Cerebral function coupling analysis method based on electrophysiological and blood microcirculation signals

Abstract

Disclosed in the present invention is a cerebral function coupling analysis method based on electrophysiological and blood microcirculation signals. The method comprises: in different motion states, synchronously acquiring an electroencephalogram signal and an fNIRS signal of a subject, and preprocessing same to obtain a preprocessed electroencephalogram signal and a cerebral oxygen signal; performing complete ensemble empirical mode decomposition with adaptive noise on the electroencephalogram signal, so as to obtain a plurality of different sub-band components; symbolizing the plurality of different sub-band components of the electroencephalogram signal and a time sequence of the cerebral oxygen signal, so as to obtain symbolic sequences of the different sub-band components and a symbolic sequence of the cerebral oxygen signal; calculating the transfer entropy between every pair of the symbolic sequences of the different sub-band components and the symbolic sequence of the cerebral oxygen signal; and on the basis of the transfer entropy, analyzing the degree of coupling between the electroencephalogram signal and the cerebral oxygen signal. The present invention explores nonlinear coupling characteristics and information transfer of electrophysiological and blood microcirculation signals, thereby providing a basis for exploring the mechanism of generation of motion dysfunction and for proposing cooperative rehabilitation intervention means.

Description

A brain function coupling analysis method based on electrophysiological and blood microcirculation signals Technical Field

[0001] The present invention relates to the field of signal processing technology, and more specifically, to a brain function coupling analysis method based on electrophysiological and blood microcirculation signals. Background Art

[0002] Stroke is one of the causes of motor dysfunction. A stroke can damage higher-level central neurons, lead to loss of control of lower motor neurons, and result in hyperactive reflexes. The nervous system regulates almost all functional activities of the human body. The central nervous system comprises the brain and spinal cord, while the peripheral nervous system includes limb motor nerves and autonomic nerves that control the activity of internal organs such as the cardiovascular system and the heart and lungs. The brain receives sensory information through spinal cord pathways and issues motor commands to control limb movement, regulating heartbeat and breathing through the autonomic nervous system. Peripheral nerves control the movement of skeletal, cardiac, and respiratory muscles by emitting action potentials. On the other hand, the normal functioning of blood circulation provides oxygen and nutrients to every tissue and organ in the body and eliminates carbon dioxide and metabolites. Impairment of circulatory system function often leads to impairment of the nervous system, and neural metabolism can be reflected in blood flow and blood oxygen levels.

[0003] Coupling is a universal relationship between various systems, tissues, and organs in the human body. The coordinated work and mutual influence of these systems, tissues, and organs determine human behavior and function. Because the principles of neuromuscular coupling during exercise remain unclear, traditional exercise training and rehabilitation therapies are ineffective in motor rehabilitation. To scientifically formulate motor rehabilitation programs for patients with motor dysfunction and understand the coupling principles during exercise, studying them from an informatics perspective is a highly effective approach. The neurovascular unit (NVU) is a key research unit for observing the relationship between nerves and blood circulation, and electrophysiological signals and blood microcirculatory signals are two important signals for detecting neural and vascular activity. Simultaneously acquiring electrophysiological and blood microcirculatory signals, preprocessing and scaling them, and then analyzing them using parameters that quantify nonlinear coupling characteristics to explore coupling mechanisms, has significant implications for developing comprehensive motor function interventions, restoring neuromuscular function, developing novel rehabilitation robotic systems for implementation, and improving motor rehabilitation outcomes for bedridden patients with hemiplegia.

[0004] In the prior art, there are many methods for analyzing the coupling relationship of multimode signals. According to the type of coupling relationship analyzed, they can be divided into linear and nonlinear analysis methods; according to the function of the analysis, they can be divided into undirected and directional analysis methods.

[0005] For example, coherence is a linear, undirected analysis method used to measure the degree of linear correlation between two time series. It is defined as the normalization of the cross-spectral density to the auto-spectral density. Due to its simple algorithm principle and strong stability, coherence is widely used to describe the coupling relationship of physiological signals. However, coherence requires frequency domain analysis of the two signals, so frequency domain algorithms need to be used. The calculation of frequency domain algorithms is relatively complex and requires a large amount of computing resources and time. Coherence cannot distinguish between the amplitude and phase of the signal, and its application premise is that the analyzed signal is assumed to be stationary. Traditional coherence analysis is not sufficient to accurately describe the nonlinear characteristics between signals and may lead to an overestimation of the coupling strength.

[0006] Mutual information (MI) is a measure of information in information theory that represents the amount of information one random variable contains about another. It is a nonlinear, undirected analytical method used to determine the statistical independence between two time series. However, calculating MI requires extensive data and relies on joint probability density estimation.

[0007] Granger causality is a linear, directional analytical method that can describe bidirectional information transmission and is relatively computationally simple. However, it can only measure linear synchronous causal coupling relationships and information flows between time series, and requires the time series to be stationary. It is difficult to quantitatively analyze complex causal coupling relationships, such as nonlinear and high-order ones. Furthermore, the Granger causality method cannot handle problems such as noise interference. Its model requires a large amount of data and appropriate data preprocessing to achieve good estimation results.

[0008] Empirical mode decomposition (EMD) is an adaptive signal decomposition method that effectively decomposes both nonlinear and nonstationary signals, better describing their transient behavior and spectral characteristics. However, EMD results may exhibit modal aliasing, where different modes interfere with or overlap each other, affecting signal analysis.

[0009] Ensemble empirical mode decomposition (EMD) can overcome the randomness inherent in EMD and, to a certain extent, eliminate modal aliasing. Its adaptability and flexibility allow it to adapt to different types of signals and noise, improving the applicability and accuracy of signal decomposition. However, this decomposition method requires multiple noise perturbations and decompositions. Excessive noise levels or too few decompositions can compromise the quality of the decomposition results.

[0010] Analysis has shown that while various methods exist for time-frequency analysis of signals, traditional Fourier transforms cannot effectively represent and analyze non-periodic signals and suffer from accuracy issues. In the high-frequency portion of a signal, the uncertain amplitude and phase of the sine and cosine functions can lead to signal processing errors and distortion. Furthermore, large-scale signal processing is computationally complex and expensive. While empirical mode decomposition (EMD) can adaptively decompose signals and better describe their spectral characteristics, its decomposition results can suffer from modal aliasing, which can affect signal analysis.

[0011] In summary, existing methods for analyzing coupling relationships in physiological signals include coherence, mutual information, and Granger causality. Traditional coherence methods are linear and undirected analysis methods that cannot distinguish between signal amplitude and phase, and their application assumes that the signals being analyzed are stationary. Traditional coherence analysis is insufficient to accurately describe nonlinear characteristics between signals and may overestimate the strength of coupling. Mutual information is a nonlinear, undirected analysis method used to calculate the statistical independence between two time series. However, mutual information only measures the correlation between two variables and cannot accurately reflect the causal relationship between them. Furthermore, its calculation requires a long amount of data, and its estimation relies on joint probability density estimation. Granger causality is a linear and directed analysis method, but it can only measure linear synchronous causal coupling relationships and information flow between time series. It also requires stationary time series, making it difficult to quantitatively analyze complex causal coupling relationships, such as nonlinear and high-order causal coupling. Furthermore, Granger causality methods cannot handle noise interference and require a large amount of data and appropriate data preprocessing to achieve good estimation results.

[0012] Summary of the Invention

[0013] The purpose of the present invention is to overcome the above-mentioned shortcomings of the prior art and provide a method for analyzing brain function coupling based on electrophysiological and blood microcirculatory signals. The method comprises the following steps:

[0014] Under different motion states, the subjects' EEG signals and fNIRS signals were synchronously collected and preprocessed to obtain preprocessed EEG signals and brain oxygen signals;

[0015] Performing fully adaptive noise set empirical mode decomposition on the EEG signal to obtain multiple different sub-band components;

[0016] symbolizing the multiple different sub-band components of the EEG signal and the time series of the brain oxygen signal to obtain symbolized sequences of the different sub-band components and the symbolized sequence of the brain oxygen signal;

[0017] Calculating the transfer entropy between the symbolized sequences of the different sub-frequency band components and the symbolized sequences of the brain oxygen signal;

[0018] The coupling degree between the EEG signal and the brain oxygen signal is analyzed based on the transfer entropy.

[0019] Compared with the existing technology, the advantage of the present invention is that, in view of the unclear coupling mechanism between electrophysiological signals and blood microcirculation information in the current nervous system, it is difficult to propose multi-angle collaborative rehabilitation intervention measures for bedridden patients with hemiplegia caused by stroke, the present invention proposes a coupling relationship analysis method based on electrophysiological and blood microcirculation signals, and explores the nonlinear coupling characteristics and information transmission of electrophysiological and blood microcirculation signals from different scales, providing a basis for further exploring the generation mechanism of motor dysfunction and proposing collaborative rehabilitation intervention measures.

[0020] Further features and advantages of the present invention will become apparent from the following detailed description of exemplary embodiments of the present invention with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention.

[0022] FIG1 is a flow chart of a method for analyzing brain function coupling based on electrophysiological and blood microcirculation signals according to one embodiment of the present invention;

[0023] FIG2 is a schematic diagram of an implementation process of a method for analyzing brain function coupling based on electrophysiological and blood microcirculation signals according to an embodiment of the present invention.

[0024] FIG3 is a schematic diagram of a synchronous acquisition signal channel according to an embodiment of the present invention;

[0025] FIG4 is a schematic diagram of an original EEG signal according to an embodiment of the present invention;

[0026] FIG5 is a schematic diagram of a preprocessed EEG signal according to an embodiment of the present invention;

[0027] FIG6 is a schematic diagram of an original fNIRS signal according to one embodiment of the present invention;

[0028] FIG7 is a schematic diagram of a preprocessed fNIRS signal according to one embodiment of the present invention;

[0029] FIG8 is an EEG modality and corresponding spectrum diagram after fully adaptive noise set empirical mode decomposition according to one embodiment of the present invention;

[0030] FIG9 is a schematic diagram of a curve showing a change in symbolic transfer entropy from a brain oxygen signal to each modality of EEG signals according to an embodiment of the present invention;

[0031] FIG10 is a schematic diagram of a curve showing a change in symbolic transfer entropy from EEG signals of various modalities to brain oxygen signals according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that unless otherwise specifically stated, the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention.

[0033] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0034] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0035] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0036] It should be noted that like reference numerals and letters refer to like items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0037] This invention discloses a method for analyzing brain function coupling based on electrophysiological and blood microcirculatory signals. This method employs fully adaptive noise ensemble empirical mode decomposition (EMD)-symbolic transfer entropy to quantitatively analyze information transfer and coupling. Hereinafter, electrophysiological signals will be described using EEG signals as an example, while blood microcirculatory signals will be described using brain oxygenation signals as an example. EEG signals are the overall reflection of the electrophysiological activity of brain nerve tissue on the surface of the cerebral cortex. Brain oxygenation signals can reflect the oxygen content in the blood.

[0038] 1 and 2 , the provided brain function coupling analysis method based on electrophysiological and blood microcirculation signals includes the following steps:

[0039] Step S110 , synchronously collecting the subject's EEG signals and fNIRS signals under different motion states.

[0040] First, the operator connected the EEG device, fNIRS (functional near-infrared spectroscopy) equipment, and the synchronization box, among other hardware. The subjects followed the operator's instructions and completed different experimental tasks. During the experiment, EEG and fNIRS signals were synchronously collected, allowing the subjects to obtain EEG and fNIRS signals under different movement states. Figure 3 shows a schematic diagram of the synchronous signal acquisition channels.

[0041] For example, three healthy subjects were recruited, none of whom had motor dysfunction or neurological diseases. The EEG device used in the experiment was NeuroScan, with a sampling frequency of 1000Hz. The brain oxygen collection device was NirScan, with a sampling frequency of 17Hz. The subjects completed the upper limb handcycle resistance task, the lower limb bicycle resistance task, and the upper and lower limb linkage resistance task according to the operator's instructions. The resistance levels of 0, 5, and 10 were set for each task, and the duration of each task was 60s. To avoid fatigue of the subjects, they rested for 2 minutes after each set of tasks before starting the next set of experiments. During the experiment, the subjects' EEG signals and fNIRS signals were collected synchronously and stored in the computer as digital signals.

[0042] Step S120 , preprocessing the collected EEG signals and fNIRS signals to obtain preprocessed EEG signals and brain oxygen signals.

[0043] Specifically, the collected EEG signals were preprocessed, including baseline drift removal, downsampling, 0.1-50 Hz bandpass filtering using an FIR digital filter, removal of the 50 Hz power frequency signal, and independent component analysis (ICA) to remove oculoculographic and myoelectric interference components from the EEG signals. Finally, valid EEG signals were obtained, and EEG data from channels C3 and C4 were selected for subsequent coupling calculations. Figure 4 is a schematic diagram of the original EEG signal. Figure 5 is a schematic diagram of the preprocessed EEG signal.

[0044] The collected fNIRS signals were preprocessed, including converting the optical density signals into time series of three hemodynamic parameters using the Beer-Lambert law: oxygenated hemoglobin concentration, deoxygenated hemoglobin concentration, and total hemoglobin concentration, all of which are relative concentration changes. The filtered signals were filtered using a 0.01-0.2 Hz bandpass filter, and the filtered signals were segmented. The relative concentration of oxygenated hemoglobin was used to characterize the degree of cerebral cortical activation, which was then used to couple with the brain's electrical frequency band components to calculate the brain oxygen signal, which was then divided into the left and right motor cortex. The fNIRS signals were converted into changes in oxygenated hemoglobin concentration in the brain to represent the brain oxygen signal. Figure 6 is a schematic diagram of the original fNIRS signal. Figure 7 is a schematic diagram of the preprocessed fNIRS signal.

[0045] Step S130 , performing fully adaptive noise set empirical mode decomposition on the preprocessed EEG signal to decompose it into a plurality of different sub-band components.

[0046] For example, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm is used to decompose the EEG signal into sub-components of different frequency bands, and the first-order IMF (Intrinsic Mode Function) is calculated. Then, paired positive and negative Gaussian white noise is added to the original signal y(t), and the new signal is obtained as follows: y(t)+(-1) q εn i (t) (1)

[0047] Where t represents the time, q = 1 and 2, ε is the amplitude, n i (t) represents the added Gaussian white noise, and i is the number of times the Gaussian white noise is added.

[0048] Perform EMD (Empirical Mode Decomposition) on the new signal to obtain the first-order IMF component u i (t), expressed as:

[0049] Where E represents the empirical mode decomposition, Indicates the margin signal.

[0050] The overall average calculation of the N modal components generated is expressed as:

[0051] in, represents the average value of N modal components.

[0052] Then the margin signal is:

[0053] Perform EMD decomposition on the new signal to obtain the corresponding first-order IMF component u i (t), from which the second-order modal component can be obtained:

[0054] The residual signal is expressed as:

[0055] Wherein, r1(t) represents the residual signal after removing the first-order modal component, and r2(t) represents the residual signal after removing the second-order modal component.

[0056] Repeat the above operation until the residual signal is monotonic or has no more than two extreme points. The residual signal can no longer be decomposed and the algorithm ends. The intrinsic mode component obtained at this time is K, and the original signal can be expressed as:

[0057] Fully adaptive noise ensemble empirical mode decomposition (CEEMD) can effectively remove noise from signals, improving signal quality and accuracy. The output of CEEMDAN decomposition is subband components of varying frequencies and amplitudes, representing the essential characteristics of the original signal at different time scales and frequencies. Figure 8 shows the EEG modalities and corresponding spectrograms after fully adaptive noise ensemble empirical mode decomposition (CEEMD).

[0058] Step S140: Symbolize each sub-band component of the EEG signal and the brain oxygen signal time series, and convert them into symbolized observation values.

[0059] Specifically, for the decomposed EEG signal sub-band components, each sub-band component of the EEG signal and the brain oxygen signal time series are symbolized and expressed as:

[0060] Where S(i) is the symbolized sequence, x(i) is the original time series, delta is the increment, and its value is (max(x)-min(x)) / nums, min(x) represents the minimum value of x(i), max(x) represents the maximum value of x(i), and nums represents the symbolized observation value of the time series.

[0061] In this step, the EEG signal's subband components are symbolized to obtain a symbolized sequence of each subband component. The brain oxygen signal is also symbolized to obtain a symbolized sequence of the brain oxygen signal. Symbolizing a time series can capture large-scale features, and the symbolized sequence preserves more important dynamic characteristics.

[0062] Step S150 , for the symbolized sequences of the EEG signal sub-band components and the brain oxygen signal, the coupling relationship between each EEG signal sub-band component and the brain oxygen signal of the left and right brain regions is calculated using transfer entropy.

[0063] Specifically, based on the EEG signal subband components obtained by the above decomposition and the symbolic sequence of the brain oxygen signal, the coupling relationship between each EEG signal subband component and the brain oxygen signal of the left and right brain regions is calculated using transfer entropy, which is expressed as follows:

[0064] X and Y are two different time series. The transfer entropy of information transmitted from signal Y to signal X is defined as:

[0065] Among them, x i+1 represents the symbolic observation value of the delayed signal X, and Represent the symbolic observation values ​​of two time series, and are the conditional probabilities of X and Y respectively. Since both X and Y are physiological signals, k=l=1.

[0066] In this step, transfer entropy is calculated between each of the selected sub-band components and the symbolized sequence of the brain oxygen signal to observe the direction and strength of their coupling. Transfer entropy is a nonlinear, directional analysis method that inherently incorporates causal direction and dynamic strength. It can effectively quantify the nonlinear characteristics of physiological signals and can be used to study nonlinear information transfer between two time series. Due to its advantages of being model-free, nonlinear, and quantitative, transfer entropy is suitable for exploring nonlinear coupling characteristics between neurophysiological signals. Symbolizing the time series before calculating transfer entropy can avoid the computational complexity of extracting the transfer entropy space.

[0067] In summary, the embodiments of the present invention adopt the advantages of fully adaptive noise set empirical mode decomposition combined with symbolic transfer entropy, which is beneficial for processing electrophysiological and blood microcirculation signals, quantitatively describing the coupling characteristics of the characteristic frequency bands of EEG signals and brain oxygen signals under different motion states, and providing a new research direction for further exploring the coupling mechanism between electrophysiology and blood microcirculation.

[0068] Step S160: draw a symbolic transfer entropy change curve.

[0069] Based on the calculation results of step S150, a symbolic transfer entropy change curve can be further plotted to observe changes in symbolic transfer entropy under different motion states and scales, and statistical analysis can be performed to observe significant changes in symbolic transfer entropy values. Figure 9 is a schematic diagram of a symbolic transfer entropy change curve from the brain oxygen signal to each modal EEG signal. Figure 10 is a schematic diagram of a symbolic transfer entropy change curve from each modal EEG signal to the brain oxygen signal.

[0070] In summary, the fully adaptive noise ensemble empirical mode decomposition (EMD)-symbolic transfer entropy method proposed in this paper offers significant advantages for analyzing the coupling relationship between electrophysiological signals and blood microcirculatory signals. FADEM can address the problem of modal aliasing by introducing noise perturbations of varying scales and distributing the interference to different intrinsic mode functions during the decomposition process. This method supports the decomposition and analysis of nonlinear, nonstationary, and non-Gaussian signals, exhibits strong adaptability and universality, and can improve the accuracy and repeatability of decomposition results.

[0071] In summary, the present invention is based on electrophysiological and blood microcirculation signals, and explores the nonlinear coupling characteristics and information transmission of electrophysiological and blood microcirculation signals from different scales. The electrophysiological and blood microcirculation signals of the human body are synchronously collected under different motion states, and the frequency domain scale of the EEG signal is divided using a fully adaptive noise set empirical mode decomposition method. The symbolic transfer entropy parameter that can quantify nonlinear characteristics is used to analyze the coupling relationship between signals and explore the coupling mechanism. This method supports the decomposition and analysis of nonlinear, non-stationary and non-Gaussian signals, has strong adaptability and universality, and can improve the accuracy and repeatability of the decomposition results. Compared with the prior art, the present invention has the following advantages:

[0072] 1) The present invention uses a fully adaptive noise set empirical mode decomposition method to decompose the preprocessed EEG signal, and adds the intrinsic mode component containing auxiliary noise after EMD decomposition, instead of directly adding the Gaussian white noise signal to the original signal, thereby improving the universality of signal processing.

[0073] 2) This method uses fully adaptive noise ensemble empirical mode decomposition (EMD) to obtain EEG subband signals, divide the EEG signal into frequency bands, and symbolize the time series. When the first-order intrinsic mode components are obtained, the resulting modal components are averaged to obtain the final first-order intrinsic mode components. The decomposition steps are then repeated for the residual signal. This approach captures features at different scales, and the symbolized sequence preserves important dynamic characteristics.

[0074] 3) The present invention uses symbolic transfer entropy to quantify the information transmission between EEG signals and brain oxygen signals. Compared with traditional coherence methods, symbolic transfer entropy is a nonlinear and directional analysis method that contains causal direction and dynamic strength. It can be used to study the nonlinear information transmission between two time series and explore the nonlinear coupling characteristics of signals. It is very helpful for exploring the coupling mechanism of neurophysiological signals.

[0075] 4) After demonstration and preliminary experiments, the present invention can provide a basis for further exploring the mechanism of motor dysfunction and proposing collaborative rehabilitation intervention methods.

[0076] The present invention may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present invention.

[0077] Computer-readable storage medium can be a tangible device that can keep and store the instructions used by the instruction execution device.Computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device or any suitable combination thereof.More specific examples (non-exhaustive list) of computer-readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, for example, a punch card or a convex structure in a groove having instructions stored thereon, and any suitable combination thereof.Computer-readable storage medium used herein is not interpreted as a transient signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagated by waveguides or other transmission media (for example, light pulses by fiber optic cables), or electrical signals transmitted by wires.

[0078] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in the computer-readable storage medium in each computing / processing device.

[0079] The computer program instructions for performing the operation of the present invention can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, Python, and conventional procedural programming languages ​​such as "C" language or similar programming languages. The computer readable program instructions can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), is personalized by utilizing the state information of the computer readable program instructions, and the electronic circuit can execute the computer readable program instructions, thereby realizing various aspects of the present invention.

[0080] Various aspects of the present invention are described herein with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present invention. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0081] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device is generated that implements the functions / actions specified in one or more blocks in the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, where these instructions cause the computer, programmable data processing device, and / or other device to operate in a specific manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0082] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0083] The flowcharts and block diagrams in the accompanying drawings show the possible implementation architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of an instruction, and the module, program segment or part of the instruction contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, implementation by software, and implementation by a combination of software and hardware are all equivalent.

[0084] While various embodiments of the present invention have been described above, the foregoing description is intended to be illustrative, non-exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, their practical applications, or technological improvements in the marketplace, or to enable others skilled in the art to understand the embodiments disclosed herein. The scope of the present invention is defined by the appended claims.

Claims

1. A brain function coupling analysis method based on electrophysiological and blood microcirculation signals, comprising the following steps: Under different motion states, the EEG signals and fNIRS signals of the subjects were synchronously collected and preprocessed to obtain preprocessed EEG signals and brain oxygen signals; Performing fully adaptive noise set empirical mode decomposition on the EEG signal to obtain a plurality of different sub-band components; Symbolizing the multiple different sub-band components of the EEG signal and the time series of the brain oxygen signal to obtain symbolized sequences of the different sub-band components and the symbolized sequence of the brain oxygen signal; Calculating the transfer entropy between the symbolized sequences of the different sub-frequency band components and the symbolized sequences of the brain oxygen signal; The coupling degree between the EEG signal and the brain oxygen signal is analyzed based on the transfer entropy.

2. The method according to claim 1, characterized in that The different motion states include upper limb hand bike resistance task, lower limb bicycle resistance task and upper and lower limb coordinated resistance task.

3. The method according to claim 1, characterized in that: The EEG signal is preprocessed according to the following steps: The collected EEG signals were de-biased, downsampled, band-pass filtered using an FIR digital filter, and the 50 Hz power frequency signal was removed. Independent component analysis was used to remove the electrooculographic and myoelectric interference in the EEG signals to obtain effective EEG signals, and then the data of C3 and C4 channels were selected as the preprocessed EEG signals.

4. The method according to claim 1, characterized in that The brain oxygen signal is preprocessed according to the following steps: The collected fNIRS signals were preprocessed, including converting the optical density signals into time series of three hemodynamic parameters through Beer-Lambert's law: relative concentration of oxygenated hemoglobin, relative concentration of deoxygenated hemoglobin and relative concentration of total hemoglobin; filtering with a bandpass filter and segmenting the filtered signals; using the relative concentration of oxygenated hemoglobin to characterize the degree of cerebral cortical activation as a preprocessed brain oxygen signal, and dividing it into the left motor cortex and the right motor cortex.

5. The method according to claim 1, characterized in that The time series of the multiple different sub-band components of the EEG signal and the brain oxygen signal are symbolized according to the following formula: Where S(i) is the symbolized sequence, x(i) is the original time series, delta is the increment, and its value is (max(x)-min(x)) / nums, min(x) represents the minimum value of x(i), max(x) represents the maximum value of x(i), and nums represents the symbolized observation value of the time series.

6. The method according to claim 1, characterized in that The transfer entropy between the symbolized sequences of the different sub-band components and the symbolized sequences of the brain oxygen signal is calculated according to the following formula: Among them, X and Y are two different symbolized sequences, T Y→X It represents the transfer entropy of information transmitted from signal Y to signal X. and are the conditional probabilities of X and Y respectively.

7. The method according to claim 1, characterized in that Also includes: A symbolic transfer entropy change curve is drawn based on the calculated transfer entropy to observe the change of the symbolic transfer entropy under different motion states and different scales.

8. A computer-readable storage medium having a computer program stored thereon, wherein: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

9. A computer device comprising a memory and a processor, wherein a computer program capable of running on the processor is stored in the memory, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

Images