CN113647938A - Method and system for advanced detection of motion state change based on physiological signals - Google Patents
Method and system for advanced detection of motion state change based on physiological signals Download PDFInfo
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Abstract
The invention discloses a method and a system for advanced detection of motion state change based on physiological signals, wherein the method comprises the following steps: s1, collecting three physiological signals; the three physiological signals comprise a brain hemoglobin signal, a lower limb surface electromyogram signal and a lower limb gait parameter; s2, preprocessing the three acquired physiological signals; s3, calculating characteristic parameters of the three preprocessed physiological signals; and S4, synchronously observing the characteristic parameters calculated by the three physiological signals, and counting the sequence of the initial moments of the change of the motion states detected by the three physiological signals. The method is more beneficial to extracting the key information representing the movement intention through the multi-metadata fusion analysis, and the discrimination performance of the system is improved; meanwhile, the time range allowed by signal processing, recognition and control instruction transmission is established, the time delay of the output of the control instruction of the brain-computer interface system can be reduced, and the calculation complexity is low.
Description
Technical Field
The invention relates to the technical field of brain-computer interface real-time control, in particular to a method and a system for advanced detection of motion state change based on physiological signals.
Background
The aging of the population is a social problem which needs to be emphasized in China, and the number of the aged population in China in 2018 accounts for 11.19% of the total population. Data show that the proportion of middle-aged and elderly people is the highest in patients with cerebral apoplexy. Meanwhile, aging leads to deterioration of physical quality in all aspects, and the probability of dysfunction due to accidents is increased. Therefore, an intelligent rehabilitation training mode is provided for stroke patients and patients with motor dysfunction, and the method has important significance in effectively recovering the motor functions of the stroke patients and the patients with motor dysfunction.
Previous studies have shown that when walking with robotic assistance, more brain areas are activated, most contributing to the rehabilitation of the patient's upper and lower limb motor functions. Therefore, the robot is selected to cooperatively control the human body movement, so that the patient can be better helped to recover the movement function autonomously. However, in most of the current researches, the control commands output by the brain-computer interface system have the problems of leading and lagging. In an actual application scene, the brain-computer interface system hopes to give out a control instruction to adjust the mechanism to move when the human body really moves, so that the time delay of the system for outputting the control instruction is reduced, the real man-computer synchronous control is realized, and a patient can obtain better use experience in the rehabilitation training process.
Research shows that the motor cortex trigger potential can be detected within 1-2 seconds before the spontaneous movement of a human body based on the EEG (electroencephalogram) (EEG) technology, the movement intention can be distinguished in advance based on the EEG and Electromyogram (EMG) technology of multivariate data fusion, and an important theoretical basis is laid for realizing the man-machine synchronous control technology. However, the EEG equipment has a great deal of noise in signals collected under the condition of large-amplitude limb movement, which can cause obvious interference to an electroencephalogram; whereas exoskeleton devices based on EMG technology require that the movement intention be detected while the muscles are active, plus signal processing and command transmission time, further extend the lag time of device control. In recent years, the near-infrared functional imaging technology has the advantages of higher signal-to-noise ratio compared with an EEG (electroencephalogram) technology, relative insensitivity to body motion and the like because the safety of signal acquisition can be ensured. Therefore, the functional near-isolated surgery (fNIRS) technology has a large development space in the application of the judgment of the movement intention of the limbs requiring a large movement during walking and the control of the assisted walking equipment.
Disclosure of Invention
The invention aims to provide a method for detecting the change of the motion state based on the advance of the physiological signal, which reduces the calculation complexity and can reduce the time delay of the output control command of a brain-computer interface system.
In order to solve the above problems, the present invention provides a method for detecting a motion state change based on an advance of a physiological signal, comprising the steps of:
s1, collecting three physiological signals; the three physiological signals comprise a brain hemoglobin signal, a lower limb surface electromyogram signal and a lower limb gait parameter;
s2, preprocessing the three acquired physiological signals;
s3, calculating characteristic parameters of the three preprocessed physiological signals; the characteristic parameters of the cerebral hemoglobin signal are TKE energy operators or brain function network topological attributes, and the characteristic parameters of the lower limb surface electromyographic signal and the lower limb gait parameter are TKE energy operators;
and S4, synchronously observing the characteristic parameters calculated by the three physiological signals, and counting the sequence of the initial moments of the change of the motion states detected by the three physiological signals.
As a further improvement of the invention, the calculation formula of the TKE energy operator is as follows:
wherein x istRepresenting the value of the signal at time t, xt+1And xt-1Representing the signal values at time t +1 and t-1, respectively.
As a further improvement of the present invention, the brain function network topology attributes are calculated as follows:
A. dividing a brain area according to the distribution position of the cerebral cortex;
B. the entropy weight method is used for redistributing the weight and the blood oxygen value to different channels of the same brain area, and the Pearson correlation coefficient of the blood oxygen signal between every two brain areas is calculated;
C. constructing an adjacency matrix through the Pearson correlation coefficient;
D. constructing a brain function network based on the adjacency matrix, and calculating the topological attribute of the brain function network; the brain function network topology attributes comprise brain network density and clustering coefficients.
As a further improvement of the invention, step B comprises:
b1, assuming that the ROI brain region X contains N channels, each channel consists of M sampling points, first, the maximum minimization process is performed on the sampling points in each channel:
wherein, i is 1,2, 1, M, j is 1,2, 1ij∈X；
B2, calculating probability value P of each channel sampling point in ROI brain areaij：
B3, calculating the information entropy e of each channel in the ROI brain areajThe numerical range is [0, 1 ]]Internal:
b4, calculating the weight w of each channel in the ROI brain area according to the information entropyij：
B5, calculating the blood oxygen signal Y of the ROI brain area according to the weight of each channelROI：
YROI＝∑jxijwj
B6, sequentially calculating the Pearson correlation coefficient R of the blood oxygen signals between two different ROI brain areas:
as a further improvement of the present invention, the calculation formula of the brain network density is as follows:
where DEN is brain network density, M is the number of edges actually existing in the network, and N is the number of nodes in the brain function network.
As a further improvement of the present invention, the calculation formula of the clustering coefficient is as follows:
wherein, CiIs a clustering coefficient, kiIs the number of neighbors of node i, EiIs the number of edges that actually exist between the k neighbors of node i.
As a further improvement of the present invention, the synchronously observing the characteristic parameters calculated by the three physiological signals includes:
and according to the multiple relation between the sampling frequency of the surface electromyography equipment and the inertial sensor and the sampling frequency of the functional infrared spectrum equipment, reducing the data volume of the collected lower limb surface electromyography signals and the data volume of the lower limb gait parameters to be equal to the data volume of the brain hemoglobin signals.
As a further improvement of the present invention, the counting the sequence of the initial time when the motion state detected by the three physiological signals changes includes:
for each physiological signal, taking the mean value and standard deviation calculated by 20 seconds of rest segment signals before exercise as a reference, and judging that the change of the exercise state is detected if the numerical value of the signals within the range of 2 seconds before and after the exercise is out of the range of 3 times of the standard deviation of the mean value of the rest segment signals; and comparing the time values of the three physiological signals when the change of the motion state is detected.
As a further improvement of the present invention, step S2 includes:
and performing band-pass filtering processing on corresponding frequency bands of the three acquired physiological signals by using a Butterworth band-pass filtering method to remove interference information in the signals.
The invention also provides a system for advanced detection of motion state changes based on physiological signals, comprising:
the acquisition module is used for acquiring three physiological signals; the three physiological signals comprise a brain hemoglobin signal, a lower limb surface electromyogram signal and a lower limb gait parameter;
the preprocessing module is used for preprocessing the three acquired physiological signals;
the feature extraction module is used for calculating feature parameters of the three preprocessed physiological signals; the characteristic parameters of the cerebral hemoglobin signal are TKE energy operators or brain function network topological attributes, and the characteristic parameters of the lower limb surface electromyographic signal and the lower limb gait parameter are TKE energy operators;
and the statistical module is used for synchronously observing the characteristic parameters calculated by the three physiological signals and counting the sequence of the initial moments of the change of the motion states detected by the three physiological signals.
The invention has the beneficial effects that:
the method and the system for detecting the change of the motion state in advance based on the physiological signals calculate the characteristic parameters of the three-axis physiological signals and synchronously observe the calculated characteristic parameters of the three physiological signals, thereby counting the sequence of the initial moments of the change of the motion state detected by the three physiological signals. The method is more beneficial to extracting the key information representing the movement intention through the multi-metadata fusion analysis, and the discrimination performance of the system is improved; meanwhile, the time range allowed by signal processing, recognition and control instruction transmission is established, the time delay of the output of the control instruction of the brain-computer interface system can be reduced, and the calculation complexity is low.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical means of the present invention more clearly understood, the present invention may be implemented in accordance with the content of the description, and in order to make the above and other objects, features, and advantages of the present invention more clearly understood, the following preferred embodiments are described in detail with reference to the accompanying drawings.
Drawings
FIG. 1 is a flow chart of a method of detecting a change in motion state based on an advance in a physiological signal in a preferred embodiment of the invention;
FIG. 2a is a graph of the time series of brain network attributes of brain hemoglobin signals in the neural activity band in a preferred embodiment of the present invention; FIG. 2b is a graph of the time series of brain network properties of brain hemoglobin signals within the frequency band of endothelial cell metabolic activity in a preferred embodiment of the present invention;
fig. 3 is a schematic diagram of characteristic parameter synchronous observation of three physiological signals of the invention.
Detailed Description
The present invention is further described below in conjunction with the following figures and specific examples so that those skilled in the art may better understand the present invention and practice it, but the examples are not intended to limit the present invention.
As shown in fig. 1, the method for detecting motion state change based on the advance of physiological signals in the preferred embodiment of the present invention comprises the following steps:
s1, collecting three physiological signals; the three physiological signals comprise a brain hemoglobin signal, a lower limb surface electromyogram signal and a lower limb gait parameter. The multivariate data fusion analysis is more beneficial to extracting the key information representing the movement intention, and the discrimination performance of the system is improved.
And S2, preprocessing the three acquired physiological signals. The method is beneficial to removing interference information in the signals, reserving key physiological information frequency bands beneficial to distinguishing motion states, and improving the real-time performance of the system.
S3, calculating characteristic parameters of the three preprocessed physiological signals; the characteristic parameters of the cerebral hemoglobin signal are TKE energy operators or brain function network topological attributes, and the characteristic parameters of the lower limb surface electromyographic signal and the lower limb gait parameter are TKE energy operators;
and S4, synchronously observing the characteristic parameters calculated by the three physiological signals, and counting the sequence of the initial moments of the change of the motion states detected by the three physiological signals.
The method and the system for detecting the change of the motion state in advance based on the physiological signals calculate the characteristic parameters of the three-axis physiological signals, synchronously observe the calculated characteristic parameters of the three physiological signals, count the sequence of the initial moments when the motion states detected by the three physiological signals change, determine the time range allowed by signal processing, recognition and control instruction transmission, reduce the time delay of the output of the control instruction by the brain-computer interface system and have low calculation complexity.
In step S1: optionally, the brain hemoglobin information is acquired by a portable near-infrared brain imaging device nirsort manufactured by comet-based medical company. Nirstart is portable and subjects can carry it on their back during signal acquisition. The test wavelengths were 780nm, 805nm and 830nm, and the sampling frequency was 16 Hz. Nirstart has 8 receivers and 8 emitters, and uses a synchronous holder to hold the detectors and emitters, with the distance between adjacent receivers and emitters fixed at 3 cm. Previous studies have shown a significant increase in blood oxygen content of Supplementariy MotorArea (SMA) during walking activities; it was found that Prefrontal Cortex (PFC) was significantly activated during the walk preparation phase, and PFC and Premotor Cortex (PMC) blood oxygen content increased significantly with increasing pace. Thus PFC, PMC and SMA are suitable as measurement areas for brain oximetry signals.
Optionally, the electromyographic signals of the surface of the lower limb are acquired by MyoSystem1400A manufactured by NORAXON. The MyoSystemm 1400A has 8 acquisition channels, the sampling frequency is 1000Hz, and the acquisition result is transmitted to the computer through a USB interface. Research shows that the muscle activity and the mobilization enhancement degree of the tibialis anterior muscle are more obvious when walking; the muscles of soleus, gastrocnemius, etc. tend to be gradually strengthened with the increase of weight when walking, and the soleus is the main walking-promoting muscle group. Therefore, the tibialis anterior, soleus and gastrocnemius are suitable as measurement regions for the lower limb surface electromyographic signals.
Optionally, the lower limb gait parameters are acquired by Xsens-MCS manufactured by mCube. The Xsens-MCS is composed of PC end software and Motion Capture Module (MCM). The X sens-MCS calculates joint motion information including joint rotation angles, angular speeds and angular accelerations based on the collected limb pose information, the sampling frequency is 100Hz, and the motion information of each joint is transmitted to the PC in real time through the USB interface. The knee joint is suitable as a measurement area for lower limb gait parameters.
Alternatively, the walking experiment was performed on a quiet corridor, and the walking distance was 15 meters. The subject is required to finish walking in a natural state, stand in place when reaching the terminal position and have a rest for a period of time, turn around to prepare the next walking, and the whole process is repeated for 4 times. Each rest time is not less than 30s, and the experiment operator gives instructions of 'experiment start' and 'experiment end' at the beginning and the end of the experiment respectively, and the experiment is performed by the testee spontaneously during the experiment. All subjects required 2-3 pre-experiments to ensure that they were in the possession and familiar with the overall experimental protocol before the experiment formally started.
In some embodiments, step S2 includes:
and performing band-pass filtering processing on corresponding frequency bands of the three acquired physiological signals by using a Butterworth band-pass filtering method to remove interference information in the signals.
The brain hemoglobin information carries a lot of physiological information in the collection process, besides the noise, as shown in table 1. The identification of the walking state mainly considers the distinction between the rest state and the walking state. It is considered that the heart rate, respiration and the like of a person in a walking state are obviously different from those in a rest state, and the signal processing time is required to be fast. Therefore, an infinite impulse response band-pass filtering method based on a Butterworth filter is adopted to filter the original signal to a frequency band of 0.6-2.0 Hz.
TABLE 1
The surface electromyography signal is a very weak signal with a frequency spectrum in the range of 0-1000Hz, the maximum frequency of the power spectrum depending on the muscle. Usually the main energy of the surface electromyographic signals is concentrated between 10-300 Hz. In addition, the surface electromyogram signal can be influenced by power frequency interference in the acquisition process. Therefore, in addition to using a Butterworth filter for 10-300Hz bandpass filtering, 50Hz bandstop filtering is required.
The inertial sensor has a certain zero drift phenomenon in the process of acquiring gait parameters. In addition, because of the reasons such as the shake of equipment, the unstability of dupont line connection, be mingled with many interfering signal, a lot of burrs can appear in the gait parameter of gathering. In order to effectively reduce the influence of the interference factors, a Butterworth band-pass filter is adopted to filter the original gait parameters to a frequency band of 0.15-6 Hz.
The TKE energy operator (Teager-Kaiser) can well reflect the instantaneous frequency and amplitude of the signal, has high calculation efficiency and is widely applied to the field of biological signal identification. The TKE energy operator only needs three adjacent sampling points for one-time calculation, the operation process comprises two times of multiplication and one time of addition, the operation speed is high, and the real-time requirement is met. The calculation formula of the TKE energy operator is as follows:
wherein x istRepresenting the value of the signal at time t, xt+1And xt-1Representing the signal values at time t +1 and t-1, respectively.
Before brain area correlation analysis is carried out on hemoglobin signals, ROI brain areas are required to be divided for a cerebral cortex test area, and specific brain area channel distribution is shown in a table 2. By dividing the ROI brain region, individual differences caused by the size of the skull can be attenuated to some extent.
TABLE 2
Specifically, the brain function network topology attribute is calculated as follows:
A. dividing a brain area according to the distribution position of the cerebral cortex;
B. the entropy weight method is used for redistributing the weight and the blood oxygen value to different channels of the same brain area, and the Pearson correlation coefficient of the blood oxygen signal between every two brain areas is calculated;
C. constructing an adjacency matrix through the Pearson correlation coefficient;
D. constructing a brain function network based on the adjacency matrix, and calculating the topological attribute of the brain function network; the brain function network topology attributes comprise brain network density and clustering coefficients.
In one embodiment, step B includes:
b1, assuming that the ROI brain region X contains N channels, each channel consists of M sampling points, first, the maximum minimization process is performed on the sampling points in each channel:
wherein, i is 1,2, 1, M, j is 1,2, 1ij∈X；
B2, calculating probability value P of each channel sampling point in ROI brain areaij：
B3, calculating the information entropy e of each channel in the ROI brain areajThe numerical range is [0, 1 ]]Internal:
b4, calculating the weight w of each channel in the ROI brain area according to the information entropyij：
B5, calculating the blood oxygen signal Y of the ROI brain area according to the weight of each channelROI：
YROI＝∑jxijwj
B6, sequentially calculating the Pearson correlation coefficient R of the blood oxygen signals between two different ROI brain areas:
after an adjacent matrix is constructed through the Pearson correlation coefficient, the upper diagonal element of the adjacent matrix is set to be 0, and the calculation formula of the brain network density is as follows:
where DEN is brain network density, M is the number of edges actually existing in the network, and N is the number of nodes in the brain function network.
The calculation formula of the clustering coefficient is as follows:
wherein, CiIs a clustering coefficient, kiIs the number of neighbors of node i, EiIs the number of edges that actually exist between the k neighbors of node i.
In one embodiment, the brain network parameters corresponding to the 4 repeated rest-walking tasks in the whole experimental process are subjected to mean processing to obtain the brain network parameters with average tasks. Fig. 2a and 2b show the brain network density and clustering coefficient time series change diagram in the neural activity frequency band and the endothelial cell metabolism activity frequency band, respectively.
In step S4, the synchronously observing the characteristic parameters calculated by the three physiological signals includes:
and according to the multiple relation between the sampling frequency of the surface electromyography equipment and the inertial sensor and the sampling frequency of the functional infrared spectrum equipment, reducing the data volume of the collected lower limb surface electromyography signals and the data volume of the lower limb gait parameters to be equal to the data volume of the brain hemoglobin signals.
The counting of the sequence of the initial moments when the motion states detected by the three physiological signals change comprises the following steps:
for each physiological signal, taking the mean value and standard deviation calculated by 20 seconds of rest segment signals before exercise as a reference, and judging that the change of the exercise state is detected if the numerical value of the signals within the range of 2 seconds before and after the exercise is out of the range of 3 times of the standard deviation of the mean value of the rest segment signals; and comparing the time values of the three physiological signals when the change of the motion state is detected.
And the sampling frequencies corresponding to the brain hemoglobin signal, the surface electromyogram signal and the gait parameter are different. In order to synchronously observe the synchronicity of the three physiological signals at the same sampling time, the number of sampling points of the three physiological signals needs to be ensured to be the same.
In one embodiment, the sampling frequency used for collecting the lower limb surface electromyography signals is 1000, the sampling frequency used for collecting the gait parameters is 100, and the sampling frequency used for collecting the brain hemoglobin signals is 16. Therefore, according to the multiple relation, the surface electromyogram signal needs to take a sampling point at an interval of 62.5, the gait parameter needs to take a sampling point at an interval of 6.25, and the number of the sampling points of the brain hemoglobin signal is kept unchanged. Fig. 3 is a schematic diagram of synchronous observation of three physiological signals.
After the data are synchronized, according to the Mark point marked by the equipment in the experimental process, 20-second rest segment data before the Mark point is extracted, and the average value and the standard deviation are respectively calculated according to the segment data. And then judging whether the difference value of the mean value of the data and the rest segment data exceeds the range of 3 times of standard deviation within the time range of 2 seconds before and after the Mark point. If so, the point is determined to be the starting time of the change of the motion state. Table 3 is a statistical diagram of the initial time when the motion state detected by the three physiological signals changes.
TABLE 3
The invention also discloses a system for advanced detection of motion state change based on physiological signals, which comprises the following modules:
the acquisition module is used for acquiring three physiological signals; the three physiological signals comprise a brain hemoglobin signal, a lower limb surface electromyogram signal and a lower limb gait parameter;
the preprocessing module is used for preprocessing the three acquired physiological signals;
the feature extraction module is used for calculating feature parameters of the three preprocessed physiological signals; the characteristic parameters of the cerebral hemoglobin signal are TKE energy operators or brain function network topological attributes, and the characteristic parameters of the lower limb surface electromyographic signal and the lower limb gait parameter are TKE energy operators;
and the statistical module is used for synchronously observing the characteristic parameters calculated by the three physiological signals and counting the sequence of the initial moments of the change of the motion states detected by the three physiological signals.
The calculation method in the system for detecting motion state change based on the advance of physiological signals according to the present invention is the same as the above method embodiment, and will not be described herein again.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications of the invention may be made without departing from the spirit or scope of the invention.
Claims (10)
1. A method for detecting a change in motion state based on an advance in a physiological signal, comprising the steps of:
s1, collecting three physiological signals; the three physiological signals comprise a brain hemoglobin signal, a lower limb surface electromyogram signal and a lower limb gait parameter;
s2, preprocessing the three acquired physiological signals;
s3, calculating characteristic parameters of the three preprocessed physiological signals; the characteristic parameters of the cerebral hemoglobin signal are TKE energy operators or brain function network topological attributes, and the characteristic parameters of the lower limb surface electromyographic signal and the lower limb gait parameter are TKE energy operators;
and S4, synchronously observing the characteristic parameters calculated by the three physiological signals, and counting the sequence of the initial moments of the change of the motion states detected by the three physiological signals.
2. The method for advanced detection of kinetic state changes based on physiological signals as claimed in claim 1, wherein the TKE energy operator is calculated as follows:
wherein x istRepresenting the value of the signal at time t, xt+1And xt-1Representing the signal values at time t +1 and t-1, respectively.
3. A method for advanced detection of movement state changes based on physiological signals as set forth in claim 1, characterized in that brain function network topology attributes are calculated as follows:
A. dividing a brain area according to the distribution position of the cerebral cortex;
B. the entropy weight method is used for redistributing the weight and the blood oxygen value to different channels of the same brain area, and the Pearson correlation coefficient of the blood oxygen signal between every two brain areas is calculated;
C. constructing an adjacency matrix through the Pearson correlation coefficient;
D. constructing a brain function network based on the adjacency matrix, and calculating the topological attribute of the brain function network; the brain function network topology attributes comprise brain network density and clustering coefficients.
4. A method for detecting motion state changes based on leads of physiological signals as set forth in claim 3, wherein step B comprises:
b1, assuming that the ROI brain region X contains N channels, each channel consists of M sampling points, first, the maximum minimization process is performed on the sampling points in each channel:
wherein i is 1,2, …, M, j is 1,2, …, N, xij∈X；
B2, calculating probability value P of each channel sampling point in ROI brain areaij:
B3, calculating the information entropy e of each channel in the ROI brain areajThe numerical range is [0, 1 ]]Internal:
b4, calculating the weight w of each channel in the ROI brain area according to the information entropyij：
B5, calculating the blood oxygen signal Y of the ROI brain area according to the weight of each channelROI：
YROI＝∑jxijwj
B6, sequentially calculating the Pearson correlation coefficient R of the blood oxygen signals between two different ROI brain areas:
5. the method for advanced detection of motion state changes based on physiological signals as set forth in claim 4, wherein the brain network density is calculated as follows:
where DEN is brain network density, M is the number of edges actually existing in the network, and N is the number of nodes in the brain function network.
6. The method for detecting motion state changes based on the leads of physiological signals as set forth in claim 4, wherein the clustering coefficients are calculated as follows:
wherein, CiIs a clustering coefficient, kiIs the number of neighbors of node i, EiIs k of node iThe number of edges that actually exist between neighbors.
7. A method for detecting motion state changes based on the advance of physiological signals as claimed in claim 1, wherein the synchronously observing the characteristic parameters calculated by the three physiological signals comprises:
and according to the multiple relation between the sampling frequency of the surface electromyography equipment and the inertial sensor and the sampling frequency of the functional infrared spectrum equipment, reducing the data volume of the collected lower limb surface electromyography signals and the data volume of the lower limb gait parameters to be equal to the data volume of the brain hemoglobin signals.
8. The method for advanced detection of motion state changes based on physiological signals as claimed in claim 1, wherein the step of counting the sequence of the initial moments when the motion states detected by the three physiological signals change comprises:
for each physiological signal, taking the mean value and standard deviation calculated by 20 seconds of rest segment signals before exercise as a reference, and judging that the change of the exercise state is detected if the numerical value of the signals within the range of 2 seconds before and after the exercise is out of the range of 3 times of the standard deviation of the mean value of the rest segment signals; and comparing the time values of the three physiological signals when the change of the motion state is detected.
9. The method for detecting motion state change based on the physiological signal lead as set forth in claim 1, wherein the step S2 comprises:
and performing band-pass filtering processing on corresponding frequency bands of the three acquired physiological signals by using a Butterworth band-pass filtering method to remove interference information in the signals.
10. A system for detecting a change in a motion state based on an advance in a physiological signal, comprising:
the acquisition module is used for acquiring three physiological signals; the three physiological signals comprise a brain hemoglobin signal, a lower limb surface electromyogram signal and a lower limb gait parameter;
the preprocessing module is used for preprocessing the three acquired physiological signals;
the feature extraction module is used for calculating feature parameters of the three preprocessed physiological signals; the characteristic parameters of the cerebral hemoglobin signal are TKE energy operators or brain function network topological attributes, and the characteristic parameters of the lower limb surface electromyographic signal and the lower limb gait parameter are TKE energy operators;
and the statistical module is used for synchronously observing the characteristic parameters calculated by the three physiological signals and counting the sequence of the initial moments of the change of the motion states detected by the three physiological signals.
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