Pathological target positioning method and system based on time domain interference stimulation

Abstract

The invention provides a pathological target positioning method and system based on time domain interference stimulation. The method comprises the steps that electroencephalogram data, functional image data and structural image data of a patient are acquired; generating a spatial distribution diagram according to the electroencephalogram data and the structure image data; performing registration processing on the spatial distribution diagram and the functional image data to obtain the spatial distribution diagram and the functional image data after registration processing; the spatial distribution map after registration processing is matched with the spatial position of the functional image data; performing data fusion on the spatial distribution map and the functional image data after registration processing to generate a comprehensive brain function map; phase synchronism analysis is carried out on the comprehensive brain function map, the phase synchronism of different brain regions is evaluated, and the brain region with the phase synchronism larger than a preset threshold value and active in the cognitive task is determined as the pathological target region. According to the embodiment of the invention, the method can achieve the precise recognition and positioning of the AD core pathological target region, and facilitates the precise intervention of the AD core pathological target region.

Description

Technical Field

[0001] The present disclosure relates to the technical field of medical data processing, and particularly to a method and system for pathological target area localization based on time-domain interference stimulation. Background Art

[0002] Alzheimer's disease (AD) is one of the most common neurodegenerative diseases, manifested as various cognitive function impairments such as memory loss, language disorders, decline in executive function, and loss of orientation. As the disease progresses, the patient's living ability deteriorates severely, bringing a huge burden to the patient and their family.

[0003] Currently, the treatment options for AD include drug treatment, physical therapy, and cognitive training. However, due to the inability to localize the core pathological target area of AD, it is difficult to achieve precise intervention on the core pathological target area of AD. Moreover, due to the individual pathological differences of patients, the existing treatment options generally lack personalized targeting, with limited efficacy and difficulty in significantly delaying the disease course. Summary of the Invention

[0004] In view of this, the present disclosure proposes a method and system for pathological target area localization based on time-domain interference stimulation to solve the problem in the related art that due to the inability to localize the core pathological target area of AD, it is difficult to achieve precise intervention on the core pathological target area of AD, resulting in insufficient regulation ability for deep brain regions.

[0005] The first aspect embodiment of the present disclosure proposes a method for pathological target area localization based on time-domain interference stimulation, including:

[0006] Obtaining electroencephalogram data, functional imaging data, and structural imaging data of a patient;

[0007] Generating a spatial distribution map according to the electroencephalogram data and the structural imaging data; the spatial distribution map reflects the activity intensity of different brain regions;

[0008] Performing registration processing on the spatial distribution map and the functional imaging data to obtain the registered spatial distribution map and functional imaging data; the spatial positions of the registered spatial distribution map and functional imaging data are matched;

[0009] Fusing the registered spatial distribution map and functional imaging data to generate a comprehensive brain function map;

[0010] Evaluating the phase synchrony between different brain regions by performing phase synchrony analysis on the comprehensive brain function map, and determining the brain regions with phase synchrony greater than a preset threshold and active in cognitive tasks as pathological target areas.

[0011] In an embodiment of the present disclosure, generating a spatial distribution map based on the electroencephalogram data and the structural image data includes:

[0012] Constructing a head model of the patient according to the structural image data; the head model is used to simulate the scalp conduction path of the source signal;

[0013] Calculating a lead matrix according to the head model; the lead matrix is used to characterize the spatial relationship between the brain source and the electrodes;

[0014] Generating the spatial distribution map according to the lead matrix and the electroencephalogram data.

[0015] In an embodiment of the present disclosure, performing data fusion on the registered spatial distribution map and the functional image data to generate a comprehensive brain function map includes:

[0016] Performing power spectral density analysis on the registered spatial distribution map to determine the power of different frequency band regions, and identifying the active frequency band regions in the cognitive task according to the power of the different frequency band regions;

[0017] Calculating the correlation coefficient between the power in a specific brain region in the registered spatial distribution map and the activation intensity in the registered functional image data in the specific brain region;

[0018] Combining the power and activation intensity in the specific brain region where the correlation coefficient is greater than the preset coefficient to generate the comprehensive brain function map.

[0019] In an embodiment of the present disclosure, after determining the pathological target regions by screening out the brain regions with phase synchrony greater than the preset threshold and active in the cognitive task, the method further includes:

[0020] Screening out a target electrode layout corresponding to the pathological target region from multiple electrode layouts; the target electrode layout is the electrode layout with the highest concentration of the electric field in the pathological target region;

[0021] Generating a low-frequency envelope signal based on the electroencephalogram characteristics of the patient;

[0022] Selecting a target waveform type according to the cognitive needs of the patient;

[0023] Generating a treatment plan according to the layout parameters of the target electrode layout, the low-frequency envelope signal, and the target waveform type; the treatment plan is used to regulate the pathological target region.

[0024] In an embodiment of the present disclosure, generating a low-frequency envelope signal based on the electroencephalogram characteristics of the patient includes:

[0025] Determining a low-frequency intervention frequency according to the electroencephalogram characteristics;

[0026] Select two high-frequency signals through the low-frequency interference frequency; the frequency difference between the two high-frequency signals is equal to the low-frequency interference frequency;

[0027] Generate the low-frequency envelope signal according to the two high-frequency signals; the low-frequency envelope signal acts on the pathological target area.

[0028] In the embodiments of the present disclosure, according to the cognitive needs of the patient, select the target waveform type, including:

[0029] When the cognitive need is phase synchronization adjustment, the target waveform type is a sine wave;

[0030] When the cognitive need is phase edge stimulation, the target waveform type is a square wave.

[0031] In the embodiments of the present disclosure, the method further includes:

[0032] During the process of regulating the pathological target area through the treatment plan, obtain the current electroencephalogram data of the patient in real time;

[0033] Judge whether the treatment plan achieves the expected effect according to the current electroencephalogram data;

[0034] If the treatment plan does not achieve the expected effect, perform adaptive adjustment on the treatment plan, and re-regulate the pathological target area through the treatment plan after adaptive adjustment.

[0035] The embodiments of the second aspect of the present disclosure provide a pathological target area positioning system based on time-domain interference stimulation, including:

[0036] A data acquisition module for acquiring the electroencephalogram data, functional image data, and structural image data of the patient;

[0037] A spatial distribution map generation module for generating a spatial distribution map according to the electroencephalogram data and the structural image data; the spatial distribution map reflects the activity intensity of different brain regions;

[0038] A registration module for performing registration processing on the spatial distribution map and the functional image data to obtain the registered spatial distribution map and functional image data; the spatial positions of the registered spatial distribution map and functional image data match;

[0039] A data fusion module for fusing the registered spatial distribution map and functional image data to generate a comprehensive brain function map;

[0040] A pathological target area determination module, configured to evaluate the phase synchrony between different brain regions by performing phase synchrony analysis on the comprehensive brain functional atlas, and determine the brain regions with phase synchrony greater than a preset threshold and active in cognitive tasks as pathological target areas.

[0041] In an embodiment of the present disclosure, the system further includes:

[0042] An electrode layout determination module, configured to screen out a target electrode layout corresponding to the pathological target area from multiple electrode layouts; the target electrode layout is the electrode layout with the highest concentration of the electric field in the pathological target area;

[0043] A treatment plan generation module, configured to generate a treatment plan according to the target electrode layout and the pathological target area;

[0044] A feedback regulation module, configured to adaptively adjust the stimulation parameters of the treatment plan according to the real-time acquired electroencephalogram data.

[0045] An embodiment of the third aspect of the present disclosure provides an electronic device, which includes a memory and a processor. The memory and the processor are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to execute the pathological target area localization method based on time-domain interference stimulation described in the first aspect above.

[0046] An embodiment of the fourth aspect of the present disclosure provides a computer-readable storage medium, on which computer instructions are stored. The computer instructions are used to cause a computer to execute the pathological target area localization method based on time-domain interference stimulation described in the first aspect above.

[0047] Additional aspects and advantages of the present disclosure will be given in part in the following description, will become apparent in part from the following description, or will be understood through the practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present disclosure. Moreover, throughout the drawings, the same reference numerals are used to represent the same components.

[0049] In the drawings:

[0050] Figure 1 A flowchart showing a method for localizing a pathological target area based on time-domain interference stimulation provided by an embodiment of the present disclosure;

[0051] Figure 2Shows a schematic flowchart of generating a spatial distribution map based on electroencephalogram data and structural image data provided by an embodiment of the present disclosure;

[0052] Figure 3 Shows a schematic flowchart of data fusion of the registered spatial distribution map and functional image data to generate a comprehensive brain function map provided by an embodiment of the present disclosure;

[0053] Figure 4 Shows a schematic flowchart of generating a low-frequency envelope signal based on the electroencephalogram characteristics of a patient provided by an embodiment of the present disclosure;

[0054] Figure 5 Shows a schematic flowchart of feedback regulation provided by an embodiment of the present disclosure;

[0055] Figure 6 Shows a schematic structural diagram of a pathological target area positioning system based on time-domain interference stimulation provided by an embodiment of the present disclosure;

[0056] Figure 7 Shows a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure;

[0057] Figure 8 Shows a schematic diagram of a storage medium provided by an embodiment of the present disclosure. Detailed implementation manners

[0058] Hereinafter, the exemplary embodiments of the present disclosure will be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0059] It should be noted that unless otherwise specified, the technical terms or scientific terms used in the present disclosure should have the ordinary meanings understood by those skilled in the art to which the present disclosure belongs.

[0060] According to an embodiment of the present disclosure, an embodiment of a pathological target area positioning method based on time-domain interference stimulation is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0061] In this embodiment, a pathological target area positioning method based on time-domain interference stimulation is provided, Figure 1 Is a flowchart of the pathological target area positioning method based on time-domain interference stimulation according to an embodiment of the present disclosure, asFigure 1 As shown, the process includes the following steps:

[0062] Step S101, obtaining electroencephalogram data, functional imaging data, and structural imaging data of the patient.

[0063] In the embodiments of the present disclosure, electroencephalogram data (EEG) is used to record the brain electrical activities of Alzheimer's disease (AD) patients in real time during the execution of cognitive tasks; functional imaging data (i.e., functional magnetic resonance imaging fMRI) is used to non-invasively measure the activity changes of the brain under specific tasks, and is particularly suitable for evaluating the brain function of AD patients; structural imaging data (i.e., structural magnetic resonance imaging sMRI) is a non-invasive imaging technique used to obtain the brain anatomical structure images of AD patients, providing high-precision brain region positions and structural information, and is an important basis for realizing accurate target positioning and personalized stimulation design.

[0064] In some specific embodiments, the specific acquisition process of electroencephalogram data (EEG) is as follows:

[0065] First, according to the cognitive characteristics and disorder types of AD patients, a suitable cognitive task is designed to ensure that the task can effectively activate the brain regions related to the core pathological network of Alzheimer's disease (such as the hippocampus, posterior cingulate cortex, and temporal lobe regions), and at the same time, the task difficulty needs to match the cognitive ability of the patient. For example, for AD patients with memory and attention deficits, an adjusted N-back task or a visual tracking task can be used for cognitive stimulation. Then, a high-density EEG system is used to collect brain electrical signals in real time. 64 to 128 electrodes are configured according to the international 10-20 system to fully cover the scalp, and key areas such as the frontal lobe, temporal lobe, and specific regions related to AD are monitored. Before electrode placement, by applying conductive paste or using aqueous gel on the scalp, good contact between the electrode and the skin is ensured, and the electrode impedance is reduced, thereby ensuring the signal acquisition quality. During the task execution, EEG signals are collected in real time at a sampling frequency of 1000 Hz or higher to capture the subtle brain electrical changes related to AD. To reduce the interference of electromyogram, electrocardiogram, and eye movement artifacts, the acquisition environment needs to be kept quiet, and the patient is guided to complete the task in a comfortable and natural state. At the same time, the acquisition system monitors the signal quality in real time to avoid the influence of artifact interference on subsequent analysis.

[0066] In some specific embodiments, the specific acquisition process of functional imaging data (i.e., functional magnetic resonance imaging fMRI) is as follows:

[0067] After the patient enters the fMRI scanner, they are instructed to keep their head as still as possible. Specific cognitive task stimuli are presented on the screen to activate brain regions related to AD. For example, for AD patients with memory deficits, visual stimuli including image matching or serial recall tasks can be shown. The design of these tasks needs to take into account the patient's cognitive abilities and research goals to ensure that the tasks can elicit significant responses in the target brain regions. The fMRI scan uses an EPI (echo-planar imaging) sequence with high temporal resolution to record the dynamic changes of the BOLD (blood oxygenation level-dependent) signal and capture the local blood oxygen level fluctuations when the patient performs the task. These fluctuations directly reflect the functional activity patterns of brain regions. To minimize the impact of head motion artifacts on data quality as much as possible, it is necessary to ensure the patient's head is fixed during the acquisition process and monitor the signal quality in real time. The responses made by the patient during the task execution are synchronously recorded, adding behavioral dimension information to the imaging data. At the same time, the scanner records the task-related brain functional activities in real time, providing high-quality data for subsequent analysis. These data are the key basis for analyzing the functional abnormalities of the brain pathological network in AD patients and are also an important reference for designing personalized intervention programs.

[0068] In some specific embodiments, the specific acquisition process of the structural imaging data (i.e., structural magnetic resonance imaging sMRI) is as follows:

[0069] The patient is guided into the scanner and keeps their head still to minimize the impact of motion artifacts. During the scan, a high-resolution T1-weighted imaging sequence is used to generate brain anatomical images, including details such as the morphological features of the cerebral cortex, the boundaries of brain tissues, and the distribution of gray and white matter. Special attention is paid to the anatomical regions related to the core pathological network of AD (such as the hippocampus, entorhinal cortex, and posterior cingulate cortex) to better support target area identification and intervention design. The acquired sMRI data provides key spatial information for subsequent electrode layout optimization. Combining functional imaging (fMRI) and electroencephalogram data (EEG), these structural images can be used to establish a personalized brain model to ensure that the precise placement of electrodes and the current distribution can cover the target area, thus achieving the best stimulation effect and regulation accuracy.

[0070] In some specific embodiments, after step S101 and before step S102, the method further includes: preprocessing the electroencephalogram data, functional imaging data, and structural imaging data respectively;

[0071] Among them, the purpose of preprocessing the electroencephalogram data is to clean noise, improve data quality, and extract effective signals. The preprocessing of imaging data (fMRI and sMRI) is used to determine data quality and consistency.

[0072] In some specific embodiments, the preprocessing of the electroencephalogram data includes:

[0073] First, a band-pass filter is used to retain the frequency band of interest (such as 0.5 - 45 Hz), and a notch filter is used to remove power frequency noise (such as 50 Hz or 60 Hz) and its harmonic interference. Subsequently, independent component analysis (ICA) is employed to separate and remove noise signals such as electromyogram, electrocardiogram, and eye movement artifacts. To further optimize the signal quality, an automated algorithm combined with manual inspection is used to identify abnormal data segments, and data with low signal-to-noise ratio is excluded. The data reference baseline is unified to a common average reference (CAR) or a specific region reference to improve signal consistency. The task event markers are corrected to ensure the alignment of EEG signals with the time points of behavioral tasks. Finally, based on the task event markers, the EEG data is segmented into time windows such as before and after stimulation and the response period, and relevant signal features are extracted to provide support for subsequent analysis.

[0074] In some specific embodiments, the preprocessing of imaging data (fMRI and sMRI) includes:

[0075] For fMRI data, first, the time deviation caused by sampling time differences during the scan is corrected, and a motion correction algorithm is used to eliminate the influence of head motion artifacts. Subsequently, the fMRI data is spatially registered to the individual's sMRI anatomical data and a standard spatial template (such as MNI space) to achieve spatial alignment. In addition, a low-pass filter is used to remove high-frequency noise, and non-task-related interferences such as the whole-brain average signal are excluded by regression methods. Data smoothing enhances the spatial continuity of the signal and reduces noise.

[0076] For sMRI data, first, an automatic segmentation algorithm is used to segment the brain anatomical image into tissue regions, dividing the data into gray matter, white matter, and cerebrospinal fluid regions, and anatomical features are extracted. Then, the sMRI image is normalized to a standard spatial template, and the cortical boundary detection is optimized to ensure the accurate identification of key anatomical regions (such as the hippocampus and entorhinal cortex).

[0077] Finally, through multi-modal data integration, the processed fMRI functional data and sMRI anatomical data are jointly registered to generate a personalized functional anatomical brain atlas. At the same time, the EEG data is strictly time-aligned with the fMRI data, and the spatial consistency between fMRI and sMRI is ensured, providing a high-quality basis for the fusion analysis of multi-modal data.

[0078] In summary, through systematic preprocessing of EEG, fMRI, and sMRI data, the quality and consistency of multi-modal data have been comprehensively improved. The preprocessing of EEG signals not only effectively removes artifacts and noise but also ensures the temporal accuracy and task relevance of the signals, laying a solid foundation for subsequent time-frequency analysis and brain region function assessment. The preprocessing of imaging data achieves high-precision anatomical-functional alignment. By correcting temporal deviations, motion artifacts, and spatial errors, the reliability of functional data is enhanced, and the accuracy of anatomical data in target area identification and personalized stimulation design is ensured. Multi-modal data integration further improves the comprehensiveness and accuracy of analysis. The combination of the fast temporal resolution of EEG signals and the high spatial resolution of fMRI data enables the accurate representation of dynamic brain network mechanisms in cognitive activities, while sMRI data provides the anatomical localization basis for these analyses.

[0079] Step S102: Generate a spatial distribution map based on the electroencephalogram data and the structural imaging data.

[0080] Specifically, the spatial distribution map reflects the activity intensity of different brain regions, and this spatial distribution map is used to locate the brain regions that show abnormalities during cognitive tasks.

[0081] In some specific embodiments, for example Figure 2 As shown, the above step S102 includes steps S1021 - S1023:

[0082] Step S1021: Construct a head model of the patient based on the structural imaging data, and this head model is used to simulate the scalp conduction path of the source signal.

[0083] Step S1022: Calculate the lead field matrix according to the head model; the lead field matrix is used to characterize the spatial relationship between the brain source and the electrodes.

[0084] Step S1023: Generate the spatial distribution map according to the lead field matrix and the electroencephalogram data.

[0085] In the above steps S1021 - S1023, first, based on the patient's structural imaging data sMRI, a personalized head model is constructed. Usually, the boundary element model (BEM) or the finite element model (FEM) is used to accurately describe the geometric structures of the brain regions, skull, and scalp to simulate the scalp conduction path of the source signal. Second, calculate the lead field matrix G to characterize the spatial relationship between the brain source and the electrodes, and use source reconstruction algorithms such as LORETA or sLORETA, combined with regularization constraints to optimize and solve the inverse problem to generate the activity intensity distribution map of different brain regions. The calculation formula of the spatial distribution map is as follows:

[0086] J = G T (GG T + λI) -1 X

[0087] Wherein, J is the activity intensity distribution of different brain regions of the reconstructed cerebral cortex, that is, the spatial distribution map (which can be understood as the EEG source reconstruction data), X is the preprocessed EEG data, G is the lead matrix, and λ is the regularization parameter used to suppress noise interference. Through the activity intensity distribution map of source reconstruction, subsequent analysis can more accurately identify the active brain regions.

[0088] Step S103: Perform registration processing on the spatial distribution map and the functional image data to obtain the registered spatial distribution map and functional image data; the spatial positions of the registered spatial distribution map and functional image data match.

[0089] In the embodiments of the present disclosure, the purpose of registration is to unify the spatial coordinates of the spatial distribution map (i.e., EEG source reconstruction data) and the functional image data (i.e., fMRI image data) to ensure the effective fusion of multi-modal data. The specific registration process includes:

[0090] Convert the EEG source reconstruction data and the fMRI image data into the same standard coordinate system (such as the MNI or Talairach coordinate system), and align the data using the feature-based mutual information method. The mutual information method realizes registration by maximizing the mutual information value between the EEG source reconstruction data and the fMRI image data. The calculation formula is as follows:

[0091]

[0092] Wherein, MI(A,B) represents the mutual information between the EEG source reconstruction data and the fMRI image, p(a,b) is the joint probability distribution between the EEG source reconstruction data and the fMRI image, and p(a) and p(b) are the marginal probability distributions of the EEG source reconstruction data and the fMRI respectively. Through mutual information registration, the spatial position of the EEG source reconstruction map is consistent with the fMRI image, providing a reliable spatial basis for subsequent functional data fusion.

[0093] Step S104: Perform data fusion on the registered spatial distribution map and the functional image data to generate a comprehensive brain function map.

[0094] Specifically, the purpose of data fusion is to generate a comprehensive brain function map to identify potential target areas for AD intervention.

[0095] In some specific embodiments, for example Figure 3 as shown, the above step S104 includes steps S1041 - S1043:

[0096] In step S1041, perform power spectral density analysis on the registered spatial distribution map to determine the power of different frequency band regions, and identify the active frequency band regions in the cognitive task according to the power of the different frequency band regions.

[0097] In step S1042, calculate the correlation coefficient between the power in a specific brain region in the registered spatial distribution map and the activation intensity of the specific brain region in the registered functional image data.

[0098] In step S1043, combine the power and activation intensity in the specific brain region where the correlation coefficient is greater than the preset coefficient to generate the comprehensive brain function map.

[0099] In the above steps S1041 - S1043,

[0100] Perform power spectral density (PSD) analysis on the EEG source reconstruction data (i.e., the registered spatial distribution map above), extract the power of different frequency bands to identify the active frequency band regions in the cognitive task, and its calculation formula is as follows:

[0101]

[0102] Where, is the Fourier transform result of the EEG source reconstruction data, and T is the total signal sampling duration. Then, perform time - space correlation analysis, calculate the correlation between the power change of different frequency bands of the EEG source reconstruction data and the fMRI activation intensity to identify the time and space associations. The Pearson correlation coefficient is used to analyze the relationship between the two, and its calculation formula is as follows:

[0103]

[0104] Where, x i and y i are respectively the power or activation intensity of the EEG source reconstruction data and the fMRI in a specific brain region, and are respectively the means of the two, and r xy is the correlation coefficient between the two. Finally, through multi - modal feature extraction, combine the EEG frequency band power with high time - space correlation and the fMRI activation region to generate a comprehensive brain function map, which helps to identify the abnormally active brain regions related to AD. These active regions serve as potential intervention target areas, providing a basis for subsequent target area confirmation and electrode layout optimization, so as to achieve precise and targeted intervention.

[0105] In the embodiments of the present disclosure, by fusing the spatially distributed map after registration processing and the functional imaging data, a comprehensive brain functional atlas is generated, providing reliable data support for the identification of potential intervention target areas for patients with Alzheimer's disease (AD).

[0106] Step S105: By performing phase synchrony analysis on the comprehensive brain functional atlas, evaluate the phase synchrony between different brain regions, and determine the brain regions with phase synchrony greater than a preset threshold and active in cognitive tasks as pathological target areas.

[0107] In some specific embodiments, the pathological target areas for AD intervention can be identified through the comprehensive brain functional atlas, as follows:

[0108] By performing phase synchrony analysis on the brain functional atlas data, evaluate the phase synchrony between different brain regions, and find the brain regions with phase synchrony higher than the preset threshold and active in cognitive tasks as target areas, especially the prefrontal lobe, parietal lobe, and hippocampus and other brain regions closely related to cognitive functions. Among them, the phase locking value (PLV) between each brain region is calculated to evaluate the phase synchrony of different brain regions in the brain functional atlas. The calculation formula of PLV is as follows:

[0109]

[0110] where N is the number of sampling points, φ 1 (k) and φ 2 (k) are the instantaneous phases of different brain regions respectively. The range of the PLV value is from 0 to 1. The closer the value is to 1, the higher the phase synchrony between the two brain regions, indicating that they may have functional interactions; approaching 0 indicates that there is no consistency or synchrony in phase, and the two brain regions may be active relatively independently.

[0111] In some specific embodiments, after the above step S105, the method further includes steps S106 - S109:

[0112] Step S106: Screen out the target electrode layout corresponding to the pathological target area from multiple electrode layouts; the target electrode layout is the electrode layout with the highest concentration of electric field in the pathological target area.

[0113] In the embodiments of the present disclosure, in order to focus the electric field generated by the stimulating current on the target area (i.e., the above-mentioned pathological target area) and reduce the influence on non-target areas, the electrode layout is optimized as follows:

[0114] Based on the location and anatomical features of the target region, the electric field distribution under different electrode layouts is predicted through an electric field simulation software (such as SimNIBS), and the influence of different electrode parameters on the electric field effect is evaluated, so as to guide the optimization of the electrode layout. The key parameters for optimizing the electrode layout include the number of electrodes, position, current intensity, etc. Subsequently, intelligent algorithms such as particle swarm optimization (PSO) or genetic algorithm (GA) are used to automatically adjust these parameters so that the intensity of the electric field in the target region reaches a set value (such as 1 mA / cm 2 ), while minimizing the influence of the electric field in the non-target region as much as possible. The specific optimization process is as follows: 1) Definition of the objective function: Set the objective function to maximize the current density in the target region and minimize the electric field intensity in the non-target region, which is defined as follows:

[0115]

[0116] where E(v) is the electric field intensity of voxel v, V t and V nt represent the volumes of the target region and non-target region respectively, and λ is a penalty factor used to adjust the non-target region constraint. 2) Particle swarm optimization (PSO) algorithm: Particle swarm optimization is a global optimization algorithm that simulates the behavior of natural populations. Initialize a group of particles (i.e., candidate electrode configurations), and through iterative calculation of the fitness of each particle in the objective function, the particles gradually approach the optimal position. The update formula for each particle in PSO is:

[0117]

[0118] where is the updated value of the velocity of particle i, w is the inertia weight, c 1 and c 2 are learning factors, p i is the historical optimal position of the particle, and g is the global optimal position. After multiple iterations, the electrode layout with the highest concentration of the electric field in the target region is found.

[0119] In some specific embodiments, after obtaining the target electrode layout, the method further includes verifying and adjusting the electric field of the target motor layout to ensure that it meets the intervention requirements, as follows:

[0120] First, import the layout scheme into an electric field simulation tool (such as SimNIBS or COMSOL), visualize the electric field distribution and check whether the electric field intensity in the target region reaches the expectation. At the same time, pay attention to the non-target region to avoid the occurrence of "hot spots" with high electric field intensity to prevent irrelevant nerve activation. If it is found that the electric field in the non-target region is too high, the non-target region electric field can be reduced by fine-tuning the electrode spacing or current intensity. Through iterative verification and feedback adjustment, finally ensure that the electric field distribution meets the set standards in the target region while minimizing the influence in the non-target region.

[0121] In the embodiments of the present disclosure, by accurately identifying target regions related to AD and optimizing the electrode layout based on the positions and anatomical features of these target regions, and finally through electric field simulation and verification, it is ensured that the stimulating electric field is focused on the target regions and the influence on non-target regions is minimized. This process realizes the optimal adjustment of the electrode layout through intelligent algorithms and electric field simulation technology, provides a solid foundation for personalized time-domain interference stimulation (TI stimulation) schemes, ensures the effectiveness, safety, and accuracy of the intervention, and provides strong support for the personalized regulation of AD patients.

[0122] Step S107: Generate a low-frequency envelope signal based on the electroencephalogram (EEG) characteristics of the patient.

[0123] In some specific embodiments, as Figure 4 shown, the above step S107 includes steps S1071 - S1073:

[0124] Step S1071: Determine the low-frequency intervention frequency according to the EEG characteristics.

[0125] Step S1072: Select two high-frequency signals through the low-frequency intervention frequency; the frequency difference between the two high-frequency signals is equal to the low-frequency intervention frequency.

[0126] Step S1073: Generate the low-frequency envelope signal according to the two high-frequency signals; the low-frequency envelope signal acts on the pathological target region.

[0127] In steps S1071 - S1073, by precisely controlling the frequency difference between the two high-frequency signals, the required low-frequency stimulation signal is generated, thereby realizing the precise adjustment of the frequency of the target brain region, specifically as follows:

[0128] First, determine the low-frequency intervention frequency according to the EEG characteristics of the patient. Different EEG frequency bands usually correspond to specific cognitive functions or emotional states. Different EEG frequencies are closely related to specific cognitive functions and emotional states. For example, the alpha wave (8 - 12 Hz) is usually related to concentration and relaxation states and is suitable for interventions to improve attention; while the theta wave (4 - 7 Hz) is mostly related to memory functions and emotional processing and is suitable for interventions to enhance memory. Module 250 selects the most suitable low-frequency intervention frequency by analyzing the characteristics of each frequency band in the EEG signal to optimize the intervention effect.

[0129] Secondly, after determining the low-frequency intervention frequency, TI stimulation generates the required low-frequency envelope signal by selecting two high-frequency signals. The frequency difference between the high-frequency signals should be equal to the target low-frequency frequency, thereby generating a matching low-frequency envelope. For example, if the target low-frequency intervention frequency is 6 Hz, the frequencies of the two selected high-frequency signals can be 2000 Hz and 2006 Hz respectively, and the frequency difference between them is 6 Hz, thus generating a 6-Hz low-frequency envelope. Generally, the high-frequency signal range is above 1000 Hz to ensure the stability and effectiveness of the low-frequency envelope. The generated low-frequency envelope signal then directly acts on the target brain region, thereby achieving the regulation of brain electrical activity.

[0130] Through the above process, the embodiments of the present disclosure can accurately control the frequency of the generated low-frequency envelope in the time domain, and further achieve the frequency regulation of a specific brain region. This process not only ensures the precise control of the intervention frequency, but also maximally improves the stimulation effect and the response of the target brain region.

[0131] Step S108, select the target waveform type according to the cognitive needs of the patient.

[0132] In the embodiments of the present disclosure, combined with the cognitive needs of AD patients, a suitable stimulation waveform type is selected to achieve the best intervention effect. For example: The sine wave is suitable for gentle phase synchronization adjustment and is often used for fine adjustment of the phase cooperation of brain regions; the square wave is suitable for generating a more significant phase edge stimulation effect to meet stronger stimulation needs. The selection of the waveform type ensures the pertinence of the intervention and makes it more in line with the personalized needs of the patient.

[0133] Step S109, generate a treatment plan according to the layout parameters of the target electrode layout, the low-frequency envelope signal, and the target waveform type; the treatment plan is used to regulate the pathological target area.

[0134] In the embodiments of the present disclosure, a complete personalized time-domain interference stimulation plan (i.e., the above treatment plan) can be formed according to the layout parameters of the target electrode layout (including the number of electrodes, positions, and current intensity), the low-frequency envelope signal, and the waveform type. This plan precisely matches the brain region characteristics of AD patients with the stimulation needs, realizes targeted and personalized TI intervention design, so as to meet the cognitive needs of AD patients and improve the intervention effect.

[0135] In the embodiments of the present disclosure, by comprehensively analyzing the electroencephalogram (EEG) characteristics and intervention requirements of patients, combined with precisely optimized electrode layout parameters, a personalized time-domain interference stimulation scheme is designed. This scheme ensures that the intervention can specifically regulate the activities of specific brain regions by precisely selecting the stimulation frequency, waveform type, and electrode layout, thereby maximizing the improvement of cognitive function and regulation effect. Through this personalized design, a customized intervention plan is provided for patients, ensuring the accuracy, effectiveness, and safety during the regulation process, and strongly supporting the personalized regulation of Alzheimer's disease (AD).

[0136] In some specific embodiments, during the process of regulating the pathological target area through the treatment plan, the method further includes a feedback adjustment process, for example Figure 5 as shown:

[0137] Step a1, obtaining the current EEG data of the patient in real time;

[0138] Step a2, judging whether the treatment plan has achieved the expected effect according to the current EEG data;

[0139] Step a3, if the treatment plan has not achieved the expected effect, adaptively adjusting the treatment plan, and re-regulating the pathological target area through the adaptively adjusted treatment plan.

[0140] In the above steps a1 - a3, the feedback adjustment process mainly includes real-time feedback supervision, adaptive adjustment, and a closed-loop control mechanism:

[0141] Real-time feedback supervision includes: obtaining the EEG data of the patient in real time through an EEG signal acquisition system and continuously monitoring the stimulation effect. By analyzing indicators such as the power of specific frequency bands and the phase-locking value (PLV) in the EEG signal, it is evaluated whether the intervention effect has reached the expectation. For example, if it is monitored that the frequency band power of the target brain region increases or the PLV value meets the set threshold, it indicates that the stimulation has had a significant impact on the target cognitive function.

[0142] Adaptive adjustment includes: when the monitoring results show that the stimulation effect has not reached the expectation, or side effects such as over-activation of non-target areas occur, the stimulation parameters will be adaptively adjusted. The specific adjustment methods include: appropriately adjusting the frequency difference of high-frequency signals to change the interference envelope frequency, or slightly adjusting the waveform shape and current intensity to optimize the stimulation effect and reduce unnecessary neural activation. At the same time, the system will automatically record all adjusted parameters to provide data support and reference for subsequent interventions.

[0143] Closed-loop control mechanism, including: achieving closed-loop control through real-time feedback signals to maintain the dynamic balance of stimulation and prevent over-activation. When the PLV value of the target brain region is monitored to reach the preset threshold, the stimulation is automatically paused to avoid unnecessary neural load; when the PLV value drops below the threshold, the stimulator output is restarted to ensure the continuous effectiveness and dynamic adaptability of the intervention. This closed-loop control mechanism can maximize the stability and personalization of the stimulation effect.

[0144] In the embodiments of the present disclosure, through the implementation of personalized TI stimulation programs, combined with real-time feedback supervision, adaptive adjustment, and closed-loop control mechanisms, precise regulation and safety protection of the target brain region are achieved. This module continuously monitors and adjusts the stimulation parameters during the stimulation process to ensure the effectiveness of the intervention effect and the personalized response ability, while avoiding unintentional activation of non-target regions and nervous system overload. Through dynamic feedback regulation, the stability and precision of the stimulation are significantly improved, providing personalized and scientific neuroregulation means for patients, and effectively supporting the goal of improving cognitive function.

[0145] The embodiments of the present disclosure have the following technical effects:

[0146] Personalized precise regulation program: Based on electroencephalogram signals and imaging data, precisely identify the pathological networks and brain regions related to Alzheimer's disease, and design personalized stimulation programs to ensure the pertinence of the regulation effect.

[0147] Precise regulation of deep brain regions: Through time-domain interference stimulation technology, effectively regulate the deep brain regions related to Alzheimer's disease, overcoming the limitations of the prior art.

[0148] Real-time dynamic regulation mechanism: Combine real-time electroencephalogram signal monitoring and cognitive task feedback to dynamically adjust the stimulation parameters, improving the safety and effect of the regulation.

[0149] Improve the regulation effect and persistence: Through personalized stimulation programs and dynamic adjustment mechanisms, the present invention can significantly improve the persistence of the regulation effect and the improvement of the patient's cognitive function.

[0150] Corresponding to the implementation manner of the above-mentioned pathological target area localization method based on time-domain interference stimulation, the embodiments of the present disclosure also provide a pathological target area localization system based on time-domain interference stimulation for performing the pathological target area localization method based on time-domain interference stimulation described in the above embodiments. As Figure 6 shown, the pathological target area localization system based on time-domain interference stimulation includes:

[0151] A data acquisition module for acquiring the electroencephalogram data, functional imaging data, and structural imaging data of the patient;

[0152] A spatial distribution map generation module, configured to generate a spatial distribution map based on the electroencephalogram data and the structural image data; the spatial distribution map reflects the activity intensity of different brain regions;

[0153] A registration module, configured to perform registration processing on the spatial distribution map and the functional image data to obtain the spatially registered spatial distribution map and functional image data; the spatial positions of the spatially registered spatial distribution map and functional image data match;

[0154] A data fusion module, configured to perform data fusion on the spatially registered spatial distribution map and the functional image data to generate a comprehensive brain function map;

[0155] A pathological target area determination module, configured to evaluate the phase synchrony between different brain regions by performing phase synchrony analysis on the comprehensive brain function map, and determine the brain regions with phase synchrony greater than a preset threshold and active during cognitive tasks as pathological target areas.

[0156] Optionally, the spatial distribution map generation module is further configured to: construct a head model of the patient based on the structural image data; the head model is used to simulate the scalp conduction path of the source signal; calculate a lead matrix based on the head model; the lead matrix is used to characterize the spatial relationship between the brain source and the electrodes; generate the spatial distribution map based on the lead matrix and the electroencephalogram data.

[0157] Optionally, the data fusion module is further configured to: perform power spectral density analysis on the spatially registered spatial distribution map to determine the power of different frequency band regions, and identify the active frequency band regions during cognitive tasks based on the power of the different frequency band regions; calculate the correlation coefficient between the power in a specific brain region in the spatially registered spatial distribution map and the activation intensity of the specific brain region in the spatially registered functional image data; combine the power and activation intensity in the specific brain region with a correlation coefficient greater than a preset coefficient to generate the comprehensive brain function map.

[0158] Optionally, the system further includes:

[0159] A target electrode layout screening module, configured to screen out a target electrode layout corresponding to the pathological target area from multiple electrode layouts; the target electrode layout is the electrode layout with the highest concentration of the electric field in the pathological target area;

[0160] A low-frequency envelope signal generation module, configured to generate a low-frequency envelope signal based on the electroencephalogram characteristics of the patient;

[0161] A waveform type selection module, configured to select a target waveform type according to the cognitive needs of the patient;

[0162] A treatment plan generation module, configured to generate a treatment plan according to the layout parameters of the target electrode layout, the low-frequency envelope signal, and the target waveform type; the treatment plan is used to regulate the pathological target area.

[0163] Optionally, the low-frequency envelope signal generation module is further configured to: determine a low-frequency intervention frequency according to the EEG feature; select two high-frequency signals through the low-frequency intervention frequency; a frequency difference between the two high-frequency signals is equal to the low-frequency intervention frequency; generate the low-frequency envelope signal according to the two high-frequency signals; the low-frequency envelope signal acts on the pathological target area.

[0164] Optionally, the waveform type selection module is further configured to: when the cognitive requirement is phase synchronization adjustment, the target waveform type is a sine wave; when the cognitive requirement is phase edge stimulation, the target waveform type is a square wave.

[0165] Optionally, the system further includes: a feedback adjustment module, configured to: acquire the current EEG data of the patient in real time; determine whether the treatment plan achieves the expected effect according to the current EEG data; if the treatment plan does not achieve the expected effect, perform adaptive adjustment on the treatment plan, and perform re-regulation on the pathological target area through the treatment plan after adaptive adjustment.

[0166] The pathological target area positioning system based on time-domain interference stimulation provided in the above embodiments of the present disclosure and the pathological target area positioning method based on time-domain interference stimulation provided in the embodiments of the present disclosure are based on the same inventive concept, and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0167] The present disclosure also provides an electronic device to execute the above-mentioned pathological target area positioning method based on time-domain interference stimulation. Please refer to Figure 7 which shows a schematic diagram of an electronic device provided in some embodiments of the present disclosure. As Figure 7 shown, the electronic device 7 includes: a processor 700, a memory 701, a bus 702, and a communication interface 707, and the processor 700, the communication interface 707, and the memory 701 are connected through the bus 702; a computer program that can run on the processor 700 is stored in the memory 701, and when the processor 700 runs the computer program, it executes the pathological target area positioning method based on time-domain interference stimulation provided in the foregoing embodiments of the present disclosure.

[0168] Among them, the memory 701 may include high-speed random access memory (RAM), and may also include non-volatile memory, such as at least one disk memory. The communication connection between this system network element and at least one other network element is realized through at least one communication interface 707 (which can be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. can be used.

[0169] The bus 702 can be an ISA bus, a PCI bus, an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. Among them, the memory 701 is used to store programs. After receiving the execution instruction, the processor 700 executes the program. The method for pathological target area localization based on time-domain interference stimulation disclosed in the foregoing embodiments can be applied to the processor 700 or implemented by the processor 700.

[0170] The processor 700 may be an integrated circuit chip with signal processing capabilities. In the implementation process, the steps of the above method can be completed by the integrated logic circuit in hardware or instructions in software form in the processor 700. The above-mentioned processor 700 can be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present disclosure. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present disclosure can be directly embodied as being executed and completed by a hardware decoding processor, or executed and completed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory 701, and the processor 700 reads the information in the memory 701 and combines its hardware to complete the steps of the above method.

[0171] The electronic device provided by the embodiments of the present disclosure and the method for pathological target area localization based on time-domain interference stimulation provided by the embodiments of the present disclosure are based on the same inventive concept and have the same beneficial effects as the methods adopted, run, or implemented by them.

[0172] The embodiments of the present disclosure also provide a computer-readable storage medium corresponding to the method for pathological target area localization based on time-domain interference stimulation provided in the foregoing embodiments. Please refer to Figure 8 , which shows that the computer-readable storage medium is an optical disc 70, on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it will execute the method for pathological target area localization based on time-domain interference stimulation provided in any of the foregoing embodiments.

[0173] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory, or other optical and magnetic storage media, which will not be elaborated here one by one.

[0174] The computer-readable storage medium provided in the above embodiments of the present disclosure and the method for pathological target area localization based on time-domain interference stimulation provided in the embodiments of the present disclosure are based on the same inventive concept and have the same beneficial effects as the methods adopted, run, or implemented by the application programs stored therein.

[0175] It should be noted that:

[0176] In the specification provided here, a large number of specific details are described. However, it can be understood that the embodiments of the present disclosure can be practiced without these specific details. In some instances, well-known structures and technologies are not shown in detail so as not to obscure the understanding of this specification.

[0177] Similarly, it should be understood that, in order to streamline the present disclosure and help understand one or more of the various inventive aspects, in the foregoing description of the exemplary embodiments of the present disclosure, the various features of the present disclosure are sometimes grouped together into a single embodiment, figure, or description thereof. However, the disclosed method should not be construed as reflecting the following schematic: that the claimed present disclosure requires more features than those expressly recited in each claim. Rather, as reflected in the following claims, the inventive aspects lie in less than all the features of the single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into the detailed description, where each claim itself serves as a separate embodiment of the present disclosure.

[0178] In addition, those skilled in the art will understand that although some of the embodiments described herein include certain features included in other embodiments rather than other features, the combination of features of different embodiments is meant to be within the scope of the present disclosure and forms different embodiments. For example, in the following claims, any one of the claimed embodiments can be used in any combination.

[0179] As described above, the above are only the preferred specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present disclosure should be covered by the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be subject to the protection scope of the claims.

Claims

1. A pathological target area positioning method based on time domain interferometric stimulation, characterized in that: The method comprises Obtaining the patient's electroencephalogram data, functional imaging data, and structural imaging data; generating a spatial distribution map according to the electroencephalogram data and the structural image data; The spatial distribution map reflects the activity intensity of different brain regions; Performing registration processing on the spatial distribution map and the functional imaging data to obtain the registered spatial distribution map and the functional imaging data; the spatial positions of the registered spatial distribution map and the functional imaging data match; fusing the spatial distribution map after the registration processing with the functional imaging data to generate a comprehensive brain function map; By performing phase synchronization analysis on the comprehensive brain function map, the phase synchronization between different brain regions is evaluated, and the brain regions whose phase synchronization is greater than a preset threshold and are active in cognitive tasks are identified as pathological target regions.

2. The method according to claim 1, characterized in that Generating a spatial distribution map according to the electroencephalogram data and the structural image data, including: Constructing a head model of the patient according to the structural image data; the head model is used to simulate the scalp conduction path of the source signal; Calculating a lead matrix based on the head model; the lead matrix is ​​used to characterize the spatial relationship between brain sources and electrodes; The spatial distribution map is generated according to the lead matrix and the electroencephalogram data.

3. The method according to claim 1 or 2, characterized in that: The spatial distribution map after the registration processing and the functional imaging data are fused to generate a comprehensive brain function map, including: Performing power spectral density analysis on the spatial distribution map after registration processing to determine the power of different frequency bands, and identifying the active frequency bands in the cognitive task according to the power of the different frequency bands; Calculate the correlation coefficient between the power in the specific brain region in the registered spatial distribution map and the activation intensity of the specific brain region in the registered functional imaging data; The power and activation intensity in the specific brain area whose correlation coefficient is greater than a preset coefficient are combined to generate the comprehensive brain function map.

4. The method according to claim 1 or 2, characterized in that: After screening out brain regions whose phase synchronization is greater than a preset threshold and are active in cognitive tasks as pathological target regions, the method further includes: Selecting a target electrode layout corresponding to the pathological target area from a plurality of electrode layouts; the target electrode layout is an electrode layout in which the electric field has the highest concentration in the pathological target area; generating a low-frequency envelope signal based on the EEG characteristics of the patient; The target waveform type was selected based on the patient’s cognitive needs; A treatment plan is generated according to the layout parameters of the target electrode layout, the low-frequency envelope signal and the target waveform type; the treatment plan is used to regulate the pathological target area.

5. The method according to claim 4, characterized in that Generating a low-frequency envelope signal based on the patient's EEG characteristics, including: Determining a low-frequency intervention frequency according to the EEG characteristics; Selecting two high-frequency signals by the low-frequency intervention frequency; the frequency difference between the two high-frequency signals is equal to the low-frequency intervention frequency; The low-frequency envelope signal is generated according to the two high-frequency signals; and the low-frequency envelope signal acts on the pathological target area.

6. The method according to claim 4, characterized in that The target waveform type is selected based on the patient's cognitive needs, including: When the cognitive requirement is phase synchronization adjustment, the target waveform type is a sine wave; When the cognitive demand is phase edge stimulation, the target waveform type is a square wave.

7. The method according to claim 4, characterized in that The method further comprises: In the process of regulating the pathological target area through the treatment plan, obtaining the patient's current EEG data in real time; Determine whether the treatment plan achieves the expected effect based on the current EEG data; If the treatment plan does not achieve the expected effect, the treatment plan is adaptively adjusted, and the pathological target area is re-regulated by the adaptively adjusted treatment plan.

8. A pathological target area positioning system based on time domain interferometric stimulation, characterized in that: The system comprises: A data acquisition module, used to obtain the patient's electroencephalogram data, functional imaging data, and structural imaging data; A spatial distribution map generating module, used to generate a spatial distribution map according to the electroencephalogram data and the structural image data; the spatial distribution map reflects the activity intensity of different brain regions; A registration module is used to perform registration processing on the spatial distribution map and the functional imaging data to obtain the spatial distribution map and the functional imaging data after the registration processing; the spatial positions of the spatial distribution map and the functional imaging data after the registration processing are matched; A data fusion module, used for fusing the spatial distribution map after the registration processing with the functional imaging data to generate a comprehensive brain function map; The pathological target area determination module is used to evaluate the phase synchronization between different brain regions by performing phase synchronization analysis on the comprehensive brain function map, and determine the brain region whose phase synchronization is greater than a preset threshold and is active in cognitive tasks as the pathological target area.

9. The system according to claim 8, characterized in that The system further comprises: An electrode layout determination module, used to select a target electrode layout corresponding to the pathological target area from multiple electrode layouts; the target electrode layout is an electrode layout in which the electric field has the highest concentration in the pathological target area; A treatment plan generating module, used for generating a treatment plan according to the target electrode layout and the pathological target area; The feedback control module is used to adaptively adjust the stimulation parameters of the treatment plan according to the EEG data obtained in real time.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 7.

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