A real-time evaluation method and device for transcranial direct current stimulation effect

Abstract

The present invention discloses a real-time evaluation method for the effect of transcranial direct current intervention stimulation, which specifically relates to the field of transcranial direct current effect evaluation, including: collecting multi-channel brain nerve electrical signals from a set cerebral cortex area, calculating its complexity change using multi-scale entropy, and establishing a complexity change time series before and after transcranial direct current intervention stimulation. Synchronously collecting nerve reflex signals from a set spinal cord segment, extracting high-frequency energy density time series using fast Fourier transform, establishing brain-spinal cord cross-regional neural signal association, and calculating the trend correlation of feature changes. Extracting brain nerve electrical signals and spinal cord reflex signals within a set frequency domain range, converting them into instantaneous phase expressions, calculating the frequency locking ratio through phase difference, and forming a phase synchronization time series. Mapping the phase synchronization time series to a high-dimensional space, combining the geometric characteristics of phase synchronization and trend correlation, and comprehensively calculating the evaluation value of the transcranial direct current intervention stimulation effect.

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 effects. Background Art

[0002] Synergy between the brain and spinal cord is a key mechanism by which the nervous system regulates movement, sensation, and autonomic function. In scenarios such as motor recovery, neuromodulation, and pain management, the functional connectivity between the cerebral cortex and spinal reflex pathways directly determines the patient's functional status. However, impaired neural signaling due to stroke, spinal cord injury, or neurodegenerative diseases often weakens or disrupts the brain's ability to regulate spinal cord signals. Transcranial direct current stimulation (tDCS), a non-invasive neuromodulation technique, indirectly influences the activity of spinal reflex pathways by modulating the excitability and inhibition of cerebral cortical neurons, and is emerging as a potential tool for enhancing brain-spinal synergy. For example, in patients with post-stroke hemiplegia, tDCS stimulation of the motor cortex can enhance the efficiency of signal transmission in the corticospinal tract, thereby improving control of spinal reflexes. In chronic pain management, tDCS can alleviate central sensitization by modulating the functional connectivity between the prefrontal cortex and spinal reflex pathways, thereby inhibiting abnormal reflex activity. However, current evaluation methods for the effectiveness 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, a multidimensional, dynamic assessment method is urgently needed to capture changes in neural signal characteristics across brain-spinal regions and to quantitatively analyze and evaluate the effects of interventions. 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 optimization of 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:

[0007] S1: Collect multi-channel brain nerve electrical signals from a set cerebral cortical area, calculate the complexity changes of brain nerve signals through multi-scale entropy, and establish a time series of complexity changes before and after transcranial direct current intervention stimulation;

[0008] S2: Collect nerve reflex signals from the specified spinal cord segments, extract the high-frequency energy density time series of the nerve reflex signals using fast Fourier transform, align them with the time series of EEG signal complexity changes, establish cross-regional brain-spinal cord nerve signal feature correlations, and calculate the trend correlation of brain-spinal cord nerve signal feature changes;

[0009] S3: Extracting brain nerve electrical signals and spinal cord reflex signals within a set frequency range on the reference time axis and converting the signals into instantaneous phase representation;

[0010] S4: Calculate the frequency locking ratio by the phase difference between the brain nerve electrical signal and the spinal cord reflex signal, and arrange the frequency locking ratios in chronological order to form a time series of phase synchronization across the brain-spinal cord signal;

[0011] S5: Map the phase synchronization time series of cross-brain-spinal cord signals 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, comprehensively calculate the evaluation value of the transcranial direct current intervention stimulation effect;

[0012] 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 output device, and use the transcranial direct current intervention stimulation parameter combination corresponding to the maximum value as the final input parameter.

[0013] In a preferred embodiment, in S1, multi-channel brain nerve electrical signals of a set cerebral cortical area are collected, and the complexity change of the brain nerve signals is calculated by multi-scale entropy. The time series of complexity change before and after transcranial direct current intervention stimulation is established, which specifically includes:

[0014] Use a transcranial direct current generator to initiate continuous current output at the patient's set site according to the set transcranial direct current intervention stimulation parameters;

[0015] An evenly distributed electrode array is arranged in a set cerebral cortical area, and a cerebral neural signal collector is used to collect multi-electrode channel cerebral neural signals reflecting cerebral cortical neural activity at a set frequency to monitor the dynamic changes of cerebral cortical neural activity;

[0016] Bandpass filtering is used to eliminate low-frequency drift and environmental noise in brain neural electrical signals, and independent component analysis is used to separate physiological artifact signals.

[0017] Set a reference time axis, synchronize the preprocessed brain neural electrical signal time series data to the reference time axis according to the acquisition timestamp, and divide the reference time axis into time windows of set lengths. Set several indefinite interval sampling scales for the brain neural electrical signal sequence in each time window, calculate the entropy value of the brain neural electrical signal intensity at different scales through a recursive multi-scale entropy analysis method, and quantify the complexity of neural signals at different time scales through the entropy value;

[0018] The multi-scale entropy calculation results of the 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 a weight distribution method to generate a time series describing the changes in the complexity of the target cerebral cortical neural activity before and after transcranial direct current intervention stimulation;

[0019] 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.

[0020] In a preferred embodiment, in S2, the nerve reflex signals of the set spinal cord segments are collected, the high-frequency energy density time series of the nerve reflex signals are extracted using fast Fourier transform, and the high-frequency energy density time series of the nerve reflex signals are synchronously aligned with the time series of the complexity change of the EEG signal to establish a cross-regional brain-spinal cord neural signal feature association. The trend correlation of the brain-spinal cord neural signal feature change is calculated, which specifically includes:

[0021] The electrode array is evenly distributed at the set spinal cord segment position, and the spinal cord reflex signal collector records the spinal cord nerve reflex signal in real time. The high-precision electrodes capture the dynamic nerve reflex signal of the spinal cord segment to generate continuous time series data.

[0022] Apply bandpass filtering to the collected spinal nerve reflex signals to eliminate low-frequency baseline drift and high-frequency background noise, and synchronize the preprocessed nerve reflex signal time series data to the reference time axis according to the acquisition timestamp;

[0023] The spinal cord reflex signal is decomposed into a spectrum by fast Fourier transform, and the time domain data is expressed as a frequency domain energy distribution. According to the frequency range of the spinal cord reflex signal, the energy contribution of each frequency band is separated.

[0024] Extracting the energy density of spinal nerve reflex signals above a preset frequency to generate a high-frequency energy change time series of the spinal nerve reflex signals;

[0025] The high-frequency energy change time series of the spinal cord nerve reflex signal is calibrated on the reference time axis according to the recorded timestamp information. The high-frequency energy change time series and the EEG signal complexity change time series are aligned on the reference time axis to establish a cross-regional brain-spinal cord neural signal association.

[0026] The Spearman rank correlation coefficient was calculated based on the cross-regional neural signal correlation of the brain and spinal cord to indicate the trend correlation of the changes in brain and spinal cord neural signal characteristics.

[0027] In a preferred embodiment, in S3, extracting the brain 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:

[0028] 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 to extract the time domain signal within the set frequency domain range;

[0029] The extracted time domain signal is converted into instantaneous phase representation using Hilbert transform to generate instantaneous phase time series.

[0030] In a preferred embodiment, in S4, the frequency locking ratio is calculated by the phase difference between the brain nerve electrical signal and the spinal cord reflex signal, and the frequency locking ratios are arranged in chronological order to form a phase synchronization time series across the brain-spinal cord signal, specifically including:

[0031] Perform point-to-point differential operations on the instantaneous phases of the cranial nerve electrical signals and spinal cord reflex signals in each time window, calculate the phase difference sequence, and standardize the phase difference values;

[0032] 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 is counted, and the proportion is used as a representative value of the locking ratio;

[0033] The frequency locking ratio of each time window on the reference time axis is averaged to calculate the phase synchronization, and the phase synchronization is arranged in chronological order to form a phase synchronization time series across the brain-spinal cord signal.

[0034] 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, specifically including:

[0035] Mapping the synchronization value in each time window of the phase synchronization time series of brain-spinal cord signals to a high-dimensional space, and constructing a high-dimensional phase space trajectory with time series points as trajectory nodes;

[0036] Analyze the geometric characteristics of trajectories in high-dimensional phase space, including the area and dispersion of the trajectory and the average curvature of the trajectory;

[0037] 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:

[0038]

[0039] Where, is the evaluation value of the effect of transcranial direct current stimulation intervention, is the trend correlation of changes in brain-spinal cord nerve signal characteristics, 、 、 They are the average curvature of the trajectory, the discreteness of the trajectory, and the area of the high-dimensional trajectory.

[0040] In a preferred embodiment, in S6, the transcranial direct current intervention stimulation parameters are 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.

[0041] 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:

[0042] Transcranial direct current generator: Initiates continuous current output at the set location according to the set transcranial direct current intervention stimulation parameters;

[0043] Brain neural electrical signal collector: internally integrated EEG unit records the neural electrical activity of the cerebral cortex;

[0044] Spinal cord reflex signal collector: internal integrated electromyography unit records the neural electrical activity of the cerebral cortex;

[0045] Arithmetic processor: used to analyze and process the signals collected by the brain nerve electrical signal collector and the spinal cord reflex signal collector;

[0046] Display output device: outputs to the computing processor.

[0047] The technical effects and advantages of the real-time evaluation method for transcranial direct current stimulation intervention of the present invention are as follows:

[0048] 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 accuracy and comprehensiveness of stimulation intervention assessments. 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 ratios and phase synchronization, the signal transmission efficiency and synchronization pattern across brain-spinal cord regions are revealed. Further, based on the geometric characteristics of high-dimensional phase space, the overall trend of signal dynamic changes is quantified, allowing the evaluation of intervention effects to be expanded from a single parameter to multi-dimensional correlation analysis. It can adapt to a variety of neural regulation scenarios, such as sports rehabilitation, chronic pain management, and neurological disease intervention, significantly improving the universality and scientific nature of clinical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A schematic diagram of a method for real-time evaluation of transcranial direct current stimulation effects according to the present invention;

[0050] Figure 2 This is a structural schematic diagram of a device for real-time evaluation of transcranial direct current intervention stimulation effects according to the present invention. DETAILED DESCRIPTION

[0051] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0052] Example 1

[0053] Figure 1 The present invention provides a method for real-time evaluation of transcranial direct current stimulation effect, which comprises the following steps:

[0054] S1: Collect multi-channel brain nerve electrical signals from a set cerebral cortical area, calculate the complexity changes of brain nerve signals through multi-scale entropy, and establish a time series of complexity changes before and after transcranial direct current intervention stimulation;

[0055] S2: Collect nerve reflex signals from the specified spinal cord segments, extract the high-frequency energy density time series of the nerve reflex signals using fast Fourier transform, align them with the time series of EEG signal complexity changes, establish cross-regional brain-spinal cord nerve signal feature correlations, and calculate the trend correlation of brain-spinal cord nerve signal feature changes;

[0056] S3: Extracting brain nerve electrical signals and spinal cord reflex signals within a set frequency range on the reference time axis and converting the signals into instantaneous phase representation;

[0057] S4: Calculate the frequency locking ratio by the phase difference between the brain nerve electrical signal and the spinal cord reflex signal, and arrange the frequency locking ratios in chronological order to form a time series of phase synchronization across the brain-spinal cord signal;

[0058] S5: Map the phase synchronization time series of cross-brain-spinal cord signals 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, comprehensively calculate the evaluation value of the transcranial direct current intervention stimulation effect;

[0059] 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 output device, and use the transcranial direct current intervention stimulation parameter combination corresponding to the maximum value as the final input parameter.

[0060] In S1, multi-channel brain nerve electrical signals are collected from the set cerebral cortical area, and the complexity changes of brain nerve signals are calculated through multi-scale entropy to establish a time series of complexity changes before and after transcranial direct current intervention stimulation.

[0061] 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.

[0062] High-density EEG equipment is used to arrange evenly distributed electrode arrays in designated cerebral cortical areas (such as the frontal lobe, parietal lobe, etc.). The number of electrodes is 64 or 128. A multi-channel acquisition method is used 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.

[0063] The collected brain neural electrical signals were filtered using a bandpass filter with a filtering 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.

[0064] Set a baseline timeline as a unified reference and synchronize the preprocessed brain neural signal time series with the baseline timeline according to the acquisition timestamp. Divide the baseline timeline into time windows of fixed length (e.g., 500 milliseconds per segment) to ensure consistent temporal resolution for subsequent analysis.

[0065] For the brain nerve electrical signal sequence in each time window, several sampling scales of variable length intervals are set. The entropy value of the brain nerve electrical signal intensity at different scales is calculated through a recursive multi-scale entropy analysis method. The entropy value is used to quantify the complexity of the neural signal at different time scales. The specific process is as follows:

[0066] Given a brain nerve electrical signal sequence , 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: .

[0067] Perform segmented averaging on the signal of the kth time window to construct different time scales The coarse-grained signal under the condition of :

[0068]

[0069] For coarse-grained signals Construct an embedding vector of length m And define distance matching:

[0070]

[0071] Set the similarity tolerance (usually 15%-25% of the signal standard deviation), and statistically meet The number of matching pairs that are less than or equal to the set similarity tolerance and , corresponding to embedding dimensions m and m+1 respectively.

[0072] The sample entropy is calculated using the sample entropy formula, which is:

[0073]

[0074] Where, 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 .

[0075] The multi-scale entropy values of brain nerve electrical signal strength across all electrode channels are summarized, and weighted averages are used to assign weights to the entropy values of different channels (for example, weights are assigned based on the importance of electrode location). This weighted summation generates a time series describing the changes in the complexity of neural activity in the target cerebral cortex before and after transcranial direct current intervention.

[0076] The generated complexity change time series is calibrated based on the starting time of each time window, corresponding to the timestamp position on the reference time axis, to align the analysis with the stimulus event. The calibrated time series is output for further dynamic characteristic evaluation.

[0077] 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.

[0078] A high-precision electrode array is evenly distributed across a designated spinal cord segment (e.g., cervical, thoracic, or lumbar), with each electrode spaced evenly apart to ensure coverage of the target area. A spinal reflex signal collector records neural reflex signals in real time at a high sampling rate (e.g., 5000 Hz), capturing dynamic neural reflex activity within the spinal cord segment and generating continuous time series data.

[0079] The acquired spinal nerve reflex signals are bandpass filtered from 30 Hz to 500 Hz to eliminate low-frequency baseline drift (such as motion artifacts below 30 Hz) and high-frequency background noise (such as electromagnetic interference above 500 Hz). The generated nerve reflex signal time series data after preprocessing is synchronized to the reference time axis based on the acquisition timestamp to ensure temporal consistency with other signals.

[0080] Fast Fourier Transform (FFT) is used to convert the preprocessed spinal reflex signals from the time domain to the frequency domain, generating a spectrum energy distribution diagram. The spectrum analyzes the specific frequency range of the spinal reflex signals, separating the energy contributions of each frequency band and retaining the frequency information related to neural activity.

[0081] The energy density of spinal nerve reflex signals is calculated within a 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.

[0082] 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.

[0083] The Spearman rank correlation coefficient is calculated based on the cross-regional neural signal correlation between the brain and spinal cord to represent the trend correlation of the changes in the characteristics of the brain and spinal cord neural signals. The formula of the Spearman rank correlation coefficient is:

[0084]

[0085] Where, 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.

[0086] 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.

[0087] On the reference time axis, the time series data of brain nerve electrical signals and spinal cord reflex signals collected within each time window are subjected to a fast Fourier transform (FFT) to convert the time domain signals into a frequency domain representation. In the spectrogram, the frequency domain signals of the target frequency band are extracted based on 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 an inverse Fourier transform, generating a time series related to the set frequency band.

[0088] The Hilbert transform method is used to convert the extracted target frequency band time domain signal into a complex form, using the formula:

[0089]

[0090] Where, 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.

[0091] In S4, the instantaneous phase is further calculated, and the frequency locking ratio is calculated by the phase difference between the brain nerve electrical signal and the spinal cord reflex signal. The frequency locking ratios are arranged in chronological order to form a time series of phase synchronization across the brain-spinal cord signal:

[0092]

[0093] Where, is the instantaneous phase, which represents the phase angle of the signal at time t, is the plural form of the signal The imaginary part of comes from the Hilbert transform, is the plural form of the signal The real part of comes from the original time domain signal.

[0094] 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 to calculate the phase difference sequence. The calculation expression is:

[0095]

[0096] Where, is the phase difference, 、 are the instantaneous phases of the brain nerve electrical signal and the spinal cord reflex signal at time t, and the phase difference is normalized to Within the interval,

[0097] 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 use this ratio as the representative value of the locking ratio.

[0098] The frequency locking ratio of each time window on the reference time axis is averaged to calculate the phase synchronization, and the phase synchronization is arranged in chronological order to form a phase synchronization time series across the brain-spinal cord signal.

[0099] In S5, the phase synchronization time series of cross-brain-spinal cord signals 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, specifically including:

[0100] Mapping the synchronization value in each time window of the phase synchronization time series of brain-spinal cord signals to a high-dimensional space, and constructing a high-dimensional phase space trajectory with time series points as trajectory nodes;

[0101] Analyze the geometric characteristics of trajectories in high-dimensional phase space, including the area and dispersion of the trajectory and the average curvature of the trajectory;

[0102] 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:

[0103]

[0104] Where, is the evaluation value of the effect of transcranial direct current stimulation intervention, is the trend correlation of changes in brain-spinal cord nerve signal characteristics, 、 、 They are the average curvature of the trajectory, the discreteness of the trajectory, and the area of the high-dimensional trajectory.

[0105] In S6, the transcranial direct current intervention stimulation parameters are adjusted continuously across the brain, the maximum value of the transcranial direct current intervention stimulation effect evaluation value displayed by the 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.

[0106] Example 2

[0107] The difference between Example 2 of the present invention and Example 1 is that this example introduces a method for real-time evaluation of the effect of transcranial direct current intervention stimulation.

[0108] Figure 2 A schematic diagram of a method for real-time evaluation of the effects of transcranial direct current (DC) intervention stimulation is provided. The method comprises a transcranial DC generator, a cranial nerve electrical signal collector, a spinal cord reflex signal collector, an arithmetic processor, and a display output device.

[0109] Transcranial direct current generator: Initiates continuous current output at the set location according to the set transcranial direct current intervention stimulation parameters;

[0110] Brain neural electrical signal collector: internally integrated EEG unit records the neural electrical activity of the cerebral cortex;

[0111] Spinal cord reflex signal collector: internal integrated electromyography unit records the neural electrical activity of the cerebral cortex;

[0112] Arithmetic processor: used to analyze and process the signals collected by the brain nerve electrical signal collector and the spinal cord reflex signal collector;

[0113] Display output device: outputs to the computing processor.

[0114] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0115] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in the embodiments of this application are fully or partially performed. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired means (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0116] Those skilled 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 beyond the scope of this application.

[0117] Those skilled in the art will 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.

[0118] In the several embodiments provided in this 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 merely schematic. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods, 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.

[0119] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to achieve the purpose of this embodiment according to actual needs.

[0120] 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.

[0121] 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, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0122] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0123] 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 scope of protection of the present invention.

Claims

1. A device for real-time evaluation of transcranial direct current stimulation effect, used to implement a real-time evaluation method for transcranial direct current stimulation effect, characterized in that: Including transcranial direct current generator, brain 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 location according to the set transcranial direct current intervention stimulation parameters; Brain neural electrical signal collector: internally integrated EEG unit records the neural electrical activity of the cerebral cortex; Spinal cord reflex signal collector: internal integrated electromyography unit records the neural electrical activity of the cerebral cortex; Arithmetic processor: used to analyze and process the signals collected by the brain nerve electrical signal collector and the spinal cord reflex signal collector; Display output device: output to the computing processor; The method for real-time evaluation of transcranial direct current intervention stimulation effect comprises: S1: Collect multi-channel brain nerve electrical signals from a set cerebral cortical area, calculate the complexity changes of brain nerve signals through multi-scale entropy, and establish a time series of complexity changes before and after transcranial direct current intervention stimulation; S2: Collect nerve reflex signals from the specified spinal cord segments, extract the high-frequency energy density time series of the nerve reflex signals using fast Fourier transform, align them with the time series of EEG signal complexity changes, establish cross-regional brain-spinal cord nerve signal feature correlations, and calculate the trend correlation of brain-spinal cord nerve signal feature changes; S3: Extracting brain nerve electrical signals and spinal cord reflex signals within a set frequency 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 brain nerve electrical signal and the spinal cord reflex signal, and arrange the frequency locking ratios in chronological order to form a time series of phase synchronization across the brain-spinal cord signal; S5: Map the phase synchronization time series of cross-brain-spinal cord signals 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, 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, 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 device 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 from a set cerebral cortical area are collected, and the complexity changes of brain nerve signals are calculated through multi-scale entropy. The time series of complexity changes before and after transcranial direct current intervention stimulation is established. Specifically, the following are included: Use a transcranial direct current generator to initiate continuous current output at the patient's set site according to the set transcranial direct current intervention stimulation parameters; An evenly distributed electrode array is arranged in a set cerebral cortical area, and a cerebral neural signal collector is used to collect multi-electrode channel cerebral neural signals reflecting cerebral cortical neural activity at a set frequency to monitor the dynamic changes of cerebral cortical neural activity; Bandpass filtering is used to eliminate low-frequency drift and environmental noise in brain neural electrical signals, and independent component analysis is used to separate physiological artifact signals. Set a reference time axis, synchronize the preprocessed brain neural electrical signal time series data to the reference time axis according to the acquisition timestamp, and divide the reference time axis into time windows of set lengths. Set several indefinite interval sampling scales for the brain neural electrical signal sequence in each time window, calculate the entropy value of the brain neural electrical signal intensity at different scales through a recursive multi-scale entropy analysis method, and quantify the complexity of neural signals at different time scales through the entropy value; The multi-scale entropy calculation results of the 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 a weight distribution method to generate a time series describing the changes in the complexity of the target cerebral 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 device 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 specified spinal cord segments are collected. The high-frequency energy density time series of the nerve reflex signals is extracted using fast Fourier transform. This is then synchronized with the time series of EEG signal complexity changes to establish a cross-regional brain-spinal cord neural signal feature association. The trend correlation of brain-spinal cord neural signal feature changes is calculated. Specifically, the following steps are involved: The electrode array is evenly distributed at the set spinal cord segment position, and the spinal cord reflex signal collector records the spinal cord nerve reflex signal in real time. The high-precision electrodes capture the dynamic nerve reflex signal of the spinal cord segment 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 preprocessed nerve reflex signal time series data to the reference time axis according to the acquisition timestamp; The spinal cord reflex signal is decomposed into a spectrum by fast Fourier transform, and the time domain data is expressed as a frequency domain energy distribution. According to the frequency range of the spinal cord reflex signal, the energy contribution of each frequency band is separated. Extracting the energy density of spinal nerve reflex signals above a preset frequency to generate a high-frequency energy change time series of the spinal nerve reflex signals; The high-frequency energy change time series of the spinal cord nerve reflex signal is calibrated on the reference time axis according to the recorded timestamp information. The high-frequency energy change time series and the EEG signal complexity change time series are aligned on the reference time axis to establish a cross-regional brain-spinal cord neural signal association. The Spearman rank correlation coefficient was calculated based on the cross-regional neural signal correlation of the brain and spinal cord to indicate the trend correlation of the changes in brain and spinal cord neural signal characteristics.

4. A device 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 within 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 to extract the time domain signal within the set frequency domain range; The extracted time domain signal is converted into instantaneous phase representation using Hilbert transform to generate instantaneous phase time series.

5. A device for real-time evaluation of transcranial direct current stimulation effect according to claim 4, characterized in that: In S4, the frequency locking ratio is calculated by the phase difference between the brain nerve electrical signal and the spinal cord reflex signal, and the frequency locking ratios are arranged in chronological order to form a time series of phase synchronization across the brain-spinal cord signal. Specifically, the frequency locking ratios include: Perform point-to-point differential operations on the instantaneous phases of the cranial nerve electrical signals and spinal cord reflex signals in each time window, calculate the phase difference sequence, and standardize the phase difference values; 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 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 is arranged in chronological order to form a phase synchronization time series across the brain-spinal cord signal.

6. The device 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 cross-brain-spinal cord signals 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, specifically including: Mapping the synchronization value in each time window of the phase synchronization time series of brain-spinal cord signals to a high-dimensional space, and constructing a high-dimensional phase space trajectory with time series points as trajectory nodes; Analyze the geometric characteristics of trajectories in high-dimensional phase space, including the area and dispersion of the trajectory and the average curvature of the trajectory; 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: Where, is the evaluation value of the effect of transcranial direct current stimulation intervention, is the trend correlation of changes in brain-spinal cord nerve signal characteristics, 、 、 They are the average curvature of the trajectory, the discreteness of the trajectory, and the area of the high-dimensional trajectory.

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