to be construed as indicating or implying relative importance.
Furthermore, the term "horizontal", if present, does not mean that the component is required to be absolutely horizontal, but may be slightly inclined. For example, "horizontal" merely means that the direction is more horizontal than "vertical" and does not mean that the structure must be perfectly horizontal, but may be slightly inclined.
In the description of the embodiments of the present invention, it should be further noted that unless otherwise explicitly stated or limited, the terms "disposed," "mounted," "connected," and "connected" should be interpreted broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
The invention is described in further detail below with reference to the accompanying drawings:
referring to fig. 1-2, the embodiment of the invention discloses a brain-muscle-electricity information synchronization system for accurately capturing movement intention, which comprises 5 modules of steady-state force acquisition, brain-muscle-electricity acquisition, signal preprocessing, brain-muscle-electricity information synchronization and the like, utilizes a multivariate model to fit acquired brain-muscle-electricity data, and performs time delay quantization on brain-muscle-electricity movement intention information based on brain-muscle-electricity coherence analysis, so that movement intention information synchronization of brain-muscle-electricity signals is realized by removing time delay.
The steady-state force acquisition module is used for acquiring the plantar pressure of a user, evaluating whether the user is in a steady-state force output state or not, outputting the result to the brain-myoelectricity acquisition module, and intercepting a brain-myoelectricity signal when the lower limb is in steady-state force output;
the brain-myoelectricity acquisition module is used for synchronously acquiring electroencephalogram signals and myoelectricity signals of a user and wirelessly transmitting the electroencephalogram signals and the myoelectricity signals to the signal preprocessing module;
the signal preprocessing module is used for carrying out artifact filtering on the electroencephalogram signal and the electromyogram signal, obtaining the electroencephalogram signal and the electromyogram signal with high signal-to-noise ratio, and then synchronizing the electroencephalogram information and the electromyogram information;
and the brain-muscle-electricity information synchronization module is used for simulating brain-muscle-electricity data by adopting a multivariate model, performing brain-muscle-electricity coherence analysis, quantizing the time delay of the brain-muscle-electricity, and outputting the result as the estimated time delay of the movement intention information between the brain-muscle electricity and the muscle electricity.
The steady-state force acquisition module comprises a pressure acquisition unit and a steady-state force judgment unit, wherein the pressure acquisition unit is used for acquiring the MVC of the sole of a testee, and the average value of the multiple acquisition is the MVC of the sole of the testee; the steady state force judging unit is used for confirming a target lower limb pressure output interval according to the MVC (vola model view controller), determining the time when the lower limb output pressure of the subject meets the target output interval, sending the time to the brain-myoelectricity acquisition module, and extracting the brain-myoelectricity data synchronously acquired in the target output interval;
the brain-myoelectricity acquisition module comprises an electroencephalogram acquisition unit and a myoelectricity acquisition unit; the electroencephalogram acquisition unit comprises an electroencephalogram acquisition subunit and a wireless transmission subunit, and the electroencephalogram acquisition subunit is connected with the electroencephalogram cap; the myoelectricity acquisition unit comprises a myoelectricity acquisition subunit and a wireless transmission subunit, and the myoelectricity acquisition subunit is connected with the myoelectricity electrode; the electroencephalogram acquisition subunit and the myoelectricity acquisition subunit are in data interaction with the signal preprocessing module through the wireless transmission subunit;
the FCz, FC1, FC2, C1, C2, C3, C4, CP1, CP2 and Cz channels of the motor cortex region were collected with a brain cap at the locations defined by the international 10-20 standard electrode placement. The myoelectric electrodes are arranged on the lower limb rectus femoris, the lateral femoris, the medial femoris, the tibialis anterior muscle, the lateral gastrocnemius and the medial gastrocnemius muscle of a user and are used for acquiring myoelectric signals of the corresponding muscles;
preprocessing the acquired brain and muscle electrical data in a signal preprocessing module, wherein the signal preprocessing module comprises an artifact removing unit and a noise removing unit and is used for filtering 50Hz power frequency interference and eye electrical artifacts in brain electricity on brain electricity and muscle electricity, and finally obtaining the brain and muscle electrical data with higher signal-to-noise ratio;
the brain-muscle-electricity information synchronization module comprises a brain-muscle-electricity coherence analysis unit and a brain-muscle-electricity time delay quantization unit;
the brain-muscle electrical coherence analysis unit comprises a model order determination subunit and a model coefficient estimation subunit; the model order determining subunit determines the optimal order of the mvAR model, and the model simplicity and the fitting accuracy are both considered; after the order of the model is determined, the model coefficient estimation subunit estimates the model coefficient by adopting a recursive least square algorithm with a forgetting factor, carries out electroencephalogram and electromyogram coherence analysis by utilizing the estimated parameter, and outputs the result to the electroencephalogram and electromyogram delay quantization unit for electroencephalogram and electromyogram delay quantization after determining the obvious coherence frequency band of the electroencephalogram and electromyogram;
the brain-muscle electric delay quantization unit carries out delay quantization on the brain-muscle electric data based on a maximum coherence rule, artificial translation of the delayed muscle electric data is carried out, translation quantity of the maximum coherence between the brain and the muscle is the corresponding brain-muscle electric delay, and after a plurality of tests, an average value is taken, so that the movement intention information delay quantity of the brain area and the muscle corresponding to the brain electricity and the muscle electricity channel of the subject is obtained.
The embodiment of the invention also discloses a method for synchronizing the brain and muscle electrical information captured accurately by the movement intention, which comprises the following steps:
step 1: the MVCs of the soles of the left leg and the right leg of each testee are collected, the testee keeps the ankle extending backwards, the sole pressure sensor is pressed down by the bottom surface of the heel for at least 10s, and the MVCs of the soles of the left leg and the right leg are obtained by multiple times of collection and averaging. The target plantar force of the left leg and the right leg can be obtained according to the left leg and the right leg plantar MVC, namely an output interval of 0.8MVC floating up and down is 0.8MVC +/-10N, wherein N represents Newton.
Step 2: the electroencephalogram cap and the electromyogram electrode are respectively used for synchronously acquiring an electroencephalogram signal and an electromyogram signal when a subject performs lower limb stable force output in a target output interval, and the electroencephalogram signal and the electromyogram signal are wirelessly transmitted to the signal preprocessing module;
and step 3: the signal preprocessing module is used for preprocessing the received electroencephalogram signals and the received electromyogram signals; performing baseline calibration on the electroencephalogram signals, performing 1-100Hz band-pass filtering, removing 50Hz power frequency interference by adopting self-adaptive filtering, and removing ocular artifacts by adopting an independent component analysis algorithm; carrying out 1-200Hz band-pass filtering on the electromyographic signals, and removing 50Hz power frequency interference by self-adaptive filtering;
and 4, step 4: a brain-muscle electricity coherence analysis unit in the brain-muscle electricity signal synchronization module performs coherence analysis on the preprocessed brain-muscle electricity data:
step 4-1, reconstructing the multi-channel brain myoelectricity data into high-dimensional data, and fitting by using an mvAR model, wherein the method comprises the following steps:
wherein, YtThe reconstructed high-dimensional brain and muscle electrical data; t is the data of brain and muscle electricitySample points, T ═ 1,2, … T; k is the sum of the number of the brain and muscle electricity data channels; m is the total number of trials of the subject; p is the order of the mvAR model; a. thet-iIs K x K dimension mvAR model coefficient; etThe post-fitting residual noise with covariance ∑.
Step 4-2: the model order determining subunit determines the order of the high-dimensional mvAR model fitting the brain and muscle electricity data by adopting an Akaichi Information Content (AIC)/Bayesian Information Content (BIC) rule, and the calculation formula is as follows:
wherein p is the order of the mvAR model; m is the number of experimental trials; k is the sum of the number of the brain and muscle electricity data channels; N-ML, L is the time window length.
And selecting the order p when the AIC/BIC is minimum in the fitting as the final order of the mvAR model.
Step 4-3: after the order of the model is determined, the model coefficient estimation subunit estimates the mvAR model coefficient fitting the brain and muscle electricity data by adopting a recursion least square algorithm with a forgetting factor, and the process is as follows:
determining the coefficient matrix theta of the mvAR model as shown in the following formula:
Θ＝(A1,A2...AP) (6)
wherein p is the order of the mvAR model, and A is calculated as shown in (3).
Determining mvAR model data matrix WnAs shown in the following formula:
Wn＝(Yn-1,Yn-2...Yn-p) (7)
wherein, Yn-iThe method is characterized in that the method is used for reconstructing brain myoelectricity data as shown in a formula (2); and n is the current sampling point.
Wherein Z isnFor the instantaneous error estimation matrix, giving the desired output YnError from the estimated output; n is the current sampling point; enTo accumulate errors, c is a forgetting factor, 0<c<1, the method ensures that the brain and muscle electrical data far away from the current moment are forgotten.
Recursive algorithm iteratively calculates Θ | min (E)n) Determining a final coefficient matrix theta of the mvAR model, and calculating a noise covariance matrix sigma according to the instantaneous error estimation matrix of the mvAR model, as shown in the following formula:
wherein c is a forgetting factor; n is the current sampling point; m is the number of experimental trials.
And the power spectrum matrix is shown as follows:
wherein f is the current electroencephalogram and electromyography coherent frequency; f. ofsThe brain and muscle electrical sampling frequency; a is the coefficient of the mvAR model; Σ is the fitted noise covariance, and the iterative calculation method is shown in equation (10).
According to the formula (11), the coherence of each channel of the brain and muscle electricity can be calculated as follows:
wherein f is the current electroencephalogram and electromyography coherent frequency; i, j are corresponding brain-muscle electrical channels.
Determining the central frequency f of the occurrence of significant coherence of the brain and muscle electricity after model fittingκAnd time t0And outputting the result to a brain-muscle electric time delay quantization unit;
step 4-4: the brain and muscle electric time delay quantization unit performs time delay quantization by adopting a maximum coherence rule. The electroencephalogram and electromyogram data at the central frequency and the time point of obvious coherence of each subject are manually translated, and the translation amount corresponding to the position where the electroencephalogram and electromyogram coherence reaches the maximum value is the electroencephalogram and electromyogram time delay estimated value, which is shown as the following formula:
δ＝max(Cohij(fκ)) (13)
wherein, δ is the finally quantized brain-muscle electrical time delay; cohijCoherence of the i, j channel of the brain myoelectricity; f. ofκA center frequency that is typical of coherence of the brain and muscle;
after averaging in multiple tests, the time delay of the movement intention information between the corresponding myoelectric channel and the brain electricity of the testee can be obtained, and then the synchronization of the brain myoelectric movement intention information is completed.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for synchronizing brain and muscle electrical information captured accurately by movement intentions is characterized by comprising the following steps:
acquiring a plantar MVC of a subject, calculating a plantar target force according to the plantar MVC, and confirming a target lower limb pressure output interval according to the plantar MVC;
collecting electroencephalogram signals and electromyogram signals of a subject;
preprocessing the acquired electroencephalogram signals and the acquired electromyogram signals to obtain preprocessed electroencephalogram and electromyogram data;
and carrying out coherence analysis on the brain and muscle electricity data to obtain the time delay of the movement intention information between the brain and muscle electricity of the testee.
2. The method for synchronizing the electroencephalogram and electromyogram information of an accurate motor intention capture according to claim 1, wherein the method for collecting the MVC under the sole of the subject is as follows: collecting for a plurality of times, and obtaining the MVC of the lower limb soles of the left leg and the right leg by taking an average value;
the target lower limb pressure output interval is 0.8MVC +/-10N.
3. The method for synchronizing the electroencephalogram and electromyogram information with the movement intention accurately captured according to claim 2, wherein the specific method for preprocessing the received electroencephalogram signal and electromyogram signal is as follows:
performing baseline calibration on the electroencephalogram signals, removing power frequency interference by adopting self-adaptive filtering, and removing ocular artifacts by using an independent component analysis algorithm; and performing band-pass filtering on the electromyographic signals, and performing self-adaptive filtering to remove power frequency interference so as to improve the signal-to-noise ratio of the electroencephalogram and the electromyographic signals.
4. The method for synchronizing the electroencephalogram and electromyogram information with the movement intention accurately captured according to claim 3, wherein the specific method for analyzing the coherence of the electroencephalogram and electromyogram is as follows:
reconstructing the brain-muscle electrical data into high-dimensional data, and fitting the brain-muscle electrical data by adopting a multivariate model;
determining the order of a multivariate model fitting the brain and muscle electrical data;
after the order of the model is determined, estimating a model coefficient fitting the electroencephalogram and electromyogram data;
and performing electroencephalogram and electromyogram coherence analysis by using the model with the determined parameters, determining an electroencephalogram and electromyogram coherence frequency band, and performing electroencephalogram and electromyogram time delay quantization on the result.
5. The method for synchronizing the brain-muscle electrical information with the movement intention accurately captured according to claim 4, wherein the method comprises the following steps:
the method for reconstructing the brain and muscle electrical data into high-dimensional data and adopting a multivariate model to simulate the brain and muscle electrical data comprises the following specific steps:
wherein, YtThe reconstructed high-dimensional brain and muscle electrical data; t is a brain myoelectricity data sampling point, and T is 1,2, … T; k is the sum of the number of the brain and muscle electricity data channels; m is the total number of trials of the subject; p is the model order; a. thet-iIs a K x K dimensional model coefficient; etThe post-fitting residual noise with covariance ∑.
6. The method for synchronizing the electroencephalogram and electromyogram information for accurately capturing the motor intention according to claim 5, wherein the specific calculation method for determining the order of the multivariate model of the electroencephalogram and electromyogram data comprises the following steps:
wherein p is the model order; m is the number of experimental trials; k is the sum of the number of the brain and muscle electricity data channels, N is ML, and L is the length of the time window.
7. The method for synchronizing the electroencephalogram and electromyogram information captured with accurate movement intention according to claim 6, wherein the specific method for estimating the model coefficient of the fitting electroencephalogram and electromyogram data is as follows:
determining a model coefficient matrix Θ:
Θ＝(A1,A2...AP) (6)
wherein p is the model order, and the calculation method of A is shown as formula (3);
determining a model data matrix Wn：
Wn＝(Yn-1,Yn-2...Yn-p) (7)
Wherein, Yn-iThe reconstructed brain and muscle electrical data shown in the formula (2) is shown in the specification, and n is a current sampling point;
wherein Z isnFor the instantaneous error estimation matrix, giving the desired output YnError from the expected output; n is the current sampling point; enTo accumulate errors, c is a forgetting factor, 0<c<1, ensuring that the brain and muscle electrical data far away from the current moment are forgotten;
recursive algorithm iteratively calculates Θ | min (E)n) Determining a final model coefficient matrix theta, and calculating a noise covariance matrix sigma according to the model instantaneous error estimation matrix, as shown in the following formula:
wherein c is a forgetting factor; n is the current sampling point; m is the number of experimental trials;
the calculation method of the power spectrum matrix comprises the following steps:
wherein f is the current electroencephalogram and electromyography coherent frequency; f. ofsThe brain and muscle electrical sampling frequency; a is the coefficient of the mvAR model; Σ is the fitted noise covariance,an iterative calculation method is shown as an expression (10);
according to the formula (11), the coherence of each channel of the brain and muscle electricity can be calculated as follows:
wherein f is the current electroencephalogram and electromyography coherent frequency; i, j are corresponding brain-muscle electrical channels;
determining the central frequency f of the occurrence of significant coherence of the brain and muscle electricity after model fittingκAnd time t0And outputting the result to a brain-muscle electric time delay quantization unit.
8. The method for synchronizing the electroencephalogram and electromyogram information captured with accurate movement intention according to claim 7, wherein the specific method for performing time delay quantization comprises the following steps:
and (3) performing artificial translation on the delayed data of the central frequency and the brain and muscle electricity at the moment of the appearance of the subject, wherein the artificial translation is expressed by the formula (13):
δ＝max(Cohij(fκ)) (13)
wherein δ is the finally quantized brain-muscle electrical time delay; cohijCoherence of the i, j channel of the brain myoelectricity; f. ofκA center frequency that is typical of coherence of the brain and muscle;
after a plurality of tests are carried out, the average value is obtained, the movement intention information time delay between the corresponding myoelectric channel of the testee and the brain electricity is obtained, and the brain myoelectric movement intention information synchronization is completed.
9. A brain-muscle-electricity information synchronization system capable of accurately capturing movement intentions is characterized by comprising a steady-state force acquisition module, a brain-muscle-electricity acquisition module, a signal preprocessing module and a brain-muscle-electricity information synchronization module;
the steady-state force acquisition module is used for acquiring the MVC of the sole of the subject, calculating the target force of the sole according to the MVC of the sole and confirming the target lower limb pressure output interval according to the MVC of the sole;
the brain-myoelectricity acquisition module is used for acquiring electroencephalogram signals and myoelectricity signals of a subject;
the signal preprocessing module is used for preprocessing the acquired electroencephalogram signals and the acquired electromyogram signals to obtain preprocessed electroencephalogram and electromyogram data;
the brain-muscle and electric-power information synchronization module is used for performing coherence analysis on the brain-muscle and electric-power data to obtain the time delay of movement intention information between the brain and the electric power of the testee.
10. The system for synchronizing the electroencephalogram and electromyogram information for accurately capturing the motor intention according to claim 9, wherein the electroencephalogram and electromyogram information synchronizing module comprises an electroencephalogram and electromyogram coherence analyzing unit and an electroencephalogram and electromyogram time delay quantizing unit;
the electroencephalogram and electromyogram coherence analysis unit determines the order of the fitting model and estimates the coefficient of the fitting model, the electroencephalogram and electromyogram coherence analysis is carried out by utilizing the model with determined parameters, and after the electroencephalogram and electromyogram coherence frequency band and time are determined, the result is output to the electroencephalogram and electromyogram delay quantization unit for electroencephalogram and electromyogram delay quantization;
the electroencephalogram and electromyogram time delay quantization unit is used for carrying out time delay quantization on the electroencephalogram and electromyogram data, carrying out artificial translation on the hysteresis electromyogram data, testing for a plurality of times and then averaging to obtain intention information time delay quantities of a brain area and a muscle corresponding to an electroencephalogram and electromyogram channel of a subject, and the electroencephalogram and electromyogram data after time delay offset are used for capturing human body movement intention.
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