Real-time evaluation method and device for transcranial direct current intervention stimulation effect

Abstract

The invention discloses a transcranial direct current intervention stimulation effect real-time evaluation method, and particularly relates to the field of transcranial direct current effect evaluation, and the method comprises the steps: collecting a multi-channel cranial nerve electric signal of a set cerebral cortex region, calculating the complexity change of the multi-channel cranial nerve electric signal through multi-scale entropy, and building a complexity change time sequence before and after transcranial direct current intervention stimulation. Neural reflection signals of a set spinal cord segment are synchronously collected, a high-frequency energy density time sequence is extracted by using fast Fourier transform, brain-spinal cord cross-regional neural signal correlation is established, and the trend correlation degree of feature change is calculated. Cranial nerve electric signals and spinal cord reflection signals in a set frequency domain range are extracted and converted into instantaneous phase expression, the frequency locking ratio is calculated through the phase difference, and a phase synchronization degree time sequence is formed. And mapping the phase synchronization degree time sequence to a high-dimensional space, and comprehensively calculating a transcranial direct current intervention stimulation effect evaluation value by combining geometric characteristics of the phase synchronization degree and trend correlation degree.

Description

Technical Field

[0001] The present invention relates to the technical field of transcranial direct current effect evaluation, and more specifically, to a method and device for real-time evaluation of transcranial direct current intervention stimulation effect. Background Art

[0002] The synergy between the brain and the spinal cord is a key mechanism for the nervous system to regulate movement, sensation, and autonomic function. In scenarios such as motor function recovery, neuroregulation, and pain management, the functional connection between the cerebral cortex and the spinal cord reflex pathway directly determines the patient's functional state. However, neural signal transmission disorders caused by stroke, spinal cord injury, or neurodegenerative diseases often weaken or interrupt the brain's ability to regulate spinal cord signals. Transcranial direct current stimulation (tDCS), as a non-invasive neuroregulatory technology, indirectly affects the activity state of the spinal cord reflex pathway by regulating the excitability and inhibition of cerebral cortical neurons, and has gradually become a potential tool for enhancing brain-spinal cord synergy. For example, in patients with hemiplegia after stroke, tDCS stimulation of the motor cortex can enhance the signal transmission efficiency of the corticospinal tract, thereby improving the control of spinal reflexes. In chronic pain management, tDCS can relieve central sensitization by regulating the functional connection between the prefrontal cortex and the spinal cord reflex pathway, inhibiting abnormal reflex activity. However, current evaluation methods for the effects of tDCS intervention are mostly limited to the analysis of single signal features, which cannot fully reveal the dynamic changing characteristics of brain-spinal cord synergy.

[0003] Therefore, there is an urgent need for a multidimensional and dynamic evaluation method to capture the changes in neural signal characteristics across the brain-spinal cord region and to quantitatively analyze and evaluate the intervention effect. This will not only help improve the clinical application value of tDCS in neurorehabilitation and pain management, but also provide a scientific basis for further optimizing stimulation parameters.

[0004] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a real-time evaluation method for transcranial direct current intervention stimulation effect to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: S1: Collect multi-channel brain nerve electrical signals from the set cerebral cortex area, calculate the complexity changes of brain nerve signals through multi-scale entropy, and establish the complexity change time series before and after transcranial direct current intervention stimulation; S2: Collect the nerve reflex signals of the set spinal cord segments, use fast Fourier transform to extract the high-frequency energy density time series of the nerve reflex signals, synchronize and align them with the time series of the complexity change of the EEG signals, establish the cross-regional nerve signal feature association between the brain and spinal cord, and calculate the trend correlation of the changes in the nerve signal features between the brain and spinal cord; S3: extracting the cranial nerve electrical signals and spinal cord reflex signals within the set frequency domain range on the reference time axis, and converting the signals into instantaneous phase representation; S4: Calculate the frequency locking ratio by the phase difference between the cranial nerve electrical signal and the spinal cord reflex signal, and arrange the frequency locking ratios in chronological order to form a phase synchronization time series across the brain-spinal cord signal; S5: Map the phase synchronization time series of the cross-brain-spinal cord signal to the high-dimensional space to obtain the geometric characteristics of the high-dimensional phase space trajectory. Based on the geometric characteristics and trend correlation of the high-dimensional phase space trajectory, comprehensively calculate the evaluation value of the transcranial direct current intervention stimulation effect; S6: continuously adjust and set the transcranial direct current intervention stimulation parameters across the brain, select the maximum value of the transcranial direct current intervention stimulation effect evaluation value displayed by the display output device, and use the transcranial direct current intervention stimulation parameter combination corresponding to the maximum value as the final input parameter.

[0007] In a preferred embodiment, in S1, multi-channel brain nerve electrical signals of a set cerebral cortex area are collected, and the complexity change of the brain nerve signals is calculated by multi-scale entropy, and the complexity change time series before and after the transcranial direct current intervention stimulation is established, which specifically includes: Use a transcranial direct current generator to initiate continuous current output at a set site of the patient according to set transcranial direct current intervention stimulation parameters; An evenly distributed electrode array is arranged in a set cerebral cortex area, and a cerebral nerve electrical signal collector is used to collect multi-electrode channel cerebral nerve electrical signals reflecting cerebral cortical nerve activity at a set frequency to monitor the dynamic changes of cortical nerve activity; The bandpass filtering method is used to eliminate the low-frequency drift and environmental noise of the brain nerve electrical signal, and the independent component analysis method is used to separate the physiological artifact signal; A reference time axis is set, and the preprocessed brain nerve electrical signal time series data is synchronized to the reference time axis according to the acquisition timestamp. At the same time, the reference time axis is divided into time window segments of set lengths. A number of indefinite interval sampling scales are set for the brain nerve electrical signal sequence in each time window. The entropy value of the brain nerve electrical signal intensity at different scales is calculated by a recursive multi-scale entropy analysis method, and the complexity of the neural signal at different time scales is quantified by the entropy value. The calculation results of multi-scale entropy values ​​of brain nerve electrical signal intensity of all electrode channels were integrated, and the multi-scale entropy values ​​of different channels were weighted and summed using the weight distribution method to generate a time series describing the changes in the complexity of target cortical neural activity before and after transcranial direct current intervention stimulation; The time series of the complexity change of the cerebral cortical neural activity is calibrated on the reference time axis according to the timestamp of the starting time point of each time window.

[0008] In a preferred embodiment, in S2, the nerve reflex signal of the set spinal cord segment is collected, the high-frequency energy density time series of the nerve reflex signal is extracted by fast Fourier transform, and the time series of the complexity change of the electroencephalogram signal is synchronously aligned to establish the cross-regional nerve signal feature association of the brain-spinal cord, and the trend correlation of the change of the nerve signal feature of the brain-spinal cord is calculated, which specifically includes: The electrode array is evenly distributed at the set spinal cord segment position, and the spinal cord nerve reflex signal is recorded in real time through the spinal cord reflex signal collector. The dynamic nerve reflex signal of the spinal cord segment is captured through high-precision electrodes to generate continuous time series data; Apply bandpass filtering to the collected spinal nerve reflex signals to eliminate low-frequency baseline drift and high-frequency background noise, and synchronize the pre-processed nerve reflex signal time series data to the reference time axis according to the collection timestamp; The spinal nerve reflex signal is spectrally decomposed by fast Fourier transform, the time domain data is expressed as frequency domain energy distribution, and the energy contribution of each frequency band is separated according to the frequency range of the spinal reflex signal; Extracting the energy density of spinal nerve reflex signals above a preset frequency, and generating a high-frequency energy change time series of the spinal nerve reflex signals; The high-frequency energy change time series of the spinal nerve reflex signal is calibrated on the reference time axis according to the recorded timestamp information, and the high-frequency energy change time series and the EEG signal complexity change time series are aligned on the reference time axis in time dimension to establish the cross-regional neural signal association between the brain and spinal cord; The Spearman rank correlation coefficient was calculated based on the cross-regional neural signal correlation of the brain-spinal cord to indicate the trend correlation of the characteristic changes of the brain-spinal cord neural signal.

[0009] In a preferred embodiment, in S3, extracting the cranial nerve electrical signal and the spinal cord reflex signal within a set frequency domain range on the reference time axis, and converting the signal into an instantaneous phase representation specifically includes: Perform frequency domain conversion on the time series data of the cranial nerve electrical signals and spinal cord reflex signals collected in each time window on the reference time axis, and extract the time domain signal within the set frequency domain range; The extracted time domain signal is converted into an instantaneous phase representation using Hilbert transform to generate an instantaneous phase time series.

[0010] In a preferred embodiment, in S4, the frequency locking ratio is calculated by the phase difference between the cranial nerve electrical signal and the spinal cord reflex signal, and the frequency locking ratio is arranged in chronological order to form a phase synchronization time series across the brain-spinal cord signal, which specifically includes: Perform point-to-point differential operation on the instantaneous phase of the cranial nerve electrical signal and the spinal cord reflex signal in each time window, calculate the phase difference sequence, and standardize the phase difference value; In each time window on the reference time axis, the proportion of time points at which the phase difference between the EEG signal and the spinal cord signal falls within a set range of the phase difference is counted, and the proportion is used as a representative value of the locking ratio; The frequency locking ratio of each time window on the reference time axis is averaged to calculate the phase synchronization, and the phase synchronization time series is formed in chronological order.

[0011] In a preferred embodiment, in S5, the phase synchronization time series of the cross-brain-spinal cord signal is mapped to a high-dimensional space to obtain the geometric characteristics of the high-dimensional phase space trajectory. Based on the geometric characteristics and trend correlation of the high-dimensional phase space trajectory, the evaluation value of the transcranial direct current intervention stimulation effect is comprehensively calculated, which specifically includes: Mapping the synchronization value in each time window of the phase synchronization time series of the brain-spinal cord signal to a high-dimensional space, and constructing a high-dimensional phase space trajectory with the time series points as trajectory nodes; Analyze the geometric characteristics of trajectories in high-dimensional phase space, including the area and dispersion of trajectories and the average curvature of trajectories; The geometric characteristics of the trajectory in the high-dimensional phase space and the trend correlation of the changes in the brain-spinal cord nerve signal characteristics are comprehensively calculated to calculate the evaluation value of the transcranial direct current intervention stimulation effect. The calculation formula is: In the formula, is the evaluation value of the effect of transcranial direct current stimulation intervention, is the trend correlation of the characteristic changes of brain-spinal cord nerve signals, , , They are the average curvature of the trajectory, the discreteness of the trajectory, and the area of ​​the high-dimensional trajectory.

[0012] In a preferred embodiment, in S6, the transcranial direct current intervention stimulation parameters are set cross-brain-continuously, the maximum value of the transcranial direct current intervention stimulation effect evaluation value displayed by the display output device is selected, and the transcranial direct current intervention stimulation parameter combination corresponding to the maximum value is used as the final input parameter.

[0013] On the other hand, the present invention provides a real-time evaluation device for transcranial direct current intervention stimulation effect, comprising a transcranial direct current generator, a cranial nerve electrical signal collector, a spinal cord reflex signal collector, an operation processor, and a display output device: Transcranial direct current generator: Initiates continuous current output at the set site according to the set transcranial direct current intervention stimulation parameters; Brain neural electrical signal collector: internal integrated EEG unit records the neural electrical activity of the cerebral cortex; Spinal cord reflex signal collector: internal integrated electromyogram unit records the neural electrical activity of the cerebral cortex; Operation processor: used to analyze and process the signals collected by the cranial nerve electrical signal collector and the spinal cord reflex signal collector; Display output device: output to the computing processor.

[0014] The technical effects and advantages of the real-time evaluation method of transcranial direct current intervention stimulation effect of the present invention are as follows: By constructing a cross-regional dynamic feature analysis framework for brain neural electrical signals and spinal cord reflex signals, the regulatory effect of transcranial direct current stimulation (tDCS) on brain-spinal cord synergy is accurately quantified, significantly improving the evaluation accuracy and comprehensiveness of stimulation intervention. Multi-scale entropy is used to calculate the complexity changes of brain neural electrical signals, combined with the high-frequency energy density characteristics of spinal cord reflex signals, to capture the dynamic characteristics of neural signals before and after intervention. By constructing a time series of frequency locking ratio and phase synchronization, the signal transmission efficiency and synchronization mode across brain-spinal cord regions are revealed. Further based on the geometric characteristics of high-dimensional phase space, the overall trend of dynamic changes in signals is quantified, so that the evaluation of intervention effects is expanded from a single parameter to multi-dimensional correlation analysis, which can adapt to a variety of neural regulation scenarios, such as motor rehabilitation, chronic pain management, and neurological disease intervention, significantly improving the universality and scientificity of clinical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A schematic diagram of a method for real-time evaluation of transcranial direct current intervention stimulation effect according to the present invention; Figure 2 It is a structural schematic diagram of a device for real-time evaluation of transcranial direct current intervention stimulation effect of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] Example 1 Figure 1 The present invention provides a method for real-time evaluation of transcranial direct current intervention stimulation effect, which comprises the following steps: S1: Collect multi-channel brain nerve electrical signals from the set cerebral cortex area, calculate the complexity changes of brain nerve signals through multi-scale entropy, and establish the complexity change time series before and after transcranial direct current intervention stimulation; S2: Collect the nerve reflex signals of the set spinal cord segments, use fast Fourier transform to extract the high-frequency energy density time series of the nerve reflex signals, synchronize and align them with the time series of the complexity change of the EEG signals, establish the cross-regional nerve signal feature association between the brain and spinal cord, and calculate the trend correlation of the changes in the nerve signal features between the brain and spinal cord; S3: extracting the cranial nerve electrical signals and spinal cord reflex signals within the set frequency domain range on the reference time axis, and converting the signals into instantaneous phase representation; S4: Calculate the frequency locking ratio by the phase difference between the cranial nerve electrical signal and the spinal cord reflex signal, and arrange the frequency locking ratios in chronological order to form a phase synchronization time series across the brain-spinal cord signal; S5: Map the phase synchronization time series of the cross-brain-spinal cord signal to the high-dimensional space to obtain the geometric characteristics of the high-dimensional phase space trajectory. Based on the geometric characteristics and trend correlation of the high-dimensional phase space trajectory, comprehensively calculate the evaluation value of the transcranial direct current intervention stimulation effect; S6: continuously adjust and set the transcranial direct current intervention stimulation parameters across the brain, select the maximum value of the transcranial direct current intervention stimulation effect evaluation value displayed by the display output device, and use the transcranial direct current intervention stimulation parameter combination corresponding to the maximum value as the final input parameter.

[0018] In S1, multi-channel brain nerve electrical signals of the set cerebral cortical area are collected, and the complexity changes of brain nerve signals are calculated by multi-scale entropy to establish the complexity change time series before and after transcranial direct current intervention stimulation.

[0019] According to the intervention plan, the transcranial direct current intervention parameters are set, including current intensity (such as 1-2 mA), stimulation duration (such as 20 minutes) and electrode polarity (anode and cathode positions). The transcranial direct current generator outputs continuous direct current and applies the stimulation signal to the patient's set part, such as the motor cortex, prefrontal cortex, etc.

[0020] Through high-density EEG equipment, evenly distributed electrode arrays are arranged in the set cerebral cortical area (such as frontal lobe, parietal lobe, etc.). The number of electrodes is 64 or 128. A multi-channel acquisition method is adopted to record multi-electrode channel brain neural electrical signals reflecting cortical neural activity at a sampling frequency of 500Hz, and the dynamic changes of neural activity are monitored in real time.

[0021] The collected brain neural electrical signals were filtered using a bandpass filter with the filter range set to 0.5 Hz to 50 Hz to remove low-frequency drift (such as baseline fluctuations) and environmental noise (such as electromagnetic interference). Subsequently, the independent component analysis (ICA) method was used to separate physiological artifact signals (such as eye movement and heartbeat artifacts), and the target neural signals were retained after removing the artifacts.

[0022] Set the reference time axis as a unified reference, synchronize the preprocessed brain neural electrical signal time series with the reference time axis according to the acquisition timestamp, and divide the reference time axis into time windows of fixed length (such as 500 milliseconds per segment) to ensure that subsequent analysis has consistent time resolution.

[0023] For the brain nerve electrical signal sequence in each time window, several indefinite interval sampling scales are set, and the entropy value of the brain nerve electrical signal intensity at different scales is calculated by recursive multi-scale entropy analysis method. The specific process of quantifying the complexity of neural signals at different time scales by entropy value is as follows: Given a sequence of brain nerve electrical signals , is the brain nerve electrical signal of the Nth sequence subscript in the sequence, and the time window length is , the kth time window signal is: .

[0024] The signal of the kth time window is averaged piecewise to construct different time scales The coarse-grained signal under the condition generates a coarse-grained signal sequence : For coarse-grained signal Construct an embedding vector of length m And define the distance matching: Set the similarity tolerance (usually 15%-25% of the signal standard deviation) and statistically satisfy The number of matching pairs that are less than or equal to the set similarity tolerance and , corresponding to the embedding dimensions m and m+1 respectively.

[0025] The sample entropy is calculated by the sample entropy formula, which is: In the formula, represents the sample entropy value when the embedding dimension is m and the similarity tolerance is r, as the time scale The scale entropy value under .

[0026] The multi-scale entropy values ​​of the brain nerve electrical signal intensity of all electrode channels are summarized, and the entropy values ​​of different channels are weighted by weighted average method (such as assigning weights according to the importance of electrode positions). Through weighted summation, a time series describing the changes in the complexity of the target cerebral cortex neural activity before and after transcranial direct current intervention is generated.

[0027] The generated complexity change time series is calibrated according to the starting time point of each time window and the timestamp position of the reference time axis, so as to align and analyze with the stimulus event. The calibrated time series is output for further dynamic characteristic evaluation.

[0028] In S2, the neural reflex signals of the set spinal cord segments are collected, and the high-frequency energy density time series of the neural reflex signals is extracted using fast Fourier transform. This is synchronized with the time series of the complexity change of the EEG signal to establish a cross-regional neural signal feature association between the brain and spinal cord, and calculate the trend correlation of the changes in the brain-spinal cord neural signal features.

[0029] A high-precision electrode array is evenly distributed in the set spinal cord segment (such as the cervical segment, thoracic segment or lumbar segment), and each electrode is evenly spaced to ensure coverage of the target area. The spinal cord reflex signal collector records the nerve reflex signal in real time at a high sampling rate (such as 5000Hz), captures the dynamic nerve reflex activity of the spinal cord segment, and generates continuous time series data.

[0030] Bandpass filtering is applied to the collected spinal nerve reflex signals, and the filter range is set to 30Hz to 500Hz to exclude low-frequency baseline drift (such as motion artifacts below 30Hz) and high-frequency background noise (such as electromagnetic interference above 500Hz). The time series data of the nerve reflex signal generated after preprocessing is synchronized to the reference time axis according to the acquisition timestamp to ensure time consistency with other signals.

[0031] Fast Fourier transform (FFT) is used to convert the preprocessed spinal nerve reflex signal from the time domain to the frequency domain to generate a spectrum energy distribution diagram. The specific frequency range of the spinal cord reflex signal is analyzed in the spectrum, the energy contribution of each frequency band is separated, and the frequency information related to neural activity is retained.

[0032] The energy density of the spinal nerve reflex signal is calculated within the preset frequency range to generate a high-frequency energy density sequence. The dynamic changes of high-frequency energy density over time are tracked to construct a high-frequency energy change time series.

[0033] The high-frequency energy change time series of the spinal nerve reflex signal is calibrated on the reference time axis according to the recorded timestamp information, and the high-frequency energy change time series and the EEG signal complexity change time series are aligned in time dimension on the reference time axis to establish a cross-regional brain-spinal cord neural signal association.

[0034] The Spearman rank correlation coefficient is calculated based on the cross-regional neural signal correlation between the brain and spinal cord, which is used to indicate the trend correlation of the characteristic changes of the brain-spinal cord neural signal. The formula of the Spearman rank correlation coefficient is: In the formula, The Spearman rank correlation coefficient reflects the trend correlation of changes in brain-spinal cord nerve signal characteristics, ranging from [-1, 1]. is the total number of time points in the time series, is the sequence rank difference at the i-th time point in the time series.

[0035] In S3, the brain nerve electrical signals and spinal cord reflex signals within the set frequency domain range on the reference time axis are extracted, and the signals are converted into instantaneous phase representation.

[0036] On the reference time axis, the time series data of the brain nerve electrical signals and spinal cord reflex signals collected in each time window are subjected to fast Fourier transform (FFT) to convert the time domain signals into frequency domain representation. In the spectrum diagram, the frequency domain signals of the target frequency band are extracted according to the set frequency range (such as 8Hz to 30Hz for brain signals and 100Hz to 300Hz for spinal cord signals). Subsequently, the frequency domain signals of the target frequency band are restored to time domain signals through inverse Fourier transform to generate a time series related to the set frequency band.

[0037] The Hilbert transform method is used to convert the extracted target frequency band time domain signal into a complex form, using the formula: In the formula, is the complex form of the target signal, which is used to contain amplitude and phase information. is a time domain signal, which represents the signal time series after frequency domain conversion and target frequency band extraction. is the imaginary signal calculated by Hilbert transform, which is expressed as Complementary 90 degree phase shifted signals, Is an imaginary unit.

[0038] In S4, the instantaneous phase is further calculated, and the frequency locking ratio is calculated by the phase difference between the cranial nerve electrical signal and the spinal cord reflex signal. The frequency locking ratios are arranged in chronological order to form a phase synchronization time series across the brain-spinal cord signal: In the formula, is the instantaneous phase, which represents the phase angle of the signal at time t. Plural form of signal The imaginary part of comes from the Hilbert transform, Plural form of signal The real part of comes from the original time domain signal.

[0039] Perform point-to-point differential operation on the instantaneous phase of the cranial nerve electrical signal and the spinal cord reflex signal in each time window, and calculate the phase difference sequence. The calculation expression is: In the formula, is the phase difference, , are the instantaneous phases of the cranial nerve electrical signal and the spinal cord reflex signal at time t, and the phase difference is normalized to Within the range, The phase difference between the EEG signal and the spinal cord signal in each time window on the reference time axis falls within the set range (e.g. ) and takes this ratio as the characterization value of the locking ratio.

[0040] The frequency locking ratio of each time window on the reference time axis is averaged to calculate the phase synchronization, and the phase synchronization time series is formed in chronological order.

[0041] In S5, the phase synchronization time series of the cross-brain-spinal cord signal is mapped to the high-dimensional space to obtain the geometric characteristics of the high-dimensional phase space trajectory. Based on the geometric characteristics and trend correlation of the high-dimensional phase space trajectory, the evaluation value of the transcranial direct current intervention stimulation effect is comprehensively calculated, including: Mapping the synchronization value in each time window of the phase synchronization time series of the brain-spinal cord signal to a high-dimensional space, and constructing a high-dimensional phase space trajectory with the time series points as trajectory nodes; Analyze the geometric characteristics of trajectories in high-dimensional phase space, including the area and dispersion of trajectories and the average curvature of trajectories; The geometric characteristics of the trajectory in the high-dimensional phase space and the trend correlation of the changes in the brain-spinal cord nerve signal characteristics are comprehensively calculated to calculate the evaluation value of the transcranial direct current intervention stimulation effect. The calculation formula is: In the formula, is the evaluation value of the effect of transcranial direct current stimulation intervention, is the trend correlation of the characteristic changes of brain-spinal cord nerve signals, , , They are the average curvature of the trajectory, the discreteness of the trajectory, and the area of ​​the high-dimensional trajectory.

[0042] In S6, the transcranial direct current intervention stimulation parameters are adjusted and set across the brain continuously, the maximum value of the transcranial direct current intervention stimulation effect evaluation value displayed by the display output device is selected, and the transcranial direct current intervention stimulation parameter combination corresponding to the maximum value is used as the final input parameter.

[0043] Example 2 The difference between Example 2 of the present invention and Example 1 is that this example introduces a method for real-time evaluation of transcranial direct current intervention stimulation effect.

[0044] Figure 2 A structural schematic diagram of a method for real-time evaluation of transcranial direct current intervention stimulation effect of the present invention is given, and a method for real-time evaluation of transcranial direct current intervention stimulation effect comprises a transcranial direct current generator, a cranial nerve electrical signal collector, a spinal cord reflex signal collector, an operation processor, and a display output device: Transcranial direct current generator: Initiates continuous current output at the set site according to the set transcranial direct current intervention stimulation parameters; Brain neural electrical signal collector: internal integrated EEG unit records the neural electrical activity of the cerebral cortex; Spinal cord reflex signal collector: internal integrated electromyogram unit records the neural electrical activity of the cerebral cortex; Operation processor: used to analyze and process the signals collected by the cranial nerve electrical signal collector and the spinal cord reflex signal collector; Display output device: output to the computing processor.

[0045] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0046] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or may be transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0047] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0048] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0049] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0050] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0051] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0052] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0053] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0054] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for real-time evaluation of transcranial direct current intervention stimulation effect, characterized in that: The steps include: S1: Collect multi-channel brain nerve electrical signals from the set cerebral cortex area, calculate the complexity changes of brain nerve signals through multi-scale entropy, and establish the complexity change time series before and after transcranial direct current intervention stimulation; S2: Collect the nerve reflex signals of the set spinal cord segments, use fast Fourier transform to extract the high-frequency energy density time series of the nerve reflex signals, synchronize and align them with the time series of the complexity change of the EEG signals, establish the cross-regional nerve signal feature association between the brain and spinal cord, and calculate the trend correlation of the changes in the nerve signal features between the brain and spinal cord; S3: extracting the cranial nerve electrical signals and spinal cord reflex signals within the set frequency domain range on the reference time axis, and converting the signals into instantaneous phase representation; S4: Calculate the frequency locking ratio by the phase difference between the cranial nerve electrical signal and the spinal cord reflex signal, and arrange the frequency locking ratios in chronological order to form a phase synchronization time series across the brain-spinal cord signal; S5: Map the phase synchronization time series of the cross-brain-spinal cord signal to the high-dimensional space to obtain the geometric characteristics of the high-dimensional phase space trajectory. Based on the geometric characteristics and trend correlation of the high-dimensional phase space trajectory, comprehensively calculate the evaluation value of the transcranial direct current intervention stimulation effect; S6: continuously adjust and set the transcranial direct current intervention stimulation parameters across the brain, select the maximum value of the transcranial direct current intervention stimulation effect evaluation value displayed by the display output device, and use the transcranial direct current intervention stimulation parameter combination corresponding to the maximum value as the final input parameter.

2. A method for real-time evaluation of transcranial direct current stimulation effect according to claim 1, characterized in that: In S1, multi-channel brain nerve electrical signals of the set cerebral cortex area are collected, and the complexity changes of brain nerve signals are calculated by multi-scale entropy to establish the complexity change time series before and after transcranial direct current intervention stimulation, which specifically includes: Use a transcranial direct current generator to initiate continuous current output at a set site of the patient according to set transcranial direct current intervention stimulation parameters; An evenly distributed electrode array is arranged in a set cerebral cortex area, and a cerebral nerve electrical signal collector is used to collect multi-electrode channel cerebral nerve electrical signals reflecting cerebral cortical nerve activity at a set frequency to monitor the dynamic changes of cortical nerve activity; The bandpass filtering method is used to eliminate the low-frequency drift and environmental noise of the brain nerve electrical signal, and the independent component analysis method is used to separate the physiological artifact signal; A reference time axis is set, and the preprocessed brain nerve electrical signal time series data is synchronized to the reference time axis according to the acquisition timestamp. At the same time, the reference time axis is divided into time window segments of set lengths. A number of indefinite interval sampling scales are set for the brain nerve electrical signal sequence in each time window. The entropy value of the brain nerve electrical signal intensity at different scales is calculated by a recursive multi-scale entropy analysis method, and the complexity of the neural signal at different time scales is quantified by the entropy value. The calculation results of multi-scale entropy values ​​of brain nerve electrical signal intensity of all electrode channels were integrated, and the multi-scale entropy values ​​of different channels were weighted and summed using the weight distribution method to generate a time series describing the changes in the complexity of target cortical neural activity before and after transcranial direct current intervention stimulation; The time series of the complexity change of the cerebral cortical neural activity is calibrated on the reference time axis according to the timestamp of the starting time point of each time window.

3. A method for real-time evaluation of transcranial direct current stimulation effect according to claim 2, characterized in that: In S2, the nerve reflex signals of the set spinal cord segments are collected, and the high-frequency energy density time series of the nerve reflex signals is extracted by fast Fourier transform, which is synchronized with the time series of the complexity change of the EEG signal to establish the cross-regional nerve signal feature association between the brain and spinal cord, and calculate the trend correlation of the change of the nerve signal feature between the brain and spinal cord. Specifically, it includes: The electrode array is evenly distributed at the set spinal cord segment position, and the spinal cord nerve reflex signal is recorded in real time through the spinal cord reflex signal collector. The dynamic nerve reflex signal of the spinal cord segment is captured through high-precision electrodes to generate continuous time series data; Apply bandpass filtering to the collected spinal nerve reflex signals to eliminate low-frequency baseline drift and high-frequency background noise, and synchronize the pre-processed nerve reflex signal time series data to the reference time axis according to the collection timestamp; The spinal nerve reflex signal is spectrally decomposed by fast Fourier transform, the time domain data is expressed as frequency domain energy distribution, and the energy contribution of each frequency band is separated according to the frequency range of the spinal reflex signal; Extracting the energy density of spinal nerve reflex signals above a preset frequency, and generating a high-frequency energy change time series of the spinal nerve reflex signals; The high-frequency energy change time series of the spinal nerve reflex signal is calibrated on the reference time axis according to the recorded timestamp information, and the high-frequency energy change time series and the EEG signal complexity change time series are aligned on the reference time axis in time dimension to establish the cross-regional neural signal association between the brain and spinal cord; The Spearman rank correlation coefficient was calculated based on the cross-regional neural signal correlation of the brain-spinal cord to indicate the trend correlation of the characteristic changes of the brain-spinal cord neural signal.

4. A method for real-time evaluation of transcranial direct current stimulation effect according to claim 3, characterized in that: In S3, the brain nerve electrical signals and spinal cord reflex signals in the set frequency domain range on the reference time axis are extracted, and the signals are converted into instantaneous phase representation, which specifically includes: Perform frequency domain conversion on the time series data of the cranial nerve electrical signals and spinal cord reflex signals collected in each time window on the reference time axis, and extract the time domain signal within the set frequency domain range; The extracted time domain signal is converted into an instantaneous phase representation using Hilbert transform to generate an instantaneous phase time series.

5. A method for real-time evaluation of transcranial direct current intervention stimulation effect according to claim 4, characterized in that: In S4, the frequency locking ratio is calculated by the phase difference between the cranial nerve electrical signal and the spinal cord reflex signal, and the frequency locking ratio is arranged in time sequence to form a phase synchronization time series across the brain-spinal cord signal, which specifically includes: Perform point-to-point differential operation on the instantaneous phase of the cranial nerve electrical signal and the spinal cord reflex signal in each time window, calculate the phase difference sequence, and standardize the phase difference value; In each time window on the reference time axis, the proportion of time points at which the phase difference between the EEG signal and the spinal cord signal falls within a set range of the phase difference is counted, and the proportion is used as a representative value of the locking ratio; The frequency locking ratio of each time window on the reference time axis is averaged to calculate the phase synchronization, and the phase synchronization time series is formed in chronological order.

6. A method for real-time evaluation of transcranial direct current stimulation effect according to claim 5, characterized in that: In S5, the phase synchronization time series of the cross-brain-spinal cord signal is mapped to the high-dimensional space to obtain the geometric characteristics of the high-dimensional phase space trajectory. Based on the geometric characteristics and trend correlation of the high-dimensional phase space trajectory, the evaluation value of the transcranial direct current intervention stimulation effect is comprehensively calculated, including: Mapping the synchronization value in each time window of the phase synchronization time series of the brain-spinal cord signal to a high-dimensional space, and constructing a high-dimensional phase space trajectory with the time series points as trajectory nodes; Analyze the geometric characteristics of trajectories in high-dimensional phase space, including the area and dispersion of trajectories and the average curvature of trajectories; The geometric characteristics of the trajectory in the high-dimensional phase space and the trend correlation of the changes in the brain-spinal cord nerve signal characteristics are comprehensively calculated to calculate the evaluation value of the transcranial direct current intervention stimulation effect. The calculation formula is: In the formula, is the evaluation value of the effect of transcranial direct current stimulation intervention, is the trend correlation of the characteristic changes of brain-spinal cord nerve signals, , , They are the average curvature of the trajectory, the discreteness of the trajectory, and the area of ​​the high-dimensional trajectory.

7. A method for real-time evaluation of transcranial direct current stimulation effect according to claim 6, characterized in that: In S6, the transcranial direct current intervention stimulation parameters are adjusted and set across the brain continuously, the maximum value of the transcranial direct current intervention stimulation effect evaluation value displayed by the display output device is selected, and the transcranial direct current intervention stimulation parameter combination corresponding to the maximum value is used as the final input parameter.

8. A device for real-time evaluation of transcranial direct current stimulation effect, used to implement a method for real-time evaluation of transcranial direct current stimulation effect according to any one of claims 1 to 7, characterized in that: Including transcranial direct current generator, cranial nerve electrical signal collector, spinal cord reflex signal collector, operation processor, display output device: Transcranial direct current generator: Initiates continuous current output at the set site according to the set transcranial direct current intervention stimulation parameters; Brain neural electrical signal collector: internal integrated EEG unit records the neural electrical activity of the cerebral cortex; Spinal cord reflex signal collector: internal integrated electromyogram unit records the neural electrical activity of the cerebral cortex; Operation processor: used to analyze and process the signals collected by the cranial nerve electrical signal collector and the spinal cord reflex signal collector; Display output device: output to the computing processor.

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