Non-intrusive sleep aided improvement method and non-intrusive sleep aided improvement system

Abstract

The invention discloses a non-invasive sleep aided improvement method and system, and belongs to the technical field of biomedical engineering.The method specifically comprises the steps that static data and dynamic data are collected, a preliminary three-dimensional grid model is generated according to the collected data, a function connection matrix and a dynamic function connection matrix are constructed, and an individualized three-dimensional grid model is generated; according to the individualized three-dimensional grid model, selecting a stimulation area, generating an electrical stimulation signal, determining stimulation parameters, simulating nerve response of a target area in combination with a machine learning model, screening out an optimal stimulation parameter combination, and applying the electrical stimulation signal according to the optimal stimulation parameter combination, using the optimal stimulation parameter combination to assist in improving the sleep of the user; according to the method, the individualized three-dimensional grid model is constructed, the stimulation parameters are optimized in real time, an individualized stimulation parameter combination scheme is formed, and the stability of electroencephalogram signals is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of biomedical engineering, and specifically relates to a non-invasive sleep assistance improvement method and system. Background Art

[0002] The sleep process of humans is closely related to electroencephalogram (EEG) activities. EEG waves are electrical signals that reflect the synchronous electrical activities of brain neurons. Their frequency, waveform, and stability are significantly correlated with the sleep state. When the EEG signals show abnormal fluctuations or unstable states, they usually interfere with the normal sleep onset process and sleep quality.

[0003] To achieve stable regulation of brain electrical signals, the existing technical means are divided into two major categories: invasive and non-invasive. Although invasive methods can achieve precise intervention in nerve signals, they have problems such as complex operations and high risks. Non-invasive methods, on the other hand, have higher safety and acceptability.

[0004] Common non-invasive techniques include using means such as sound, light stimulation, weak electric fields, and magnetic fields for guidance or external regulation. However, these methods generally have the following deficiencies: First, the feedback mechanism is imperfect, and it is impossible to accurately and real-time obtain changes in electrical signals, thus affecting the regulation effect. Second, the adaptability of the regulation signals is poor, and it is difficult to effectively respond to specific electrical signal abnormalities. Third, it is impossible to form dynamic monitoring and real-time optimization of electrical signals, resulting in deviations in the stable regulation of electrical signals. Summary of the Invention

[0005] Aiming at the deficiencies of the existing technology, the present invention proposes a non-invasive sleep assistance improvement method and system, which performs personalized adjustment in combination with individual brain characteristics, and uses an electric current with a specific frequency, intensity, and waveform to precisely stimulate the target brain region through a set of non-invasive electrode devices to optimize the stimulation depth and effect.

[0006] To achieve the above object, the present invention provides the following technical solutions: A non-invasive sleep assistance improvement method, comprising: Collecting static data and dynamic data, generating a preliminary three-dimensional grid model based on the collected data, constructing a functional connectivity matrix and a dynamic functional connectivity matrix, generating an individualized three-dimensional grid model. The dynamic functional connectivity matrix encodes task events as time series, dynamically calculates the information transfer direction and intensity through a modulation function, and shares task modulation parameters at the population level; Selecting a stimulation region according to the individualized three-dimensional grid model, generating an electrical stimulation signal, and determining stimulation parameters; Combining with a machine learning model to simulate the neural response of the target region, screening out the optimal combination of stimulation parameters, and using the optimal combination of stimulation parameters to assist in improving the user's sleep.

[0007] Specifically, the generation of the individualized three-dimensional grid model includes: Construct a surface topology map of the neural signal activity region based on static data and dynamic data, and identify and segment the surface topology map of the neural signal activity region; Calculate the morphological parameters of the neural signal activity region and generate a preliminary three-dimensional grid model; Construct a dynamic functional connection matrix to generate an individualized three-dimensional grid model.

[0008] Specifically, the construction of the dynamic functional connection matrix to generate an individualized three-dimensional grid model includes: Through phase synchrony analysis, calculate the temporal correlation of the blood oxygen signals in different neural signal activity regions to construct a functional connection matrix; Based on time-frequency analysis, extract the power changes in the task-related frequency bands, identify the task-induced neural oscillation patterns, encode the task events as time series through task-state-driven dynamic connection weight adjustment, dynamically calculate the information transfer direction and intensity between neural signal activity regions through a modulation function, and share the task modulation parameters at the population level to generate a dynamic connection weight matrix; Integrate the structural features and functional connection strength, and analyze the structure-function coupling relationship; According to the functional connection matrix, dynamic connection weight matrix, and structure-function coupling relationship, construct an individualized three-dimensional grid model, including: nodes, edges, and dynamic markers.

[0009] Specifically, the sharing of the task modulation parameters includes: task event-related parameters and modulation function parameters.

[0010] Specifically, the stimulation parameters include: current intensity, synchronous phase, stimulation frequency, waveform type.

[0011] Specifically, the simulation of the neural response in the target region by combining a machine learning model and screening out the optimal stimulation parameter combination includes: Extract the features of the individualized three-dimensional grid model, including: static features and dynamic features, and establish a feature mapping relationship; According to the feature mapping relationship, use a numerical simulation or a neural network model based on physical modeling to construct a neural response simulation model of the target region; Use a machine learning model to optimize and screen the stimulation parameters to obtain a first stimulation parameter combination; Input the first stimulation parameter combination into the neural response simulation model for simulation, simulate the neural activity in the target region under the first stimulation parameter combination, and obtain simulation results, including: neuron excitability, neural synchrony, and therapeutic effect; Evaluate the simulation results. According to the simulation evaluation results, adjust the stimulation parameters. If the simulation evaluation results do not meet the expected effects, re-screen the combination of stimulation parameters through a machine learning model and conduct simulations until the optimal combination of stimulation parameters is found.

[0012] Specifically, the machine learning model includes: CNN, SVM.

[0013] A non-invasive sleep assistance improvement system for implementing the described non-invasive sleep assistance improvement method, including: a grid model establishment module, a signal generation module, an optimization and screening module, and a monitoring and feedback module; The grid model establishment module is used to collect static data and dynamic data, generate a preliminary three-dimensional grid model based on the collected data, construct a functional connectivity matrix and a dynamic functional connectivity matrix, and generate an individualized three-dimensional grid model. The dynamic functional connectivity matrix encodes task events as time series, dynamically calculates the information transfer direction and intensity through a modulation function, and shares task modulation parameters at the population level; The signal generation module is used to select a stimulation area according to the individualized three-dimensional grid model, generate an electrical stimulation signal, and determine the stimulation parameters; The optimization and screening module is used to simulate the neural response of the target area in combination with a machine learning model, screen out the optimal combination of stimulation parameters, and use the optimal combination of stimulation parameters to assist in improving the user's sleep.

[0014] Specifically, the grid model establishment module includes: a data collection unit, a preliminary grid model establishment unit, and a grid model establishment unit; The data collection unit is used to collect static data and dynamic data; The preliminary grid model establishment unit is used to calculate the morphological parameters of the neural signal activity area and generate a preliminary three-dimensional grid model according to the morphological parameters of the neural signal activity area; The grid model establishment unit is used to construct a functional connectivity matrix and a dynamic functional connectivity matrix, and combine with the preliminary three-dimensional grid model to generate an individualized three-dimensional grid model.

[0015] Specifically, the grid model establishment unit includes: a functional connectivity subunit, a connection weight subunit, a coupling analysis subunit, and a grid model establishment subunit; The functional connectivity subunit is used to calculate the temporal correlation of the blood oxygen signals in different neural signal activity areas through phase synchrony analysis and construct a functional connectivity matrix; The connection weight subunit is used to encode task events into time series through task-state-driven dynamic connection weight adjustment, dynamically calculate the information transfer direction and intensity of the neural signal activity region through a modulation function, and share task modulation parameters at the population level to generate a dynamic connection weight matrix; The coupling analysis subunit is used to integrate structural features and functional connection strength and analyze the structural-functional coupling relationship; The grid model establishment subunit is used to construct an individualized three-dimensional grid model according to the functional connection matrix, the dynamic connection weight matrix, and the structural-functional coupling relationship.

[0016] Compared with the prior art, the beneficial effects of the present invention are: 1. The present invention proposes a non-invasive sleep assistance improvement method. By constructing an individualized three-dimensional grid model, encoding and processing task events, and sharing task modulation parameters for individuals, it can more accurately capture how the connection strength between stimulation regions dynamically changes under different task conditions. And at the population level, it can improve the robustness of individualized estimation and at the same time improve the targeting accuracy.

[0017] 2. The present invention proposes a non-invasive sleep assistance improvement method. By using a machine learning model to simulate neural responses and real-time optimize stimulation parameters such as frequency, synchronization phase, intensity, waveform, etc., a personalized stimulation parameter combination scheme is formed, avoiding the one-size-fits-all method based on standard stimulation parameters in the prior art.

[0018] 3. The present invention proposes a non-invasive sleep assistance improvement method. By real-time collecting feedback from the stimulation region and applying a neural simulation model, the stimulation parameters can be adjusted at any time to avoid over-strong or over-weak stimulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flowchart of a non-invasive sleep assistance improvement method provided by the present invention; Figure 2 It is a flowchart of constructing an individualized three-dimensional grid model provided by the present invention; Figure 3 It is a flowchart of screening and simulating stimulation parameters provided by the present invention; Figure 4 It is an architecture diagram of a non-invasive sleep assistance improvement system provided by the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0020] The present application is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present application, but do not limit the present application in any form. It should be noted that, for those of ordinary skill in the art, several variations and improvements can also be made without departing from the concept of the present application. These all belong to the protection scope of the present application.

[0021] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0022] It should be noted that, if there is no conflict, the various features in the embodiments of the present application can be combined with each other and are all within the scope of protection of the present application. In addition, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flow chart, in some cases, the steps shown or described can be performed in a different order than the module division in the device or the order in the flow chart. In addition, the " The words "first", "second", "third", etc. do not limit the data and execution order, but only distinguish the same items or similar items with basically the same functions and effects.

[0023] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used in this specification and in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. The term "and / or" used in this specification includes any and all combinations of one or more of the related listed items.

[0024] Example 1 See also Figures 1-3 The present invention provides an embodiment: a non-invasive sleep aid improvement method, comprising the following specific steps: Step S1: Based on EEG and other technologies, static and dynamic data of patients are collected to construct an individualized three-dimensional grid model to accurately locate the target area and its functional connection path; High-density electroencephalogram (EEG): Use a 64-lead electrode cap with a sampling rate of 1000 Hz to record EEG activity during the task state, focusing on capturing event-related potentials (ERPs) and frequency band power (such as alpha waves, beta waves, and gamma waves); like Figure 2 As shown, in step S1, an individualized three-dimensional mesh model is constructed, and the specific steps are: Step S101: Construct a surface topology map of the neural signal activity region based on the collected static and dynamic data, and identify and segment the surface topology map of the neural signal activity region, including the boundaries of key brain regions such as the prefrontal cortex, motor cortex, and hippocampus; Step S102: Calculate morphological parameters such as the volume, surface area, and cortical thickness of the neural signal activity region, and generate a preliminary three-dimensional mesh model; Step S103: Construct a dynamic functional connection matrix and generate an individualized three-dimensional mesh model.

[0025] The specific steps of Step S103 are as follows: Step S1031: Through phase synchrony analysis, calculate the temporal correlation of the blood oxygen signal in different neural signal activity regions, and construct a functional connection matrix; In this embodiment, collect blood oxygenation level-dependent (BOLD) signal data of the brain and perform preprocessing, including: temporal correction, head motion correction, spatial normalization, and filtering, etc. Perform time-frequency analysis on the preprocessed blood oxygen signal, convert the blood oxygen signal in the time domain to the time-frequency domain, extract the phase information of each brain region signal at different frequencies, calculate the instantaneous phase of the blood oxygen signal in each brain region at each time point and specific frequency, for each pair of brain regions, calculate the phase synchrony index between them, and the phase synchrony index includes the phase locking value (PLV), coherence, etc. According to the calculated phase synchrony index, construct a functional connection matrix, where the rows and columns of the matrix correspond to different brain regions, and the matrix elements represent the functional connection strength between brain regions, that is, the value of the phase synchrony index; Step S1032: Based on time-frequency analysis, extract the power changes in the task-related frequency bands (such as theta wave 4 - 8 Hz, gamma wave 30 - 80 Hz), identify the task-induced neural oscillation patterns, encode the task events as a time series through task-state-driven dynamic connection weight adjustment, dynamically calculate the information transfer direction and strength between brain regions through a modulation function, and share the task modulation parameters at the population level to generate a dynamic connection weight matrix; The shared task modulation parameters include: task time-related parameters, including stimulus start time parameters and response time parameters, etc., and modulation function parameters. The sharing method: divide the parameters into different levels through a hierarchical Bayesian model, and the task modulation parameters are divided into individual-level parameters and population-level parameters. The population-level parameters are common parameters shared by all individuals, and the individual-level parameters are related to the specific characteristics of each individual but are constrained by the population-level parameters.

[0026] In this embodiment, according to the result of power change, the neural oscillation patterns in different brain regions and frequency bands are classified. For example, if the theta wave power in a certain brain region significantly increases during task execution, it may indicate that this brain region is involved in cognitive processes such as task-related memory and attention; while the increase in gamma wave power may be related to the integration and processing speed of information. Traditional dynamic causal modeling is based on prior knowledge and the results of neural oscillation pattern analysis. Although it can describe the effective connections between brain regions, it has certain limitations in dealing with connection changes related to tasks. This application improves traditional dynamic causal modeling. By encoding task-related events as time series, perceiving the process and time characteristics of the task, and using a modulation function (such as the Sigmoid function) to achieve dynamic adjustment of connection weights. At the population level, task modulation parameters are shared among different individuals. Although there are individual differences, there are also some common task modulation patterns. By sharing these parameters, the information of the population can be used to provide additional constraints and support for the parameter estimation of each individual. Benefits: 1) It can more accurately capture how the connection strength between brain regions in the brain network dynamically changes under different task conditions; 2) And at the population level, it can improve the robustness of individual estimation. For a single individual, its data will be affected by factors such as noise and measurement errors, resulting in inaccurate parameter estimation. By sharing parameters at the population level and integrating the information of the population into the individual parameter estimation, the uncertainty of individual data is reduced, and a more reliable and stable individual parameter estimation result is obtained.

[0027] Exemplary: In an experiment involving multiple subjects, when different subjects perform a memory task, there is a certain similarity in the task modulation patterns between the brain regions related to memory in the brain. When there is noise in the data of a certain individual, its individual parameter estimation will show large fluctuations, but with the help of the task modulation parameters shared by the population, its individual parameter estimation is corrected and optimized to make it closer to the true value.

[0028] Construct a dynamic causal model with improved multiple brain regions. Each brain region in the model serves as a node, and the connections between nodes represent the information transmission paths between brain regions. The direction of information transmission between brain regions is determined according to the positive and negative values of the connection parameters in the model. A positive connection parameter indicates that the previous brain region promotes the next brain region (information is transmitted from the former to the latter), and a negative connection parameter indicates an inhibitory effect. The absolute value of the connection parameter reflects the intensity of information transmission. The larger the absolute value, the stronger the information transmission between brain regions. Taking brain regions as rows and columns, construct a dynamic connection weight matrix based on the information transmission intensity between brain regions calculated by the model. The elements in the matrix represent the connection weights between the corresponding brain regions, that is, the information transmission intensity. Since neural activities change dynamically over time, repeat the above analysis process at different time points to update the connection weight matrix, thereby obtaining a sequence of connection weight matrices reflecting the dynamic changes of the brain network. Step S1033: Integrate structural features (such as the volume of the neural signal activity region) and functional connection strength, and analyze the structure-function coupling relationship. In this embodiment, the structure-function coupling relationship is analyzed through an improved Bayesian network model for target priority ranking. In the study of the brain network, the structural feature variables are: the connection strength, connection length, and brain region volume between brain regions, etc. The functional connection strength is obtained by calculating the functional connection matrix and the dynamic connection weight matrix. Step S1034: Construct an individualized three-dimensional grid model according to the functional connection matrix, the dynamic connection weight matrix, and the structure-function coupling relationship, including: nodes, edges, and dynamic markers. The nodes are the segmented brain regions, and the size of the nodes represents their functional connection degree; the edges are the connection strength between brain regions, and the edge width and color gradient (such as red - strong, blue - weak) represent the connection weight; the brain regions significantly activated in the task state are highlighted by flashing.

[0029] In this embodiment, the traditional brain network topological atlas only describes functional connections. This application quantifies the interaction relationship between brain region morphological features and functional connections through a Bayesian network, providing a dual basis for individualized stimulation target selection.

[0030] Step S2: Select a stimulation region according to the individualized three-dimensional grid model, generate an electrical stimulation signal, and determine the stimulation parameters. The stimulation parameters in step S2 include: current intensity, synchronous phase, stimulation frequency, waveform type. The current intensity directly determines the intensity of the stimulation and its impact on neural activities. For transcranial alternating current stimulation (tACS), the current intensity is usually between 1 mA and 3 mA. The stimulation frequency determines the activation mode and pattern of neurons. The stimulation frequencies are divided into: low-frequency stimulation (1 Hz - 10 Hz), which is commonly used to promote neural plasticity or increase the activation degree of brain regions, and is suitable for the treatment of chronic neurological diseases such as depression and cognitive impairment; high-frequency stimulation (20 Hz - 100 Hz and above), which is used to enhance the synchronous activity of neurons and is suitable for the treatment of movement disorders (such as Parkinson's disease) or the improvement of anxiety symptoms; and frequency modulation, which uses a mixed stimulation of specific frequencies. For example, the combination of low-frequency and high-frequency stimulation can produce a more complex neural response pattern and is used for the multi-dimensional regulation of symptom improvement; The waveform types are divided into: sine wave, which regulates the neural activity of brain regions through a steady current change and is used to enhance neural plasticity; square wave, which produces a stronger pulse effect and triggers more significant neural excitation, and is suitable for treatments that require rapid and short-term effects; triangular wave, which has a gradual change effect and can slowly activate neurons while effectively avoiding over-stimulation, and is suitable for the treatment of some chronic diseases such as depression or chronic anxiety; Step S3: Combine a machine learning model to simulate the neural response of the target area, screen the optimal combination of stimulation parameters, and use the optimal combination of stimulation parameters to assist in improving the user's sleep; Such as Figure 3 As shown, the specific steps of step S3 are as follows: Step S301: Extract the features of the individualized three-dimensional mesh model, including: static features and dynamic features, and establish a feature mapping relationship; Specifically, the static features are the features of the functional connection matrix, including: structural connection features, node attribute features, and network topology features, etc.; the dynamic features are the features of the dynamic connection weight matrix, including: the time-varying characteristics of functional connections and state transition features, etc.; the disease labels include: clinical diagnosis information and biomarker information; for high-dimensional features, dimensionality reduction processing is required, calculate the correlation between brain network features and disease labels, screen out the features significantly related to the disease, and then establish a disease-brain network feature mapping model through a machine learning algorithm to obtain the feature mapping relationship; Step S302: According to the feature mapping relationship, use numerical simulation or a neural network model based on physical modeling (such as a model based on biological neurons, a population neuron model, etc.) to construct a neural response simulation model of the target area; The core of the model is to be able to reflect the influence of different stimulation parameters (frequency, intensity, waveform, etc.) on neuron activity and simulate the excitation and inhibition processes of neurons; Step S303: Use a machine learning model to optimize and screen the stimulation parameters to obtain the first combination of stimulation parameters; The machine learning models in step S303 include: CNN, SVM, etc.; Step S304: Input the first stimulation parameter combination into the neural response simulation model for simulation to simulate the neural activities in the target brain region under the first stimulation parameter combination, and obtain simulation results, including: neuron excitation degree, neural synchrony, and curative effect; Step S305: Evaluate the simulation results, and further adjust the stimulation parameters according to the simulation evaluation results. If the simulation evaluation results do not meet the expected effects, return to Step S303 to re-screen the stimulation parameter combination through the machine learning model until the optimal stimulation parameter combination is found.

[0031] Embodiment 2 Please refer to Figure 4 , another embodiment provided by the present invention: a non-invasive sleep assistance improvement system, including: a grid model establishment module, a signal generation module, an optimization and screening module, and a monitoring and feedback module; The grid model establishment module is used to collect static data and dynamic data, generate a preliminary three-dimensional grid model according to the collected data, construct a functional connection matrix and a dynamic functional connection matrix, and generate an individualized three-dimensional grid model. The dynamic functional connection matrix encodes task events as time series, dynamically calculates the information transfer direction and intensity through a modulation function, and shares task modulation parameters at the population level; The signal generation module is used to select a stimulation area according to the individualized three-dimensional grid model, generate an electrical stimulation signal, and determine the stimulation parameters; The optimization and screening module is used to simulate the neural response of the target area in combination with a machine learning model, screen out the optimal stimulation parameter combination, and use the optimal stimulation parameter combination to assist in improving the user's sleep.

[0032] The grid model establishment module includes: a data collection unit, a preliminary grid model establishment unit, and a grid model establishment unit; The data collection unit is used to collect static data and dynamic data; The preliminary grid model establishment unit is used to calculate the morphological parameters of the neural signal activity area and generate a preliminary three-dimensional grid model according to the morphological parameters of the neural signal activity area; The grid model establishment unit is used to construct a functional connection matrix and a dynamic functional connection matrix, and combine the preliminary three-dimensional grid model to generate an individualized three-dimensional grid model.

[0033] The grid model establishment unit includes: a functional connection subunit, a connection weight subunit, a coupling analysis subunit, and a grid model establishment subunit; The functional connection subunit is used to calculate the temporal correlation of the blood oxygen signals in different neural signal activity areas through phase synchrony analysis and construct a functional connection matrix; The connection weight subunit is configured to encode task events into a time series through task-state-driven dynamic connection weight adjustment, dynamically calculate the information transfer direction and intensity of the neural signal activity region through a modulation function, and share task modulation parameters at the population level to generate a dynamic connection weight matrix; The coupling analysis subunit is configured to integrate structural features and functional connection strength and analyze the structural-functional coupling relationship; The grid model establishment subunit is configured to construct an individualized three-dimensional grid model based on the functional connection matrix, the dynamic connection weight matrix, and the structural-functional coupling relationship.

[0034] In addition, parts of the above technical solutions provided in the embodiments of the present application that are consistent with the implementation principles of the corresponding technical solutions in the prior art are not described in detail to avoid excessive elaboration.

[0035] As described above in the specific embodiments, the purpose, technical solutions, and beneficial effects of the present invention have been further described in detail. It should be understood that the above are only specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A non-invasive sleep assistance improvement method, characterized in that, Comprising: Collecting static data and dynamic data, generating a preliminary three-dimensional grid model based on the collected data, constructing a functional connectivity matrix and a dynamic functional connectivity matrix, generating an individualized three-dimensional grid model, where the dynamic functional connectivity matrix encodes task events as time series, dynamically calculates the information transfer direction and intensity through a modulation function, and shares task modulation parameters at the population level; Selecting a stimulation area according to the individualized three-dimensional grid model, generating an electrical stimulation signal, and determining stimulation parameters; Combining a machine learning model to simulate the neural response of the target area, screening out the optimal combination of stimulation parameters, and using the optimal combination of stimulation parameters to assist in improving the user's sleep.

2. The non-invasive sleep assistance improvement method according to claim 1, characterized in that, The generating of the individualized three-dimensional grid model includes: Constructing a surface topology map of the neural signal activity area based on the static data and dynamic data, and identifying and segmenting the surface topology map of the neural signal activity area; Calculating the morphological parameters of the neural signal activity area and generating a preliminary three-dimensional grid model; Constructing a dynamic functional connectivity matrix and generating an individualized three-dimensional grid model.

3. The non-invasive sleep assistance improvement method according to claim 2, wherein The constructing of the dynamic functional connectivity matrix and generating the individualized three-dimensional grid model includes: Calculating the temporal correlation of the blood oxygen signals of different neural signal activity areas through phase synchrony analysis and constructing a functional connectivity matrix; Based on time-frequency analysis, extracting the power changes in the task-related frequency bands, identifying the task-induced neural oscillation patterns, encoding task events as time series through task-state-driven dynamic connection weight adjustment, dynamically calculating the information transfer direction and intensity between neural signal activity areas through a modulation function, and sharing task modulation parameters at the population level to generate a dynamic connection weight matrix; Integrating the structural features and functional connectivity strength and analyzing the structure-function coupling relationship; Constructing an individualized three-dimensional grid model according to the functional connectivity matrix, the dynamic connection weight matrix, and the structure-function coupling relationship, including: nodes, edges, and dynamic markers.

4. The non-invasive sleep assistance improvement method according to claim 3, characterized in that, The sharing of task modulation parameters includes: task event-related parameters and modulation function parameters.

5. The non-invasive sleep assistance improvement method according to claim 4, wherein, The stimulation parameters include: current intensity, synchronous phase, stimulation frequency, waveform type.

6. The non-invasive sleep assistance improvement method according to claim 5, wherein The combining of the machine learning model to simulate the neural response of the target area and screening out the optimal combination of stimulation parameters includes: Extracting the features of the individualized three-dimensional grid model, including: static features and dynamic features, and establishing a feature mapping relationship; According to the feature mapping relationship, using a numerical simulation or a neural network model based on physical modeling to construct a neural response simulation model of the target area; Using a machine learning model to optimize and screen the stimulation parameters to obtain a first combination of stimulation parameters, and the machine learning model includes: CNN, SVM; Inputting the first combination of stimulation parameters into the neural response simulation model for simulation, simulating the neural activity of the target area under the first combination of stimulation parameters, and obtaining simulation results, including: neuron excitability, neural synchrony, and therapeutic effect; Evaluating the simulation results, adjusting the stimulation parameters according to the simulation evaluation results. If the simulation evaluation results do not meet the expected effect, re-screen the combination of stimulation parameters through the machine learning model and conduct simulation until the optimal combination of stimulation parameters is found.

7. A non-invasive sleep assistance improvement system for implementing a non-invasive sleep assistance improvement method according to any one of claims 1-6, characterized in that, Comprising: Grid model establishment module, signal generation module, optimization and screening module, and monitoring and feedback module; The grid model establishment module is used to collect static data and dynamic data, generate a preliminary three-dimensional grid model based on the collected data, construct a functional connectivity matrix and a dynamic functional connectivity matrix, and generate an individualized three-dimensional grid model. The dynamic functional connectivity matrix encodes task events as time series, dynamically calculates the information transfer direction and intensity through a modulation function, and shares task modulation parameters at the population level; The signal generation module is used to select a stimulation area according to the individualized three-dimensional grid model, generate an electrical stimulation signal, and determine stimulation parameters; The optimization and screening module is used to simulate the neural response of the target area in combination with a machine learning model and screen out the optimal combination of stimulation parameters.

8. The non-invasive sleep assistance improvement system according to claim 7, characterized in that, The grid model establishment module includes: a data acquisition unit, a preliminary grid model establishment unit, and a grid model establishment unit; The data acquisition unit is used to collect static data and dynamic data; The preliminary grid model establishment unit is used to calculate the morphological parameters of the neural signal activity area and generate a preliminary three-dimensional grid model according to the morphological parameters of the neural signal activity area; The grid model establishment unit is used to construct a functional connectivity matrix and a dynamic functional connectivity matrix, and generate an individualized three-dimensional grid model in combination with the preliminary three-dimensional grid model.

9. The non-invasive sleep assistance improvement system according to claim 8, characterized in that, The grid model establishment unit includes: a functional connectivity subunit, a connection weight subunit, a coupling analysis subunit, and a grid model establishment subunit; The functional connectivity subunit is used to calculate the temporal correlation of blood oxygen signals in different neural signal activity areas through phase synchrony analysis and construct a functional connectivity matrix; The connection weight subunit is used to encode task events as time series through task-state-driven dynamic connection weight adjustment, dynamically calculate the information transfer direction and intensity of the neural signal activity area through a modulation function, and share task modulation parameters at the population level to generate a dynamic connection weight matrix; The coupling analysis subunit is used to integrate structural features and functional connection strength and analyze the structure-function coupling relationship; The grid model establishment subunit is used to construct an individualized three-dimensional grid model according to the functional connectivity matrix, the dynamic connection weight matrix, and the structure-function coupling relationship.

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