Closed-loop time interference nerve regulation and control system and method based on synchronous electroencephalogram signals

Abstract

The invention belongs to the technical field of biomedical signal processing and nerve regulation and control, and discloses a closed-loop time interference nerve regulation and control system and method based on synchronous electroencephalogram signals. The closed-loop nerve regulation and control system integrates multi-channel electroencephalogram collection, neural state real-time recognition, brain network function connection analysis, traceability positioning calculation and individualized time interference stimulation strategies, an upper computer module can obtain electroencephalogram signals and analyze brain function states in real time, abnormal network structures are automatically recognized, and the brain function states are analyzed in real time. A double-target stimulation path is formulated by combining a nerve source positioning result, so that the defects of inaccurate positioning, incapability of responding to brain state change in real time and unpredictable stimulation effect of a traditional nerve stimulation method are overcome; the closed-loop mechanism of'perception-decision-execution-evaluation 'of the system not only enhances the effectiveness of stimulation, but also remarkably improves the safety, controllability and individualization level of the regulation and control process.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of biomedical signal processing and neural regulation, and particularly relates to a closed-loop temporal interference neural regulation system and method based on synchronous electroencephalogram signals. BACKGROUND

[0002] Abnormal brain function is closely related to various neuropsychiatric diseases (such as depression, epilepsy, Parkinson's disease). Traditional neural regulation techniques, such as transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS), are limited in their efficacy and application range due to the difficulty of precisely focusing the electric field on deep brain regions.

[0003] The proposal of temporal interference stimulation (TIS) technology provides a new solution for deep and non-invasive electrical stimulation. This technology applies two or more high-frequency (usually higher than 1 kHz) currents externally to cause interference in the skull, forming a low-frequency modulation envelope corresponding to the frequency difference. This low-frequency envelope can selectively act on specific deep brain regions, while the high-frequency carrier has less effect on superficial brain regions because it is beyond the response range of neurons. Therefore, TIS has the advantages of deep and relatively focused stimulation.

[0004] However, current TIS technology research and application are mostly in the open-loop stage, that is, the stimulation parameters (such as location, intensity, frequency) are pre-set by the operator, and there is no adjustment according to the real-time state of the brain during the stimulation process. This mode ignores the dynamic nature and individual differences of brain neural activity, making it difficult to achieve personalized and precise treatment.

[0005] Electroencephalography (EEG) as an important tool that can reflect the neural electrical activity of the brain has been widely used in the decoding and identification of brain function states. Although existing brain-computer interface systems can achieve certain closed-loop feedback, their regulation methods are mostly sensory feedback (such as visual and auditory) or traditional non-focused electrical stimulation, which cannot be combined with the deep focusing advantage of TIS.

[0006] Therefore, there is an urgent need for a closed-loop system that can deeply integrate real-time EEG brain network analysis with precise deep TIS stimulation. This system should be able to interpret the functional connectivity state of the brain in real time, automatically identify abnormal network pathways, and achieve precise identification and dual-target point co-regulation of abnormal brain function connections, thereby dynamically correcting abnormal neural activity and achieving individualized, adaptive, and precise neural regulation effects. SUMMARY

[0007] The purpose of the present application is to solve the problems of the prior art and provide a closed-loop temporal interference neural regulation system and method based on synchronous electroencephalogram signals.

[0008] In order to solve the technical problems, the technical scheme of the present application is: a closed-loop time interference neural regulation method based on synchronous electroencephalogram signals, comprising the following steps: Step 1: Electroencephalogram signal acquisition and processing; Step 1.1: Signal acquisition; the electroencephalogram signals collected by the integrated electroencephalogram cap are amplified by a low-noise amplifier, digitized by an analog-to-digital converter, and transmitted to a signal processing unit; Step 1.2: Signal processing; the low-frequency interference and noise components in the electroencephalogram signals are eliminated by the low-pass filter of the signal processing unit, and the frequency with physiological significance in the electroencephalogram signals is extracted by the band-pass filter, and then the electroencephalogram signals are transmitted to the host computer module; Step 2: Host computer processing; Step 2.1: Electroencephalogram signal processing; the electroencephalogram signal processing sub-module of the host computer module performs real-time analysis on the electroencephalogram signals to obtain processed electroencephalogram signals; Step 2.2: Traceability positioning and modeling; the neurosource traceability positioning of the processed electroencephalogram signals in step 2.1 is performed by using the traceability positioning and modeling sub-module; then a dynamic brain network model is constructed for dynamic brain network analysis and abnormal connection edge identification; then double-target point time interference simulation is performed to obtain an output stimulation scheme; Step 2.3: Closed-loop double-target point decision; the abnormal connection edge information and the output stimulation scheme are received by the closed-loop double-target point decision sub-module, the brain network state of the real-time calculated output stimulation scheme is converted into executable double-target point stimulation instructions, and the intervention effect is evaluated in real time to form a closed-loop control; Step 3: Time interference electric stimulation; Step 3.1: The communication and control unit of the time interference electric stimulation module receives the double-target point stimulation instructions of step 2.3, feeds back the current stimulation state, and sends the double-target point stimulation instructions to the time interference stimulation unit to obtain a time interference high-frequency signal; Step 3.2: The signal generator of the time interference stimulation unit receives the time interference high-frequency signal, generates two frequencies with a difference from the time interference high-frequency signal, and then filters out the low-frequency stray components through a high-pass filter, so that the two frequencies with a difference produce a low-frequency modulation envelope with a frequency of in the intracranial target area for time interference stimulation.

[0009] Preferably, the integrated electroencephalogram cap of step 1.1 integrates recording electrodes and stimulation electrodes, the recording electrodes are made of Ag / AgCl material, and the stimulation electrodes are made of sintered Ag / AgCl or platinum-iridium alloy; the frequency with physiological significance in step 1.2 is 、 , wave.

[0010] Preferably, the step 2.1 is specifically: using the zero-phase band-pass filter of the electroencephalogram signal processing submodule of the host computer module to limit the electroencephalogram signal in the range of 0.5 Hz to 80 Hz, then using the independent component analysis method to automatically identify and eliminate the electrooculogram, electromyogram and electrocardiogram artifact components, and using z-score for normalization processing to obtain the preprocessed electroencephalogram signal .

[0011] Preferably, the step 2.2 includes the following steps: Step 2.2.1: neurosource localization; Based on individual MRI or standard head model, head tissue segmentation is performed and the corresponding conductivity parameters are configured, a plurality of equivalent current dipoles are arranged as source points on the grid nodes of the cortical surface or the center of the gray matter voxels, forming a discretized source space, the source points are basic units representing local neuron group activity, each source point includes its spatial position, dipole direction and time-varying current intensity; The finite element forward solution is used to solve the electromagnetic forward problem to generate the lead field matrix for describing the contribution mode of any source point activity to each electroencephalogram measurement channel; in the forward model framework, the preprocessed electroencephalogram signal and the source point current density vector The relationship is expressed as: In the formula: The lead field matrix obtained by the finite element forward solution is represented as The noise term is represented as The eLORETA inversion algorithm is used to solve the source point current density vector; eLORETA regards the source localization problem as a weighted Tikhonov regularization optimization task, and obtains the optimized source point current density vector by minimizing the sum of the reconstruction error and the source space smoothing constraint: In the formula: The source point current density vector after optimization at time is represented as The transpose matrix of the source point current density vector is represented as The regularization parameter is represented as The weighted matrix containing spatial neighborhood relationship is represented as Thus, a set of source space estimation sequences arranged in time can be obtained at all sampling times. ; Step 2.2.2: Dynamic brain network analysis and abnormal connectivity identification; Based on the source space estimation sequence output in step 2.2.1, the source points are first aggregated according to a preset brain region division template. Multiple source points belonging to the same brain region are weighted and integrated to obtain the overall time series of the corresponding brain region ROI. According to the patient's disease type and known abnormal brain network patterns, key brain region pairs related to the disease are pre-determined as potential functional connectivity pairs. Functional connectivity indices are calculated only for these key brain region pairs related to the disease to reflect the synchronization or coupling relationship of the activities of the two brain regions, thus obtaining the dynamic brain network analysis results. The functional connectivity indices include phase lock value (PLV), weighted phase lag index (wPLI), and amplitude envelope correlation (AEC). Step 2.2.3: Dual-target time-interference simulation; Target coordinate extraction: Determine the spatial location of the two targets and the real-time neural source intensity based on the results of dynamic brain network analysis; Electric field superposition calculation: Apply two high-frequency carrier waves to calculate the instantaneous electric field superposition and low-frequency envelope intensity in the brain; Focusing and safety assessment: Analyze the target field strength and non-target leakage, evaluate the focusing effect and adjust the electrode and current strategies to optimize simultaneous focusing of two targets while meeting safety constraints; Output stimulation scheme: including three-dimensional electric field matrix, target field strength, focusing performance, electrode combination, current distribution and carrier parameters.

[0012] Preferably, the phase-locked value (PLV) calculation process is as follows: for brain regions and time series , First, the instantaneous phase is obtained through Hilbert transform. and In length of Within the sliding window, PLV is defined as: In the formula: k Indicates 1 to Any value between; The closer the value is to 1, the higher the instantaneous phase synchronization between the two brain regions. The dynamic timing of PLV is obtained by scanning the time axis through a sliding window.

[0013] Preferably, step 2.3 includes: Step 2.3.1: Closed-loop stimulus decision trigger: When abnormal connection edge information is received, the closed-loop mode is automatically triggered, which can be started manually or automatically; Step 2.3.2: Simultaneous Dual-Target Stimulation Planning and Execution: Target connection sorting: Abnormal connections are sorted according to the degree of deviation of functional connection strength to generate a priority queue for regulation; by default, the connections with the most significant deviation are processed first, and the strategy is adjusted according to clinical or research needs; Stimulation planning and execution: Abnormal connectivity edges are sorted to generate a priority queue for regulation; dual-target stimulation instructions are generated sequentially, including electrode configuration, carrier frequency, frequency difference, current intensity and stimulation duration; instructions are sent to the time-interference electrical stimulation module to execute synchronous time-interference stimulation, and EEG signals are collected within a short window after stimulation to update the connectivity status, forming a closed loop.

[0014] Preferably, in step 3.2 It is 10Hz.

[0015] Preferably, the closed-loop time-interference neuromodulation system based on synchronized EEG signals includes an EEG signal acquisition and processing module, a host computer module, and a time-interference electrical stimulation module. The EEG signal acquisition and processing module includes a signal acquisition unit, a signal processing unit, and a signal transmission unit; The signal acquisition unit includes an integrated EEG cap, a low-noise amplifier, and an analog-to-digital converter. The integrated EEG cap is electrically connected to the low-noise amplifier, and the low-noise amplifier is electrically connected to the analog-to-digital converter. The integrated EEG cap amplifies the acquired EEG signals through the low-noise amplifier, then performs digital processing through the analog-to-digital converter, and finally transmits the EEG signals to the signal processing unit. The signal processing unit includes a bandpass filter and a low-pass filter, which are electrically connected. The bandpass filter is used to extract physiologically significant frequency components from the EEG signal and remove low-frequency drift and high-frequency noise. The low-pass filter is used to eliminate high-frequency interference and noise components in the EEG signal. Then, the EEG signal is transmitted to the signal transmission unit. The signal transmission unit sends the processed EEG signals to the host computer module through the data transmission interface; The host computer module includes an EEG signal processing submodule, a source tracing and localization and modeling submodule, and a closed-loop dual-target decision-making submodule. The EEG signal processing submodule is used to process EEG signals in real time to obtain processed EEG signals; The source localization and modeling submodule is used to accurately locate the brain regions that require electrical stimulation intervention, ensuring a high degree of matching between the stimulation target and neural activity; The closed-loop dual-target decision submodule is used to receive real-time brain network state parameters and electric field simulation results, complete closed-loop stimulation control decisions, convert real-time brain network state parameters and electric field simulation results into precise, sequential stimulation commands, and transmit the stimulation commands to the time-interference electrical stimulation module. The time-interference electrical stimulation module includes a time-interference stimulation unit, a power supply unit, and a communication and control unit; The communication and control unit is used to receive stimulation instructions from the host computer module, provide feedback on the current stimulation status, and send the time interference high-frequency signal to the time interference stimulation unit. The time-interference stimulation unit includes a signal generator and a high-pass filter. The signal generator is electrically connected to the high-pass filter. The signal generator receives the time-interference high-frequency signal and generates two channels with a difference based on the time-interference high-frequency signal. The frequency, the signal generator is used to output two channels with a difference. The frequency and the frequency generated in the intracranial target area are The low-frequency modulation envelope is used to filter out low-frequency stray components in time-interfered high-frequency signals; The power supply unit is used to provide constant current output power to the time-interference stimulation unit and the communication and control unit.

[0016] Preferably, the cutoff frequency of the high-pass filter is 400Hz.

[0017] Compared with the prior art, the advantages of this application are: (1) This application proposes a closed-loop neuromodulation system that integrates multi-channel EEG acquisition, real-time identification of neural state, brain network functional connectivity analysis, source localization calculation and individualized time-interference stimulation strategy. The host computer module can acquire EEG signals and analyze brain functional state in real time, automatically identify abnormal network structures, and formulate dual-target stimulation paths based on neural source localization results, thereby overcoming the defects of traditional neural stimulation methods such as inaccurate localization, inability to respond to changes in brain state in real time, and unpredictable stimulation effects. The closed-loop mechanism of "perception-decision-execution-evaluation" of the system in this application not only enhances the effectiveness of stimulation, but also significantly improves the safety, controllability and individualization level of the modulation process. (2) This application utilizes TIS to achieve non-invasive deep focused electric field generation, breaking through the limitation of traditional tES in focusing on deep brain regions. Through the source localization module of the host computer, the deep neural source is inverted and calculated. The system can determine the actual occurrence area of ​​deep abnormal activity and automatically plan the high-frequency carrier combination and phase configuration accordingly, so that the interference envelope accurately covers the target brain region. This process realizes the complete link from deep source localization → automatic generation of interference path → controllable electric field distribution, improving the accuracy, stability and repeatability of deep neural modulation. (3) The true personalization and adaptive closed loop of this application: The system decodes and locates targets based on the individual's real, real-time dynamic EEG signals, realizes personalized mapping from the source of neural activity to the stimulation path, and automatically adjusts the subsequent stimulation strategy according to the immediate feedback after stimulation, realizing the fully automated closed loop of "evaluation-decision-intervention-re-evaluation", which changes the current mainstream open-loop stimulation mode. (4) The innovative network-level regulation of this application: the intervention target is raised from "single brain region" to "functional connectivity edge", and direct regulation at the neural pathway level is achieved through dual-target synchronous stimulation mode, which is more in line with the physiological mechanism of brain network and significantly improves regulation efficiency and treatment effect; (5) The system of this application is widely applicable to the precision treatment of neuropsychiatric diseases such as depression, epilepsy, cognitive impairment, attention deficit, and consciousness disorder, as well as brain-computer interface, neurocognitive science research and other fields, providing a new technical path for future personalized and intelligent neuromodulation. Attached Figure Description

[0018] Figure 1 This is a structural block diagram of the closed-loop time-interference neuromodulation system based on synchronized EEG signals according to this application; Figure 2 This is a flowchart of the closed-loop time-interference neuromodulation method based on synchronized EEG signals used in this application; Figure 3 This document describes the changes in electroencephalogram (EEG) signals before and after the time-interference stimulation in Example 1 of this application. Figure 4 This is a schematic diagram of the EEG signal acquisition processor of this application; Figure 5 This is a schematic diagram of the multi-stage low-noise filtering and amplification circuit for EEG signal acquisition in this application.

[0019] Explanation of reference numerals in the attached figures: 1. EEG signal acquisition and processing module; 2. Host computer module; 3. Time-interference electrical stimulation module; 1-1 Signal acquisition unit; 1-2 Signal processing unit; 1-3 Signal transmission unit; 1-1-1 Integrated EEG cap; 1-1-2 Low-noise amplifier; 1-1-3 Analog-to-digital converter; 1-2-2, Bandpass filter; 1-2-1, Low-pass filter; 2-1. EEG signal processing submodule; 2-2. Source localization and modeling submodule; 2-3. Closed-loop dual-target decision-making submodule; 3-1. Time-interference stimulation unit; 3-2. Power supply unit; 3-3. Communication and control unit; 3-1-1, Signal Generator; 3-1-2, High-Pass Filter. Detailed Implementation

[0020] The present application is described in detail below with reference to the accompanying drawings and specific embodiments, but the present application is not limited to these embodiments. The present application covers any alternatives, modifications, equivalent methods, and solutions made within the spirit and scope of the present application. To provide the public with a thorough understanding of the present application, specific details are described in detail in the following embodiments, but those skilled in the art will fully understand the present application even without these detailed descriptions.

[0021] This application discloses a closed-loop time-interference neuromodulation method based on synchronized electroencephalogram (EEG) signals, comprising the following steps: Step 1: EEG signal acquisition and processing; Step 1.1: Signal acquisition; The EEG signals acquired by the integrated EEG cap 1-1-1 are amplified by the low-noise amplifier 1-1-2, digitized by the analog-to-digital converter 1-1-3, and transmitted to the signal processing unit 1-2. Step 1.2: Signal processing; The low-pass filter 1-2-1 of the signal processing unit 1-2 is used to eliminate high-frequency interference and noise components in the EEG signal, and the band-pass filter 1-2-2 is used to extract physiologically significant frequencies in the EEG signal. Then the EEG signal is transmitted to the host computer module 2. Step 2: Host computer processing; Step 2.1: EEG signal processing; The EEG signal processing submodule 2-1 of the host computer module 2 is used to analyze the EEG signal in real time to obtain the processed EEG signal; Step 2.2: Source localization and modeling; using the source localization and modeling submodule 2-2, the EEG signals processed in Step 2.1 are used to locate the neural source; then, a dynamic brain network model is constructed, and dynamic brain network analysis and abnormal connection edge identification are performed; then, a dual-target time interference simulation is performed to obtain the output stimulus scheme; Step 2.3: Closed-loop dual-target decision-making; using the closed-loop dual-target decision-making submodule 2-3 to receive abnormal connection edge information and output stimulus scheme, the brain network state of the output stimulus scheme calculated in real time is transformed into executable dual-target stimulus instructions, and the intervention effect is evaluated in real time to form closed-loop control. Step 3: Time-interference electrical stimulation; Step 3.1: The communication and control unit 3-3 of the time-interference electrical stimulation module 3 receives the dual-target stimulation command from step 2.3, feeds back the current stimulation state, and sends the dual-target stimulation command to the time-interference stimulation unit 3-1 to obtain the time-interference high-frequency signal; Step 3.2: The signal generator 3-1-1 of the time interference stimulation unit 3-1 receives the time interference high-frequency signal and generates two channels with difference based on the time interference high-frequency signal. The frequency is then filtered out by a high-pass filter 3-1-2 to remove low-frequency spurious components, resulting in a difference between the two paths. The frequency generated in the intracranial target area is [frequency]. The low-frequency modulation envelope is used for time-interference stimulation.

[0022] Preferably, the integrated EEG cap 1-1-1 in step 1.1 integrates recording electrodes and stimulation electrodes. The recording electrodes are made of Ag / AgCl material, and the stimulation electrodes are made of sintered Ag / AgCl or platinum-iridium alloy. The physiologically significant frequency in step 1.2 is... , , Wave.

[0023] Preferably, step 2.1 specifically involves: using the zero-phase bandpass filter of the EEG signal processing submodule 2-1 of the host computer module 2 to limit the EEG signal to the range of 0.5Hz~80Hz; then using independent component analysis to automatically identify and remove artifacts from electrooculography (EOG), electromyography (EMG), and electrocardiography (ECG); and then using z-score normalization to obtain the preprocessed EEG signal. .

[0024] Preferably, step 2.2 includes the following steps: Step 2.2.1: Neural source localization; Head tissue segmentation is performed based on individual MRI or standard head model and corresponding conductivity parameters are configured. Multiple equivalent current dipoles are arranged as source points on the grid nodes on the cortical surface or at the center of gray matter voxels to form a discretized source space. The source point is the basic unit representing the activity of local neuronal groups. Each source point includes its spatial location, dipole direction and current intensity that changes over time. The electromagnetic problem is solved using the finite element method in a forward direction to generate a lead field matrix, which describes the contribution pattern of arbitrary source point activity to each EEG measurement channel. Within the forward model framework, the preprocessed EEG signals... With the source current density vector The relationship is represented as: In the formula: This represents the guiding field matrix obtained by the forward solution of the finite element method; Indicates the noise term; The eLORETA inversion algorithm is used to solve for the source current density vector. eLORETA treats the source localization problem as a weighted Tikhonov regularization optimization task, and obtains the optimized source current density vector by minimizing the sum of reconstruction error and source space smoothing constraint. In the formula: Indicates the optimized value at time [time]. The source current density vector; The transpose matrix represents the source current density vector; This represents the regularization parameter, used to balance the data fitting term and the smoothing constraint term; Represents a weighted matrix that includes spatial nearest neighbor relationships; Thus, a set of source space estimation sequences arranged in time can be obtained at all sampling times. ; Step 2.2.2: Dynamic brain network analysis and abnormal connectivity identification; Based on the source space estimation sequence output in step 2.2.1, the source points are first aggregated according to a preset brain region division template. Multiple source points belonging to the same brain region are weighted and integrated to obtain the overall time series of the corresponding brain region ROI. According to the patient's disease type and known abnormal brain network patterns, key brain region pairs related to the disease are pre-determined as potential functional connectivity pairs. Functional connectivity indices are calculated only for these key brain region pairs related to the disease to reflect the synchronization or coupling relationship of the activities of the two brain regions, thus obtaining the dynamic brain network analysis results. The functional connectivity indices include phase lock value (PLV), weighted phase lag index (wPLI), and amplitude envelope correlation (AEC). Step 2.2.3: Dual-target time-interference simulation; Target coordinate extraction: Determine the spatial location of the two targets and the real-time neural source intensity based on the results of dynamic brain network analysis; Electric field superposition calculation: Apply two high-frequency carrier waves to calculate the instantaneous electric field superposition and low-frequency envelope intensity in the brain; Focusing and safety assessment: Analyze the target field strength and non-target leakage, evaluate the focusing effect and adjust the electrode and current strategies to optimize simultaneous focusing of two targets while meeting safety constraints; Output stimulation scheme: including three-dimensional electric field matrix, target field strength, focusing performance, electrode combination, current distribution and carrier parameters.

[0025] Preferably, the phase-locked value (PLV) calculation process is as follows: for brain regions and time series , First, the instantaneous phase is obtained through Hilbert transform. and In length of Within the sliding window, PLV is defined as: In the formula: k Indicates 1 to Any value between; The closer the value is to 1, the higher the instantaneous phase synchronization between the two brain regions. The dynamic timing of PLV is obtained by scanning the time axis through a sliding window.

[0026] Preferably, step 2.3 includes: Step 2.3.1: Closed-loop stimulus decision trigger: When abnormal connection edge information is received, the closed-loop mode is automatically triggered, which can be started manually or automatically; Step 2.3.2: Simultaneous Dual-Target Stimulation Planning and Execution: Target connection sorting: Abnormal connections are sorted according to the degree of deviation of functional connection strength to generate a priority queue for regulation; by default, the connections with the most significant deviation are processed first, and the strategy is adjusted according to clinical or research needs; Stimulation planning and execution: Abnormal connectivity edges are sorted to generate a priority queue for regulation; dual-target stimulation instructions are generated sequentially, including electrode configuration, carrier frequency, frequency difference, current intensity and stimulation duration; instructions are sent to the time-interference electrical stimulation module 3 to execute synchronous time-interference stimulation, and EEG signals are collected within a short window after stimulation to update the connectivity status, forming a closed loop.

[0027] Preferably, in step 3.2 It is 10Hz.

[0028] Preferably, the closed-loop time-interference neuromodulation system based on synchronized EEG signals includes an EEG signal acquisition and processing module 1, a host computer module 2, and a time-interference electrical stimulation module 3; The EEG signal acquisition and processing module 1 includes a signal acquisition unit 1-1, a signal processing unit 1-2, and a signal transmission unit 1-3; The signal acquisition unit 1-1 includes an integrated EEG cap 1-1-1, a low-noise amplifier 1-1-2, and an analog-to-digital converter 1-1-3. The integrated EEG cap 1-1-1 is electrically connected to the low-noise amplifier 1-1-2, and the low-noise amplifier 1-1-2 is electrically connected to the analog-to-digital converter 1-1-3. The integrated EEG cap 1-1-1 amplifies the acquired EEG signals through the low-noise amplifier 1-1-2, then performs digital processing through the analog-to-digital converter 1-1-3, and finally transmits the EEG signals to the signal processing unit 1-2. The signal processing unit 1-2 includes a bandpass filter 1-2-2 and a low-pass filter 1-2-1. The bandpass filter 1-2-2 and the low-pass filter 1-2-1 are electrically connected. The bandpass filter 1-2-2 is used to extract physiologically significant frequency components from the EEG signal and remove low-frequency drift and high-frequency noise. The low-pass filter 1-2-1 is used to eliminate high-frequency interference and noise components in the EEG signal. Then the EEG signal is transmitted to the signal transmission unit 1-3. Signal transmission units 1-3 send the processed EEG signals to host computer module 2 through the data transmission interface; The host computer module 2 includes an EEG signal processing submodule 2-1, a source tracing and localization and modeling submodule 2-2, and a closed-loop dual-target decision-making submodule 2-3; The EEG signal processing submodule 2-1 is used to process EEG signals in real time to obtain processed EEG signals; The source localization and modeling submodule 2-2 is used to accurately locate the brain regions that require electrical stimulation intervention, ensuring a high degree of matching between the stimulation target and neural activity; The closed-loop dual-target decision submodule 2-3 is used to receive real-time brain network state parameters and electric field simulation results, complete closed-loop stimulation control decisions, convert real-time brain network state parameters and electric field simulation results into precise, sequential stimulation commands, and transmit the stimulation commands to the time-interference electrical stimulation module 3. The time-interference electrical stimulation module 3 includes a time-interference stimulation unit 3-1, a power supply unit 3-2, and a communication and control unit 3-3; The communication and control unit 3-3 is used to receive stimulation instructions from the host computer module 2, provide feedback on the current stimulation status, and send the time interference high-frequency signal to the time interference stimulation unit 3-1; The time-interference stimulation unit 3-1 includes a signal generator 3-1-1 and a high-pass filter 3-1-2. The signal generator 3-1-1 is electrically connected to the high-pass filter 3-1-2. The signal generator 3-1-1 receives the time-interference high-frequency signal and generates two channels with difference based on the time-interference high-frequency signal. The frequency of the signal generator 3-1-1 is used to output two channels with a difference. The frequency and the frequency generated in the intracranial target area are The low-frequency modulation envelope, and the high-pass filter 3-1-2 are used to filter out low-frequency spurious components in the time-interference high-frequency signal; The power supply unit 3-2 is used to provide constant current output power to the time interference stimulation unit 3-1 and the communication and control unit 3-3.

[0029] Preferably, the cutoff frequency of the high-pass filter 3-1-2 is 400Hz.

[0030] The system described in this application includes the following modules: EEG signal acquisition and processing module 1: This module is used for real-time synchronous acquisition, front-end amplification and filtering preprocessing of EEG signals, and stably transmits the processed signals to the host computer module via a wired connection. It mainly includes: Signal Acquisition Unit 1-1: Integrated Electrode Cap. This EEG cap integrates both recording and stimulation electrodes. The recording electrodes are made of Ag / AgCl material to ensure good signal recording characteristics; the stimulation electrodes are made of materials with better polarization tolerance (such as sintered Ag / AgCl or platinum-iridium alloy) to withstand long-term current injection; the acquired signals are amplified by a pre-amplifier to improve the signal-to-noise ratio, and then digitized by a high-precision analog-to-digital converter (ADC). The ADC depth is 24 bits, and the sampling rate is not less than 8000Hz; the recording electrodes can be equipped with different numbers of channels (such as 8 / 16 / 32 / 64 channels) to adapt to different application scenarios.

[0031] Signal processing unit 1-2: Integrates dedicated hardware filter circuitry for front-end artifact suppression and frequency passband shaping of the acquired signals. This mainly includes: a bandpass filter (0.1Hz~85Hz) to extract physiologically significant frequency components from the EEG signal (such as…). , , The system employs various techniques to remove low-frequency drift and high-frequency noise. A low-pass filter eliminates high-frequency interference and noise components in the EEG signal, further reducing high-frequency artifacts. A high-voltage protection and fast recovery circuit is designed at the front end of the recording amplifier to prevent the stimulation pulse from saturating the amplifier. The placement of the stimulation and recording electrodes follows the principle of spatial isolation to minimize direct coupling.

[0032] Signal transmission units 1-3: The processed EEG signal is sent to the host computer module through the data transmission interface. The wired digital communication method is adopted, which has high bandwidth, low latency and high anti-interference performance.

[0033] Host computer module 2: The host computer module is the core processing and decision control unit of this system, and contains multiple sub-modules: EEG signal processing submodule 2-1: Used for real-time analysis of multi-channel EEG signals, mainly including the following steps: using a zero-phase bandpass filter to limit the signal to the range of 0.5Hz–80Hz; using an adaptive notch filter to remove power frequency interference. Then, independent component analysis is used to automatically identify and remove artifacts from electrooculography, electromyography, and electrocardiography, and z-score normalization is used to ensure consistent amplitude distribution across leads.

[0034] The source tracing, localization, and modeling submodule 2-2 includes the following steps: Step 2.2.1: Neural origin tracing; Head tissue segmentation was performed based on individual MRI or a standard head model, and corresponding conductivity parameters were configured. A large number of equivalent current dipoles were arranged as source points at grid nodes on the cortical surface or at the centers of gray matter voxels, forming a discretized source space. Each source point is a basic unit representing the activity of a local neuronal group, and each source point includes its spatial location, dipole orientation, and time-varying current intensity.

[0035] The electromagnetic forward problem is solved using the finite element method to generate the lead field matrix (i.e., the transfer matrix), which describes the contribution mode of arbitrary source point activity to each EEG measurement channel. Within the forward model framework, the preprocessed EEG signal... With the source current density vector The relationship can be represented as: in, The guide field matrix is ​​obtained by the forward solution of the finite element method. This is the noise term.

[0036] Based on this, the system employs the eLORETA inversion algorithm to solve for the source current density. eLORETA treats the source localization problem as a weighted Tikhonov regularized optimization task, obtaining the time-varying current estimate of the source point by minimizing the sum of the reconstruction error and the source space smoothing constraint. Its optimization form is as follows: in, Indicates at time The source current density vector; EEG signal; The field matrix is ​​obtained by forward finite element method. This is a regularization parameter used to balance the data fitting term and the smoothing constraint term; This is a weighted matrix that includes spatial nearest neighbor relationships. Therefore, a set of source spatial estimation sequences arranged in time can be obtained at all sampling times. This provides stable and reliable neural activity identifiers for subsequent brain region aggregation and dynamic network modeling.

[0037] Step 2.2.2: Dynamic brain network analysis and abnormal connectivity identification; Based on the source point time series output by the neural source tracing submodule, the source points are first aggregated according to a preset brain region segmentation template. Multiple source points belonging to the same brain region are weighted and integrated to obtain the overall time series of the corresponding brain region (ROI). The system pre-determines key brain region pairs related to the disease as potential functional connectivity pairs based on the patient's disease type and known abnormal brain network patterns. Functional connectivity indices, such as phase lock value (PLV), weighted phase lag index (wPLI), or amplitude envelope correlation (AEC), are calculated only for these disease-related brain region pairs to reflect the synchronization or coupling relationship of the two brain regions' activities.

[0038] Taking phase-locked value (PLV) as an example, for brain regions and time series , (From the ROI signal after source aggregation), the instantaneous phase is first obtained through Hilbert transform. and In length of Within the sliding window, PLV is defined as: in, A value closer to 1 indicates higher instantaneous phase synchronization between the two brain regions. The dynamic temporal sequence of PLV can be obtained by scanning the time axis through a sliding window. This index is updated in real time within the sliding time window, and its temporal continuity and noise resistance can be improved through exponential weighted smoothing.

[0039] To identify whether the current connection is in an abnormal state, the system provides two types of threshold calibration methods: (1) a reference range of connection indicators extracted from a statistical database of healthy groups; and (2) an individualized baseline range automatically constructed based on resting-state EEG at the beginning of the session. The functional connection indicators calculated in real time are compared with the calibration thresholds after standardization. If they exceed the upper or lower limits, they are judged as overly strong or insufficient connection. The instantaneous connection strength and abnormal judgment results are output to the host computer decision module to guide the adjustment of electrical stimulation parameters.

[0040] Step 2.2.3: Dual-target time-interference simulation; Based on the coordinates of the nodes at both ends of the abnormal connection edge, multi-channel electrodes are arranged on individualized or standard head models. Combining candidate electrode configuration and current distribution strategy, the finite element method is called to calculate the instantaneous electric field distribution of the dual carrier in the brain and to estimate the amplitude of the low-frequency modulation envelope in the target space.

[0041] The simulation process includes: Target coordinate extraction: Determining the spatial location of the two targets and the real-time neural source intensity based on dynamic brain network analysis results. Electric field superposition calculation: Applying two high-frequency carrier waves to calculate the instantaneous electric field superposition and low-frequency envelope intensity within the brain. Focusing and safety assessment: Analyzing target field strength and non-target leakage, evaluating focusing effectiveness, and adjusting electrode and current strategies to optimize simultaneous focusing of the two targets while meeting safety constraints. The output stimulation scheme includes a three-dimensional electric field matrix, target field strength, focusing performance, electrode combination, current allocation, and carrier parameters for closed-loop stimulation decision-making.

[0042] Closed-loop dual-target decision submodule 2-3: This submodule receives abnormal connection information and simulation results, transforms the brain network state into executable dual-target stimulation commands, and evaluates the intervention effect in real time, forming a closed-loop control.

[0043] Step 2.3.1: Closed-loop stimulus decision trigger: The closed-loop mode is automatically triggered when the target connection is abnormal. It can be started manually or automatically.

[0044] Step 2.3.2: Simultaneous Dual-Target Stimulation Planning and Execution: Step 2.3.2.1: Target Connection Sorting: The system sorts abnormal connections according to the degree of deviation from functional connection strength, generating a priority queue for regulation. By default, the system prioritizes processing the connections with the most significant deviations, but the strategy can be adjusted according to clinical or research needs.

[0045] Step 2.3.2.2: Stimulation planning and execution: sort abnormal connections and generate a regulation priority queue; generate dual-target stimulation instructions in sequence, including electrode configuration, carrier frequency, frequency difference, current intensity and stimulation duration; issue instructions to execute synchronous time interference stimulation, and collect EEG to update the connection status within a short window after stimulation, forming a closed loop.

[0046] Step 2.3.2.3: Loop termination condition; Successful termination: All connected edges in the queue have reached the required strength.

[0047] User termination: The operator manually terminates the operation.

[0048] Safe termination: The preset maximum total stimulation duration is reached or the system detects any abnormality (such as sudden changes in electrode impedance or patient discomfort feedback).

[0049] Step 2.3.2.4: Data recording and report generation: Continuously record stimulus parameters and network status. After the closed-loop cycle ends, generate a structured report for analysis and scheme optimization.

[0050] Time-interference electrical stimulation module 3: This module generates high-precision, controllable dual-path high-frequency current signals in real time according to the control instructions of the host computer module, forming a low-frequency modulation envelope in a specific deep brain target area to achieve non-invasive, controllable neural electrical stimulation. It mainly includes: Time-interference stimulation unit 3-1: Signal Generator 3-1-1: Contains two independent high-precision, high-stability sinusoidal current generators; the output frequencies of the two generators have a slight difference ( (e.g., 10Hz), generating a frequency of [frequency value] in the intracranial target area. The low-frequency modulation envelope.

[0051] High-pass filter 3-1-2 (cutoff frequency 400Hz) filters out low-frequency stray components in high-frequency time-interference signals.

[0052] Power supply unit 3-2: Provides a stable and controllable constant current output power to the stimulation module; has functions such as overcurrent protection, short circuit protection, and voltage monitoring.

[0053] Communication and Control Unit 3-3: Used for real-time data interaction and parameter control with the host computer. Its main functions include receiving start / stop commands from the host computer, setting carrier frequency and frequency difference, and adjusting stimulation intensity and duration. It supports both wired serial communication and wireless communication modes to adapt to different usage scenarios. The control unit integrates a microcontroller to parse host computer commands, adjust signal parameters in real time, and provide feedback on the current stimulation status (such as current intensity, frequency setting, electrode status, etc.). It is configured with a stimulation execution feedback mechanism to automatically send status signals back to the host computer when stimulation is completed or abnormally interrupted, facilitating overall system status management.

[0054] Example 1 To verify the regulatory effect of the dual-target temporal interference closed-loop stimulation system described in this application on brain neural activity, a comparative experiment was conducted. Changes in electroencephalogram (EEG) signals were recorded and analyzed before and during temporal interference (TI) stimulation. The EEG signal acquisition channel was 64 channels with a sampling rate of 8000 Hz. The stimulation frequencies were set to 2000 Hz and 2010 Hz, forming a 10 Hz low-frequency modulation envelope within the deep brain target area.

[0055] like Figure 3As shown, the image is divided into two parts: the upper part contains EEG signals acquired before TI stimulation (i.e., before stimulation); the lower part contains EEG signals acquired in real-time during TI stimulation (i.e., during stimulation). Each part contains two sub-images: the left side is the time-domain waveform, and the right side is the power spectral density (PSD) graph. Figure 3 As shown in the upper part, before TI stimulation, the EEG signals in each channel fluctuated little, and the time-domain waveforms were relatively stable, exhibiting typical natural resting-state EEG activity. The overall amplitude was mainly distributed within ±100 µV, and no obvious abnormal discharges or synchronization activities were observed. In the frequency domain analysis, the power spectral density plot showed a standard 1 / f decreasing trend, and no obvious power peaks or abnormal frequency band enhancements appeared in the spectrum, indicating that the EEG was in a relatively resting or low-activity state. Figure 3 As shown in the lower part, during the implementation of time-interference stimulation, the EEG waveform exhibited stronger rhythmic activity across multiple channels, with an overall amplitude slightly higher than before stimulation. Frequency domain analysis revealed a significant power enhancement in the low-frequency range (particularly around 10 Hz). This frequency corresponds precisely to the frequency difference between the two high-frequency sinusoidal currents used for TI stimulation in this experiment. The 10Hz frequency band indicates that TI intervention created a 10Hz low-frequency modulation envelope in the target brain region and effectively stimulated a synchronous response in the neuronal population. The power enhancement in this frequency band occurred synchronously in multiple channels, suggesting that TI stimulation can induce low-frequency coordinated oscillations in neural networks non-invasively, thereby achieving a neuromodulation effect.

[0056] The experimental results fully validate the effectiveness of the closed-loop TI stimulation system described in this application in regulating neural electrical activity. The TI stimulation system proposed in this application can effectively apply high-frequency signals to deep brain regions under non-invasive conditions, forming a spatially focused low-frequency modulated electric field and inducing specific neural rhythmic activity changes, demonstrating a clear neuromodulation effect. This system possesses advantages such as non-invasiveness, high spatial selectivity, and adjustable stimulation parameters, making it suitable for regulating functional connectivity networks under abnormal rhythmic states, providing technical support for the treatment of neurological diseases.

[0057] like Figure 4The image shows the EEG signal acquisition processor used in this system. This processor can be connected to an integrated electrode cap and a host computer terminal for the acquisition and transmission of EEG signals. The EEG signal acquisition processor includes a front-end amplification module, a filtering module, an analog-to-digital conversion module, and a communication module. The processor connects to the electrode interface of the integrated electrode cap to receive raw EEG signals from each lead. The raw signals are amplified by the front-end amplification module, and the filtering module performs preliminary suppression of baseline drift, power line interference, and high-frequency noise. The processed analog signal enters the analog-to-digital conversion module, is converted into a digital signal, and then sent to the host computer via the communication module. After receiving the digitized EEG data, the host computer further executes source localization, functional connectivity analysis, and closed-loop control procedures. The EEG signal acquisition processor in this embodiment can achieve stable, high signal-to-noise ratio EEG acquisition, meeting the needs of subsequent real-time analysis and closed-loop stimulation control. This instrument can be connected to an integrated electrode cap and a host computer to process the acquired raw EEG signals and transmit them to the host computer for analysis.

[0058] like Figure 5 The diagram shows the multi-stage low-noise filtering and amplification circuit used in this system for EEG signal acquisition. The circuit consists of an input protection and coupling network, a pre-amplifier low-noise amplifier module, a multi-stage active filter network, a bandpass frequency shaping module, and an output buffer module. At the input, protection resistors and limiting diodes suppress transient high voltage and electrostatic interference. After DC bias isolation via a high-impedance AC coupling network, the signal enters the pre-amplifier. The pre-amplifier module uses a low-noise operational amplifier and a precision feedback network to amplify the weak EEG signal, resulting in an overall input noise level below 1μV and significantly improving signal amplification accuracy. The amplified signal then passes through multiple stages of active low-pass filters composed of resistors, capacitors, and operational amplifiers. Each stage attenuates high-frequency interference in a second- or fourth-order manner, effectively suppressing noise and environmental artifacts above 85Hz. Simultaneously, by introducing a high-pass network composed of coupling capacitors and feedback resistors into the filtering link, the overall bandpass range is limited to 0.1Hz–85Hz, ensuring the complete preservation of the main frequency bands of EEG, such as δ, θ, α, and β. The filtered signal ultimately enters the output buffer module, where a voltage follower structure provides low output impedance to meet the input requirements of the ADC or subsequent sampling circuits and maintain stable filtering characteristics. Furthermore, this embodiment effectively reduces power ripple and parasitic noise by employing a multi-stage decoupling and low-noise power supply design at the power supply end, resulting in low overall power consumption and high stability. Through the above structure, this embodiment achieves high-precision, low-noise EEG front-end filtering functionality, while also possessing good anti-interference capabilities and integrability, providing a reliable hardware foundation for high-quality EEG signal acquisition.

[0059] This application proposes a closed-loop neuromodulation system integrating multi-channel EEG acquisition, real-time neural state recognition, brain network functional connectivity analysis, source localization calculation, and individualized time-interference stimulation strategy. The host computer module can acquire EEG signals and analyze brain functional states in real time, automatically identify abnormal network structures, and formulate dual-target stimulation paths based on neural source localization results. This overcomes the shortcomings of traditional neural stimulation methods, such as inaccurate localization, inability to respond to changes in brain state in real time, and unpredictable stimulation effects. The "perception-decision-execution-evaluation" closed-loop mechanism of this application's system not only enhances the effectiveness of stimulation but also significantly improves the safety, controllability, and individualization level of the modulation process.

[0060] This application utilizes TIS to achieve non-invasive deep focused electric field generation, overcoming the limitation of traditional tES in focusing on deep brain regions. Through the source localization module of the host computer, the system performs inversion calculations on deep neural sources, which can determine the actual occurrence area of ​​deep abnormal activity and automatically plan the high-frequency carrier combination and phase configuration accordingly, so that the interference envelope accurately covers the target brain region. This process realizes a complete link from deep source localization → automatic generation of interference path → controllable electric field distribution, improving the accuracy, stability and repeatability of deep neural modulation.

[0061] This application features true personalization and adaptive closed-loop: the system decodes and locates targets based on the individual's real, real-time dynamic EEG signals, realizing personalized mapping from the source of neural activity to the stimulation path, and automatically adjusts subsequent stimulation strategies based on immediate feedback after stimulation, achieving a fully automated closed loop of "assessment-decision-intervention-reassessment", which changes the current mainstream open-loop stimulation mode.

[0062] This application innovates network-level regulation by elevating the intervention target from "single brain region" to "functional connectivity edge." Through a dual-target synchronous stimulation mode, it achieves direct regulation at the neural pathway level, which is more in line with the physiological mechanism of brain networks and significantly improves regulation efficiency and therapeutic effect.

[0063] This application system is widely applicable to the precision treatment of neuropsychiatric diseases such as depression, epilepsy, cognitive impairment, attention deficit, and disorder of consciousness, as well as to fields such as brain-computer interfaces and neurocognitive science research, providing a new technical path for future personalized and intelligent neuromodulation.

[0064] The preferred embodiments of this application have been described in detail above. However, this application is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of this application.

[0065] Many other changes and modifications can be made without departing from the concept and scope of this application. It should be understood that this application is not limited to the specific embodiments, and the scope of this application is defined by the appended claims.

Claims

1. A closed-loop time-interference neuromodulation method based on synchronous electroencephalogram signals, characterized in that, The method comprises the following steps: Step 1: Electroencephalogram signal acquisition and processing; Step 1.1: Signal acquisition; the electroencephalogram signals collected by the integrated electroencephalogram cap (1-1-1) are amplified through a low-noise amplifier (1-1-2), and are digitized through an analog-to-digital converter (1-1-3), and are transmitted to a signal processing unit (1-2); Step 1.2: Signal processing; the low-pass filter (1-2-1) of the signal processing unit (1-2) is used to eliminate high-frequency interference and noise components in the electroencephalogram signals, and the band-pass filter (1-2-2) is used to extract the frequency with physiological significance in the electroencephalogram signals, and then the electroencephalogram signals are transmitted to the host computer module (2); Step 2: Host computer processing; Step 2.1: Electroencephalogram signal processing; the electroencephalogram signal processing submodule (2-1) of the host computer module (2) is used to analyze the electroencephalogram signals in real time to obtain processed electroencephalogram signals; Step 2.2: Trace positioning and modeling; the neurosource trace positioning of the processed electroencephalogram signals in step 2.1 is performed by using the trace positioning and modeling submodule (2-2); then a dynamic brain network model is constructed, and dynamic brain network analysis and abnormal connection edge identification are performed; then double-target time interference simulation is performed to obtain an output stimulation scheme; Step 2.3: Closed-loop double-target decision; the abnormal connection edge information and the output stimulation scheme are received by the closed-loop double-target decision submodule (2-3), the brain network state of the output stimulation scheme calculated in real time is converted into executable double-target stimulation instructions, and the intervention effect is evaluated in real time to form a closed-loop control; Step 3: Time interference electric stimulation; Step 3.1: The communication and control unit (3-3) of the time interference electric stimulation module (3) receives the double-target stimulation instructions of step 2.3, feeds back the current stimulation state, and sends the double-target stimulation instructions to the time interference stimulation unit (3-1) to obtain a time interference high-frequency signal; Step 3.2: The signal generator (3-1-1) of the time-interference stimulation unit (3-1) receives the time-interference high-frequency signal, and generates two paths of the existing difference frequency according to the time-interference high-frequency signal, then filters out the low-frequency stray components through the high-pass filter (3-1-2), and finally the two paths of the existing difference frequency generate a low-frequency modulation envelope with a frequency of in the intracranial target area, and carry out time-interference stimulation.

2. The closed-loop time-interference neuromodulation method based on synchronized brain electrical signals according to claim 1, characterized in that, The integrated brain electroencephalogram cap (1-1-1) of the step 1.1 integrates recording electrodes and stimulating electrodes, the recording electrodes adopt Ag / AgCl material, and the stimulating electrodes adopt sintered Ag / AgCl or platinum-iridium alloy; the frequency with physiological significance in the step 1.2 is 、 、 wave.

3. The closed-loop time- interferential neuromodulation method based on synchronized brain electrical signals according to claim 1, characterized in that, The step 2.1 is specifically: the electroencephalogram signal is limited in the range of 0.5Hz~80Hz by using the zero-phase band-pass filter of the electroencephalogram signal processing submodule (2-1) of the host computer module (2), then the electrooculogram, electromyogram and electrocardiogram false trace components are automatically identified and removed by using the independent component analysis method, and the z-score is used for normalization processing to obtain the preprocessed electroencephalogram signal .

4. The closed-loop time-interference neuromodulation method based on synchronized brain electrical signals according to claim 1, characterized in that, The step 2.2 comprises the following steps: Step 2.2.1: Neurosource trace positioning; Head tissue segmentation is performed based on individual MRI or standard head model, and corresponding conductivity parameters are configured, a plurality of equivalent current dipoles are arranged as source points on the grid nodes on the cortical surface or the center of the gray matter voxels, a discretized source space is formed, each source point contains its spatial position, dipole direction and time-varying current intensity, and represents the activity of a local neuron group; The finite element forward solution is used to solve the electromagnetic forward problem, and the lead field matrix is generated to describe the contribution mode of any source point activity to each electroencephalogram measurement channel. Under the framework of the forward model, the preprocessed electroencephalogram signal is expressed as the relationship between the source point current density vector . In the formula: represents the lead field matrix resulting from the finite element forward solution; represents a noise term; The eLORETA inversion algorithm is used to solve the source current density vector; eLORETA regards the source positioning problem as a weighted Tikhonov regularization optimization task, and obtains the optimized source current density vector by minimizing the sum of the reconstruction error and the source space smoothing constraint: In the formula: represents the optimized source point current density vector at time t; denotes the transpose matrix of the source point current density vector; denotes a regularization parameter balancing the data fitting term and the smoothness constraint term; denotes a weighted matrix containing spatial neighborhood relations; Thereby a set of time-ordered source space estimate sequences is obtained at all sampling instants ; Step 2.2.2: Dynamic brain network analysis and abnormal connection identification; Based on the source space estimation sequence output in step 2.2.1, first, the source points are aggregated according to the preset brain region division template, and multiple source points belonging to the same brain region are weighted and integrated to obtain the overall time sequence of the ROI corresponding to the brain region; according to the disease type of the patient and the known brain network abnormal mode, the key brain region pairs related to the disease are determined in advance as potential functional connection pairs, and only the functional connection indicators of these disease-related key brain region pairs are calculated to reflect the synchronization or coupling relationship between the activities of the two brain regions, to obtain the dynamic brain network analysis result; the functional connection indicators include phase locking value PLV, weighted phase lag index wPLI and amplitude envelope correlation AEC; Step 2.2.3: Double-target time interference simulation; Target point coordinate extraction: determine the spatial position of the double-target and the real-time neural source intensity according to the dynamic brain network analysis result; electric field superposition calculation: apply two high-frequency carriers to calculate the instantaneous electric field superposition and low-frequency envelope intensity in the brain; Focusing and safety evaluation: analyze the target point field strength and non-target point leakage, evaluate the focusing effect and adjust the electrode and current strategy to optimize the simultaneous focusing of the double-target and meet the safety constraints; Output stimulation scheme: including three-dimensional electric field matrix, target point field strength, focusing performance, electrode combination, current distribution and carrier parameters.

5. The closed-loop temporal interference neuromodulation method based on synchronized brain electrical signals according to claim 4, characterized in that, The phase locking value PLV calculation process is: for the brain region With time series , , first get the instantaneous phase and by Hilbert transform, in the sliding window with length , PLV is defined as: In the formula: k represents any number from 1 to between 1 and 10. The closer the value is to 1, the higher the instantaneous phase synchronization of the two brain regions. By scanning the time axis with a sliding window, the dynamic timing of the PLV is obtained.

6. The closed-loop temporal interference neuromodulation method based on synchronized brain electrical signals according to claim 1, wherein, The step 2.3 comprises: Step 2.3.1: Closed-loop stimulation decision trigger: automatically trigger the closed-loop mode when receiving abnormal connection edge information, which can be manually or automatically started; Step 2.3.2: Synchronous double-target stimulation planning and execution: Target connection sorting: sort the abnormal connection edges according to the functional connection strength deviation degree to generate a regulation priority queue; by default, the most significant connection edge is processed first, and the strategy can be adjusted according to clinical or research needs; Stimulation planning and execution: sort the abnormal connection edges to generate a regulation priority queue; generate double-target stimulation instructions one by one, including electrode configuration, carrier frequency, frequency difference, current intensity and stimulation duration; issue the instructions to the time interference electric stimulation module (3) to execute synchronous time interference stimulation, and collect EEG signals in a short window after stimulation to update the connection state, forming a closed-loop cycle.

7. The closed-loop temporal interference neuromodulation method based on synchronized brain electrical signals according to claim 1, wherein, Said step 3.2 in is 10 Hz.

8. A closed-loop temporal interference neuromodulation system based on synchronized electroencephalographic signals, characterized in that: The implementation of the closed-loop time interference neuroregulation method based on synchronous EEG signals according to any one of claims 1-7 comprises an EEG signal acquisition and processing module (1), an upper computer module (2) and a time interference electric stimulation module (3); The EEG signal acquisition and processing module (1) comprises a signal acquisition unit (1-1), a signal processing unit (1-2) and a signal transmission unit (1-3); The signal acquisition unit (1-1) comprises an integrated electroencephalogram cap (1-1-1), a low-noise amplifier (1-1-2) and an analog-to-digital converter (1-1-3), the integrated electroencephalogram cap (1-1-1) is electrically connected with the low-noise amplifier (1-1-2), the low-noise amplifier (1-1-2) is electrically connected with the analog-to-digital converter (1-1-3), the integrated electroencephalogram cap (1-1-1) amplifies the collected electroencephalogram signals through the low-noise amplifier (1-1-2), then the signals are digitized by the analog-to-digital converter (1-1-3), and then the electroencephalogram signals are transmitted to the signal processing unit (1-2); The signal processing unit (1-2) comprises a band-pass filter (1-2-2) and a low-pass filter (1-2-1), the band-pass filter (1-2-2) is electrically connected with the low-pass filter (1-2-1), the band-pass filter (1-2-2) is used for extracting the frequency components with physiological significance in the electroencephalogram signals, removing low-frequency drift and high-frequency noise, and the low-pass filter (1-2-1) is used for eliminating high-frequency interference and noise components in the electroencephalogram signals, and then the electroencephalogram signals are transmitted to the signal transmission unit (1-3); The signal transmission unit (1-3) transmits the processed electroencephalogram signals to the host computer module (2) through a data transmission interface; The host computer module (2) comprises an electroencephalogram signal processing sub-module (2-1), a traceability positioning and modeling sub-module (2-2) and a closed-loop double-target decision sub-module (2-3); The electroencephalogram signal processing sub-module (2-1) is used for real-time processing of the electroencephalogram signals to obtain processed electroencephalogram signals; The traceability positioning and modeling sub-module (2-2) is used for accurately positioning the brain area needing electrical stimulation intervention, and ensuring that the stimulation target is highly matched with the neural activity; The closed-loop double-target decision sub-module (2-3) is used for receiving real-time brain network state parameters and electric field simulation results, completing closed-loop stimulation control decision, converting the real-time brain network state parameters and electric field simulation results into accurate and serialized stimulation instructions, and transmitting the stimulation instructions to the time interference electrical stimulation module (3); The time interference electrical stimulation module (3) comprises a time interference stimulation unit (3-1), a power supply unit (3-2) and a communication and control unit (3-3); The communication and control unit (3-3) is used for receiving the stimulation instructions from the host computer module (2), feeding back the current stimulation state, and sending the time interference high-frequency signal to the time interference stimulation unit (3-1); The time-interference stimulation unit (3-1) includes a signal generator (3-1-1) and a high-pass filter (3-1-2). The signal generator (3-1-1) and the high-pass filter (3-1-2) are electrically connected. The signal generator (3-1-1) receives the time-interference high-frequency signal and generates two channels with difference based on the time-interference high-frequency signal. The frequency of the signal generator (3-1-1) is used to output two channels with a difference. The frequency and the frequency generated in the intracranial target area are The low-frequency modulation envelope is used to filter out low-frequency spurious components in the time-interference high-frequency signal; The power supply unit (3-2) is used for providing constant-current output power for the time interference stimulation unit (3-1) and the communication and control unit (3-3).

9. The closed-loop temporal interference neuromodulation system based on synchronized brain electrical signals of claim 8, wherein: The cutoff frequency of the high-pass filter (3-1-2) is 400 Hz.

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