Intelligently-controlled closed-loop transcranial magnetic stimulation system and method

Abstract

The invention relates to the technical field of medical detection equipment, and discloses an intelligently-controlled closed-loop transcranial magnetic stimulation system and method, and the method comprises the steps: collecting the nerve imaging data of a brain stimulation patient in real time, and recognizing the brain activity mode and the nerve signal transmission path of the brain stimulation patient; determining a key brain region of the brain stimulation patient, identifying stimulation point locations of the key brain region, and setting a personalized adjustment mode of the stimulation point locations; the self-adaptive control unit is used for identifying an attention mode of a brain stimulation patient, analyzing brain function expression of a key brain region under the attention mode and setting stimulation parameters; the stimulation effect and the attention change trend of the key brain area are analyzed, and a magnetic stimulation closed-loop feedback mechanism of the key brain area is set; and in combination with a magnetic stimulation closed-loop feedback mechanism, a personalized adjustment mechanism and a self-adaptive control unit, executing magnetic stimulation treatment on the brain stimulation patient. According to the invention, intelligent control of transcranial magnetic stimulation treatment can be realized, and the treatment effect of a patient is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical detection equipment, and in particular to an intelligently controlled closed-loop transcranial magnetic stimulation system and method. Background Art

[0002] Closed-loop transcranial magnetic stimulation refers to a neuroregulatory technology that can adjust stimulation parameters according to the real-time state of the brain. It optimizes the treatment effect by monitoring the brain's activity state in real time and adjusting the stimulation parameters and patterns according to the monitoring results. With the rapid development of neuroscience and brain-computer interface technology, therapeutic magnetic stimulation technology is gradually shifting from traditional open-loop fixed solutions to more flexible and efficient closed-loop systems. As an advanced treatment method, closed-loop transcranial magnetic stimulation systems have great potential in the treatment of brain diseases such as epilepsy and depression. However, in order to ensure the safety and effectiveness of treatment, precise control of transcranial magnetic stimulation is particularly important.

[0003] Traditional transcranial magnetic stimulation therapy mainly uses an open-loop transcranial magnetic stimulation method, which sends out stimulation signals at artificially set time points and then observes the brain's response to the stimulation. Therefore, it is impossible to adjust the stimulation parameters according to the real-time changes in brain activity, resulting in a mismatch between the stimulation parameters and the patient's current brain activity, affecting the patient's treatment effect. Summary of the invention

[0004] The present invention provides an intelligently controlled closed-loop transcranial magnetic stimulation system, the main purpose of which is to achieve intelligent control of transcranial magnetic stimulation treatment and improve the treatment effect of patients.

[0005] To achieve the above-mentioned purpose, the present invention provides an intelligently controlled closed-loop transcranial magnetic stimulation system, comprising: a neural pathway identification module, a parameter setting module, a point adjustment module, a parameter control module, a closed-loop feedback module and a magnetic stimulation processing module;

[0006] The neural pathway identification module is used to obtain the brain stimulation patient to be controlled and the corresponding disease type, collect the neural imaging data of the brain stimulation patient in real time, identify the brain activity pattern of the brain stimulation patient based on the neural imaging data, and identify the neural signal transmission path of the brain stimulation patient according to the brain activity pattern;

[0007] The parameter setting module is used to identify the associated neural network of the neural signal transmission path, determine the key brain area of ​​the brain stimulation patient according to the associated neural network, identify the stimulation points of the key brain area, and set the stimulation parameters of the brain stimulation patient according to the key brain area and the stimulation points;

[0008] The point adjustment module is used to identify the pathological manifestations of the brain stimulation patient based on the disease type, extract the personalized characteristics of the brain stimulation patient according to the pathological manifestations, and set the personalized adjustment mode of the stimulation point based on the personalized characteristics;

[0009] The parameter control module is used to extract the activity characteristics of the associated neural network, and identify the attention mode of the brain stimulation patient according to the activity characteristics, analyze the brain function performance of the key brain area under the attention mode, monitor the physiological state of the brain stimulation patient in real time, and set the adaptive control unit of the stimulation parameters based on the brain function performance and the physiological state;

[0010] The closed-loop feedback module is used to analyze the stimulation effect of the key brain area according to the personalized adjustment mechanism and the adaptive control unit, analyze the attention change trend of the key brain area based on the attention pattern, and set the magnetic stimulation closed-loop feedback mechanism of the key brain area according to the attention change trend and the stimulation effect;

[0011] The magnetic stimulation processing module is used to combine the magnetic stimulation closed-loop feedback mechanism, the personalized adjustment mechanism and the adaptive control unit to perform magnetic stimulation processing on the brain stimulation patient and obtain a magnetic stimulation processing result.

[0012] A closed-loop transcranial magnetic stimulation method with intelligent control, characterized in that the method comprises:

[0013] Acquire a brain stimulation patient to be controlled and the corresponding disease type, collect the neuroimaging data of the brain stimulation patient in real time, identify the brain activity pattern of the brain stimulation patient based on the neuroimaging data, and identify the neural signal transmission path of the brain stimulation patient according to the brain activity pattern;

[0014] Identify the associated neural network of the neural signal transmission path, determine the key brain area of ​​the brain stimulation patient according to the associated neural network, identify the stimulation points of the key brain area, and set the stimulation parameters of the brain stimulation patient according to the key brain area and the stimulation points;

[0015] Based on the disease type, identifying the pathological manifestations of the brain stimulation patient, extracting personalized characteristics of the brain stimulation patient according to the pathological manifestations, and setting a personalized adjustment mode of the stimulation point based on the personalized characteristics;

[0016] Extracting the activity features of the associative neural network, and identifying the attention mode of the brain stimulation patient according to the activity features, analyzing the brain function performance of the key brain area under the attention mode, monitoring the physiological state of the brain stimulation patient in real time, and setting the adaptive control unit of the stimulation parameters based on the brain function performance and the physiological state;

[0017] Analyzing the stimulation effect of the key brain area according to the personalized adjustment mechanism and the adaptive control unit, analyzing the attention change trend of the key brain area based on the attention pattern, and setting a magnetic stimulation closed-loop feedback mechanism for the key brain area according to the attention change trend and the stimulation effect;

[0018] In combination with the magnetic stimulation closed-loop feedback mechanism, the personalized adjustment mechanism and the adaptive control unit, the magnetic stimulation treatment of the brain stimulation patient is performed to obtain a magnetic stimulation treatment result.

[0019] The embodiment of the present invention can identify the current state of the patient's brain by identifying the brain activity pattern of the brain stimulation patient based on the neural imaging data, provide a basis for subsequent stimulation parameter adjustment, identify the neural signal transmission path of the brain stimulation patient, and ensure that the stimulation can reach the most effective site; further, the embodiment of the present invention can ensure the safety of treatment and avoid damage to important brain structures by determining the key brain areas of the brain stimulation patient according to the associative neural network and identifying the stimulation points of the key brain areas; the embodiment of the present invention can identify the personalized differences between patients by identifying the pathological manifestations of the brain stimulation patient based on the disease type, extract the personalized characteristics of the brain stimulation patient, set the personalized adjustment mode of the stimulation points, adapt to the patient's response changes, improve the patient's compliance with treatment, and achieve more humane and precise brain stimulation treatment; further, the embodiment of the present invention can evaluate the patient's concentration, distraction or transfer of attention by extracting the activity characteristics of the associative neural network and identifying the attention pattern of the brain stimulation patient according to the activity characteristics. brain activity, predict the patient's response to treatment, and accurately determine the brain area that needs to be stimulated, thereby realizing intelligent control of transcranial magnetic stimulation; the embodiment of the present invention sets the magnetic stimulation closed-loop feedback mechanism of the key brain area according to the attention change trend and the stimulation effect, and can adjust the parameters of transcranial magnetic stimulation (TMS) in real time according to the attention change trend and the stimulation effect, so that transcranial magnetic stimulation can better adapt to the dynamic changes of the brain, realize real-time adjustment of the attention network, reduce unnecessary side effects, and improve the safety of treatment, thereby improving cognitive function and treatment effect; further, the embodiment of the present invention performs magnetic stimulation treatment of the brain stimulation patient by combining the magnetic stimulation closed-loop feedback mechanism, the personalized adjustment mechanism and the adaptive control unit, and obtains the magnetic stimulation treatment result, which can automatically adjust the stimulation parameters according to the patient's real-time data and feedback, improve the convenience and efficiency of treatment, reduce unnecessary side effects, improve the safety of treatment, enhance the comfort of patients, and also ensure that transcranial magnetic stimulation treatment can achieve the expected effect, improve the repeatability of treatment, and save medical resources. Therefore, the present invention can realize intelligent control of transcranial magnetic stimulation treatment and improve the treatment effect of patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A functional module diagram of a closed-loop transcranial magnetic stimulation system with intelligent control provided by one embodiment of the present invention;

[0021] Figure 2 A schematic flow chart of a closed-loop transcranial magnetic stimulation method with intelligent control provided by one embodiment of the present invention;

[0022] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] In addition, the step sequence in the following method embodiments is only an example and not a strict limitation.

[0025] In fact, the server-side device deployed by the intelligently controlled closed-loop transcranial magnetic stimulation system may be composed of one or more devices. The above-mentioned intelligently controlled closed-loop transcranial magnetic stimulation system can be implemented as: a business instance, a virtual machine, and a hardware device. For example, the intelligently controlled closed-loop transcranial magnetic stimulation system can be implemented as a business instance deployed on one or more devices in a cloud node. In simple terms, the intelligently controlled closed-loop transcranial magnetic stimulation system can be understood as a software deployed on a cloud node, which is used to provide intelligently controlled closed-loop transcranial magnetic stimulation services to each user terminal. Alternatively, the intelligently controlled closed-loop transcranial magnetic stimulation system can also be implemented as a virtual machine deployed on one or more devices in a cloud node. The virtual machine is installed with application software for managing each user terminal. Alternatively, the intelligently controlled closed-loop transcranial magnetic stimulation system can also be implemented as a server consisting of many hardware devices of the same or different types, and one or more hardware devices are set to provide intelligently controlled closed-loop transcranial magnetic stimulation services to each user terminal.

[0026] In terms of implementation, the closed-loop transcranial magnetic stimulation system with intelligent control and the user end are adapted to each other. That is, the closed-loop transcranial magnetic stimulation system with intelligent control is an application installed on the cloud service platform, and the user end is a client that establishes a communication connection with the application; or the closed-loop transcranial magnetic stimulation system with intelligent control is implemented as a website, and the user end is implemented as a web page; or the closed-loop transcranial magnetic stimulation system with intelligent control is implemented as a cloud service platform, and the user end is implemented as a small program in an instant messaging application.

[0027] Reference Figure 1 , which is a functional module diagram of an intelligently controlled closed-loop transcranial magnetic stimulation system provided in one embodiment of the present invention.

[0028] The intelligently controlled closed-loop transcranial magnetic stimulation system 100 of the present invention can be set in a cloud server. In terms of implementation, it can be used as one or more service devices, or it can be installed as an application on the cloud (such as a server, server cluster, etc. of the intelligently controlled closed-loop transcranial magnetic stimulation), or it can also be developed as a website. According to the functions implemented, the intelligently controlled closed-loop transcranial magnetic stimulation system 100 includes a neural pathway identification module 101, a parameter setting module 102, a point adjustment module 103, a parameter control module 104, a closed-loop feedback module 105, and a magnetic stimulation processing module 106.

[0029] In the embodiment of the present invention, in the tracking of closed-loop transcranial magnetic stimulation based on intelligent control, each of the above modules can be independently implemented and called with other modules. The call here can be understood as a module that can connect multiple modules of another type and provide corresponding services to the multiple modules connected to it. In the closed-loop transcranial magnetic stimulation system with intelligent control provided by the embodiment of the present invention, the scope of application of the closed-loop transcranial magnetic stimulation architecture with intelligent control can be adjusted by adding modules and directly calling them without modifying the program code, so as to achieve cluster-type horizontal expansion, so as to achieve the purpose of quickly and flexibly expanding the closed-loop transcranial magnetic stimulation system with intelligent control. In practical applications, the above modules can be set in the same device or different devices, or they can be set in virtual devices, such as service instances in cloud servers.

[0030] In the following, in conjunction with specific embodiments, various components and specific workflows of the intelligently controlled closed-loop transcranial magnetic stimulation system are described respectively.

[0031] The neural pathway identification module 101 is used to obtain the brain stimulation patient to be controlled and the corresponding disease type, collect the neural imaging data of the brain stimulation patient in real time, identify the brain activity pattern of the brain stimulation patient based on the neural imaging data, and identify the neural signal transmission path of the brain stimulation patient according to the brain activity pattern.

[0032] The embodiments of the present invention can accurately identify patient information and improve the accuracy of treatment by obtaining brain stimulation patients to be controlled and their corresponding disease types. The brain stimulation patients refer to people who develop a series of symptoms after the brain is stimulated in various ways, and the disease type refers to the type of specific disease or disorder suffered by the patient, such as depression, schizophrenia, etc.

[0033] Furthermore, the embodiments of the present invention can provide data support for subsequent analysis of brain activity patterns by collecting neuroimaging data of the brain stimulation patient in real time. The neuroimaging data refers to images and information about brain structure and function obtained using various neuroimaging technologies.

[0034] Optionally, real-time collection of neuroimaging data of the brain stimulation patient may be accomplished using magnetic resonance imaging technology.

[0035] The embodiment of the present invention can identify the current state of the patient's brain by identifying the brain activity pattern of the brain stimulation patient based on the neuroimaging data, thereby providing a basis for subsequent adjustment of stimulation parameters. The brain activity pattern refers to a specific pattern of neuronal activity in the patient's brain under specific conditions or when performing a specific task, such as the brain's response pattern to stimulation.

[0036] As an embodiment of the present invention, identifying the brain activity pattern of the brain stimulation patient based on the neuroimaging data includes: analyzing the classified brain regions of the brain stimulation patient and their corresponding EEG signals based on the neuroimaging data; calculating the power spectral density of the EEG signal, and identifying the frequency characteristics of the EEG signal based on the power spectral density; performing time series analysis on the EEG signal to obtain time series analysis results; identifying the frequency response characteristics of the EEG signal based on the time series analysis results; determining the associated events corresponding to the frequency response characteristics; analyzing the functional changes of the classified brain regions based on the frequency response characteristics; and identifying the brain activity pattern of the brain stimulation patient in combination with the frequency characteristics, the response events and the functional changes.

[0037] Among them, the classified brain intervals refer to different brain areas divided according to the brain's anatomical structure or functional characteristics, the EEG signal refers to the brain's electrical activity recorded by electroencephalography and other technologies, the power spectral density refers to a statistic that describes the distribution of signal power at different frequencies, the frequency characteristics refer to the power distribution characteristics of EEG signals in different frequency bands, the time series analysis results refer to the results obtained after time series analysis of EEG signals, the frequency response characteristics refer to the frequency response of EEG signals to specific events or stimuli, such as event-related desynchronization, the associated events refer to external events associated with specific EEG signal frequency response characteristics, such as auditory stimulation, and the functional changes refer to the changes in the functional states of brain intervals obtained based on the frequency response characteristics analysis.

[0038] Optionally, the identification of the frequency characteristics of the EEG signal based on the power spectral density can be obtained by clustering analysis of the spectral data of the EEG signal, the determination of associated events corresponding to the frequency response characteristics can be achieved by event-related potentials (ERPs) analysis, and the analysis of functional changes between the classified brain regions based on the frequency response characteristics can be obtained by functional connectivity analysis.

[0039] In an optional embodiment of the present invention, the power spectral density of the EEG signal is calculated using the following formula:

[0040]

[0041] Among them, S(f) represents the power spectrum density of the EEG signal, b represents the starting point of the EEG signal, a represents the ending point of the EEG signal, x(v) represents the signal value of the EEG signal at time v, and e -j2πfv It means converting the time domain signal of EEG signal into frequency domain, and dv means the integral value of time v.

[0042] Furthermore, the embodiments of the present invention can identify the connection paths between brain regions by identifying the neural signal transmission pathways of the brain stimulation patient based on the brain activity pattern, thereby ensuring that the stimulation can reach the most effective sites. The neural signal transmission pathway refers to the pathway for transmitting signals between neurons in the brain, including the axons, synapses, nerve fiber bundles, etc. of neurons.

[0043] Optionally, the identification of the neural signal transmission pathway of the brain stimulation patient based on the brain activity pattern can be obtained by measuring the diffusion pattern of water molecules in brain tissue.

[0044] The parameter setting module 102 is used to identify the associated neural network of the neural signal transmission path, determine the key brain area of ​​the brain stimulation patient based on the associated neural network, and identify the stimulation points of the key brain area, and set the stimulation parameters of the brain stimulation patient based on the key brain area and the stimulation points.

[0045] The embodiment of the present invention can ensure that stimulation can be targeted at a specific neural network and improve the accuracy of treatment by identifying the associated neural network of the neural signal transmission path. The associated neural network refers to a group of neurons or specific brain areas that are interconnected and work together in the brain, such as nerve fibers, prefrontal cortex, cingulate gyrus, etc.

[0046] Optionally, the associated neural network identification of the neural signal transmission pathway can be obtained by neural tracing technology.

[0047] Furthermore, the embodiments of the present invention can ensure the safety of treatment and avoid damage to important brain structures by determining the key brain areas of the brain stimulation patient based on the associative neural network and identifying the stimulation points of the key brain areas. The key brain areas refer to specific areas in the brain that are directly related to specific functions, disease states or treatment goals, and the stimulation points refer to the specific locations where the stimulation device actually acts on the cerebral cortex during brain stimulation treatment.

[0048] Optionally, the identification of stimulation points in the key brain regions can be determined by a neuronavigation system.

[0049] As an embodiment of the present invention, determining the key brain areas of the brain stimulation patient based on the associative neural network includes: locating the brain area positions of the associative neural network, and analyzing the interactions of the associative neural network based on the brain area positions; detecting the cognitive response of the brain stimulation patient based on the interactions; analyzing the brain activity pattern of the brain stimulation patient based on the cognitive response; based on the brain activity pattern, analyzing the active brain areas of the brain stimulation patient under the cognitive response; identifying the key role of the active brain areas in the cognitive response; and determining the key brain areas of the brain stimulation patient based on the key role.

[0050] Among them, the brain area location refers to the physical location of the neural network in the brain, the interaction refers to the connection and communication relationship between different brain areas, the cognitive response refers to the individual's psychological and physiological response to a specific stimulus or task, including perception, attention, memory, etc., the brain activity pattern refers to the pattern of brain activity under a specific cognitive task or stimulus, the active brain area refers to the brain area that is significantly active under a specific cognitive task or stimulus, and the key role refers to the specific function and importance of the active brain area in the cognitive process, including information processing, decision making, emotion regulation, etc.

[0051] Optionally, the positioning of the brain regions of the associated neural network can be achieved using PET imaging technology, and based on the positions of the brain regions, the interaction analysis of the associated neural network can be obtained through functional connectivity analysis, and based on the interactions, the cognitive response detection of the brain stimulation patient can be achieved using cognitive games, such as action strategy games, and the identification of the key role of the active brain regions in the cognitive response can be obtained through behavioral tests, such as neuropsychological tests.

[0052] Furthermore, the embodiments of the present invention can accurately control the depth and range of stimulation by setting the stimulation parameters of the brain stimulation patient according to the key brain areas and the stimulation points, ensuring that only the target brain areas are stimulated, avoiding affecting surrounding tissues, and reducing discomfort or other side effects. The stimulation parameters refer to specific values ​​and conditions used to control and adjust the output of the stimulation device during brain stimulation treatment, such as stimulation intensity, stimulation frequency, etc.

[0053] Optionally, the stimulation parameter setting for the brain stimulation patient based on the key brain areas and the stimulation points can be achieved using a brain stimulation device, such as a transcranial magnetic stimulator.

[0054] The point adjustment module 103 is used to identify the pathological manifestations of the brain stimulation patient based on the disease type, extract the personalized characteristics of the brain stimulation patient according to the pathological manifestations, and set the personalized adjustment mode of the stimulation point based on the personalized characteristics.

[0055] The embodiment of the present invention can identify the pathological manifestations of the brain stimulation patient based on the disease type, thereby identifying the individual differences between patients and formulating a personalized stimulation plan. The pathological manifestations refer to the abnormal physiological and psychological characteristics of the patient caused by brain disease or injury, such as sensory disorders, mood swings, etc.

[0056] Optionally, the identification of pathological manifestations of the brain stimulation patient based on the disease type can be obtained through a comprehensive assessment method, such as behavioral and emotional assessment, genetic and gene testing, neuropsychological assessment, etc.

[0057] Furthermore, the embodiments of the present invention can formulate a more targeted treatment plan and improve the effectiveness of treatment by extracting the personalized characteristics of the brain stimulation patient based on the pathological manifestations. The personalized characteristics refer to the unique physiological, psychological, genetic and social characteristics of the brain stimulation patient.

[0058] As an embodiment of the present invention, the method of extracting personalized characteristics of the brain stimulation patient based on the pathological manifestations includes: identifying the pathological pattern of the brain stimulation patient based on the pathological manifestations; calculating the similarity of the brain stimulation patients based on the pathological pattern; grouping the brain stimulation patients according to the similarity to obtain grouped patients; collecting medical data of the grouped patients and screening out key attribute data of the medical data; and extracting personalized characteristics of the brain stimulation patient based on the key attribute data.

[0059] Among them, the pathological pattern refers to a specific pattern or feature combination identified from the patient's pathological manifestations, the similarity level refers to the similarity of pathological patterns between different patients, the grouped patients refers to a group of patients who are divided into the same group according to the degree of similarity, the medical data refers to all information related to the patient's health, such as treatment data, laboratory test data, etc., and the key attribute data refers to data that has a significant impact on the patient's pathological state and treatment response, screened from a wide range of medical data.

[0060] Optionally, based on the pathological manifestations, the pathological pattern recognition of the brain stimulation patients can be obtained through the scikit-learn tool, the key attribute data screening of the medical data can be achieved using data visualization tools, such as the Power BI tool, and the grouping of the brain stimulation patients can be obtained through a classification algorithm.

[0061] In an optional embodiment of the present invention, based on the pathological pattern, the similarity of the brain stimulation patients is calculated using the following formula:

[0062]

[0063] Among them, A represents the similarity degree of brain stimulation patients, n represents the total number of features of the pathological pattern corresponding to the brain stimulation patients, i represents the feature index within the pathological pattern, P represents the pathological pattern samples of brain stimulation patient P, and G represents the pathological pattern samples of brain stimulation patient G.

[0064] The embodiment of the present invention can adapt to the changes in patients' responses, improve patients' compliance with treatment, and achieve more humane and precise brain stimulation treatment by setting a personalized adjustment mode of the stimulation points based on the personalized characteristics. The personalized adjustment mode refers to a system that customizes the stimulation points in brain stimulation treatment according to the patient's specific physiological, psychological and pathological characteristics.

[0065] As an embodiment of the present invention, the personalized adjustment mode of the stimulation point is set based on the personalized characteristics, including: identifying a brain stimulation patient corresponding to the personalized characteristics; extracting electrophysiological characteristics and behavioral characteristics corresponding to the brain stimulation patient based on the personalized characteristics; identifying the electrophysiological activity of the brain stimulation patient according to the electrophysiological characteristics and the behavioral characteristics; extracting the active brain area corresponding to the brain stimulation patient based on the electrophysiological activity, and analyzing the response performance of the active brain area; setting the personalized adjustment mode of the stimulation point according to the active brain area and the response performance.

[0066] Among them, the electrophysiological characteristics refer to the characteristics of brain electrical activity or magnetic field changes measured by electrophysiological technology, such as the discharge pattern of neurons; the behavioral characteristics refer to the behavioral performance of patients during treatment; the electrophysiological activity refers to the electrophysiological response of the brain when performing specific tasks or receiving specific stimuli; the active brain area refers to the area of ​​the brain that is significantly active under the promotion of electrophysiological activity; the response performance refers to the response characteristics of the brain to specific stimuli or tasks, including the activity intensity of the active brain area, changes in activation patterns, dynamic interactions of neural networks, etc.

[0067] Optionally, the electrophysiological feature extraction corresponding to the brain stimulation patient based on the personalized features can be obtained through cortical electroencephalogram, the behavioral feature extraction corresponding to the brain stimulation patient based on the personalized features can be achieved by evaluating the patient's behavior and cognitive function through neuropsychological testing, the electrophysiological activity identification of the brain stimulation patient based on the electrophysiological features and the behavioral features can be obtained by recording the electrical activity of the brain, and the response performance analysis of the active brain areas can be achieved using a behavioral rating scale.

[0068] The parameter control module 104 is used to extract the activity characteristics of the associated neural network, and identify the attention mode of the brain stimulation patient based on the activity characteristics, analyze the brain function performance of the key brain area under the attention mode, monitor the physiological state of the brain stimulation patient in real time, and set the adaptive control unit of the stimulation parameters based on the brain function performance and the physiological state.

[0069] The embodiment of the present invention extracts the activity features of the associative neural network and identifies the attention pattern of the brain stimulation patient based on the activity features, thereby evaluating the patient's brain activity when the patient's attention is concentrated, distracted or shifted, and predicting the patient's response to treatment. The activity features refer to the activity characteristics of the brain's neural network when performing specific tasks or being in a specific state, such as activity intensity, activation pattern, etc., and the attention pattern refers to the activity pattern of the brain when processing attention-related tasks.

[0070] Optionally, the activity feature extraction of the associative neural network can be achieved using magnetoencephalography (MEG) technology.

[0071] As an embodiment of the present invention, identifying the attention pattern of the brain stimulation patient according to the activity characteristics includes: extracting the brain neural network corresponding to the activity characteristics; collecting activity data of the brain neural network according to the activity characteristics; analyzing the brain functional areas of the brain stimulation patient based on the activity data; identifying the response functions of the brain functional areas, and analyzing the induced behaviors corresponding to the response functions; identifying the activation pattern of the brain neural network based on the response functions and the induced behaviors; extracting the attention characteristics of the brain stimulation patient according to the activation patterns; and identifying the attention pattern of the brain stimulation patient based on the attention characteristics.

[0072] Among them, the brain neural network refers to the complex network structure formed by the interconnected neurons in the brain, the activity data refers to the data of brain activity collected by brain imaging technology, the brain functional area refers to the area in the brain responsible for specific functions, such as the visual cortex is responsible for processing visual information, and the motor cortex is responsible for controlling body movements, the response function refers to the ability of the brain functional area to respond to specific stimuli or tasks, such as when a visual stimulus appears, the response function of the visual cortex will be reflected in the processing and reaction to this stimulus, the induced behavior refers to the external behavioral manifestation caused by the activity of a specific area of ​​the brain, such as the behavioral manifestation of facial muscles, the activation pattern refers to the area activated in the brain neural network and its activity pattern under a specific task or stimulus, and the attention feature refers to the feature of the brain activity of an individual when he or she is concentrating.

[0073] Optionally, the induced behavior analysis corresponding to the response function can be obtained by designing cognitive tasks, such as attention tasks, language tasks, etc., and the activation pattern recognition of the brain neural network based on the response function and the induced behavior can be achieved using functional magnetic resonance imaging. According to the activation pattern, the attention feature extraction of the brain stimulation patient can be obtained through the empirical mode decomposition method, and based on the attention feature, the attention pattern recognition of the brain stimulation patient can be achieved using a convolutional neural network combined with an attention mechanism.

[0074] Furthermore, the embodiments of the present invention can accurately determine the brain area that needs to be stimulated by analyzing the brain function performance of the key brain area in the attention mode, thereby realizing intelligent control of transcranial magnetic stimulation, improving the effect of transcranial magnetic stimulation treatment, and reducing adverse effects on surrounding tissues. The brain function performance refers to the activity performance of the brain when performing specific tasks or functions.

[0075] Optionally, the analysis of brain function performance of the key brain areas in the attention mode can be obtained by statistical analysis methods, such as multiple comparisons method.

[0076] The embodiments of the present invention can perform adaptive adjustments based on the real-time physiological changes of the patient by monitoring the physiological state of the brain stimulation patient in real time, thereby reducing the extension of the treatment cycle caused by poor regulation effect and further improving the treatment effect. The physiological state refers to the physiological changes and adjustments that occur in the human body to adapt to the environment and life needs, including the influence of multiple factors such as metabolism, nerves, and endocrine.

[0077] Optionally, the real-time monitoring of the physiological state of the brain stimulation patient can be obtained by a physiological multichannel recorder.

[0078] Furthermore, the embodiments of the present invention can accurately determine the optimal timing and parameters of stimulation by setting the adaptive control unit of the stimulation parameters based on the brain function performance and the physiological state, thereby improving the accuracy of treatment. At the same time, it can also improve the flexibility and effectiveness of treatment by dynamically adjusting the stimulation parameters. The adaptive control unit refers to a control system that can automatically adjust its behavior or output according to real-time feedback information.

[0079] As an embodiment of the present invention, the method of setting the adaptive control unit of the stimulation parameters based on the brain function performance and the physiological state includes: identifying the treatment patient corresponding to the stimulation parameters; analyzing the current health status of the treatment patient based on the brain function performance and the physiological state; setting the control unit of the stimulation parameters according to the current health status; defining the control rules of the stimulation parameters based on the control unit; setting the safety range of the stimulation parameters according to the control rules; monitoring the parameter changes of the stimulation parameters in real time based on the safety range; setting the abnormal feedback nodes of the stimulation parameters according to the parameter changes, and setting the learning mechanism of the control unit; and setting the adaptive control unit of the stimulation parameters in combination with the control unit, the abnormal feedback nodes and the learning mechanism.

[0080] Among them, the treated patient refers to an individual receiving transcranial magnetic stimulation treatment, the current health status refers to the patient's physiological and psychological state at a specific point in time, such as heart rate, blood pressure, cognitive function, etc., the control unit refers to the core component that adjusts the output parameters according to the patient's brain function performance and physiological state, the control rule refers to a series of logics or algorithms used to guide the control unit on how to adjust the stimulation parameters according to the patient's current condition, the safety range refers to the threshold of the stimulation parameters set to ensure the safety of treatment, including the maximum and minimum values, the parameter change refers to the actual change of the stimulation parameters during the treatment process, including the direction, amplitude and speed of the change, the abnormal feedback node refers to the mechanism used to detect and respond to abnormal conditions in the control system, and the learning mechanism refers to the ability of the control unit to self-optimize based on historical data and treatment effects.

[0081] Optionally, according to the current health condition, the control unit setting of the stimulation parameters can be obtained through an adaptive PID controller, and the control rule definition of the stimulation parameters based on the control unit can be implemented using a rule-based system, such as a Drools system, and according to the parameter changes, the abnormal feedback node setting of the stimulation parameters can be obtained through an alarm system, and according to the parameter changes, the learning mechanism setting of the control unit can be implemented using the TensorFlow deep learning framework.

[0082] The closed-loop feedback module 105 is used to analyze the stimulation effect of the key brain area according to the personalized adjustment mechanism and the adaptive control unit, analyze the attention change trend of the key brain area based on the attention pattern, and set the magnetic stimulation closed-loop feedback mechanism of the key brain area according to the attention change trend and the stimulation effect.

[0083] The embodiment of the present invention analyzes the stimulation effect of the key brain area according to the personalized adjustment mechanism and the adaptive control unit, and can dynamically adjust the neural regulation method according to the brain state evaluation result, give full play to the potential and advantages of transcranial electrical stimulation technology, and improve the pertinence and effectiveness of regulation. The stimulation effect refers to the positive influence and therapeutic effect observed on the patient after the patient is subjected to brain stimulation treatment using the personalized adjustment mechanism and the adaptive control unit.

[0084] Optionally, according to the personalized adjustment mechanism and the adaptive control unit, the stimulation effect analysis of the key brain area can be obtained by simulating the brain circuit through a Simulink model.

[0085] Furthermore, the embodiments of the present invention can ensure that transcranial magnetic stimulation can better adapt to the dynamic changes of the brain and achieve real-time adjustment of the attention network by analyzing the attention change trend of the key brain area based on the attention pattern. The attention change trend refers to the individual's attention allocation pattern to specific stimuli or tasks in different time periods.

[0086] As an embodiment of the present invention, analyzing the attention change trend of the key brain area based on the attention pattern includes: identifying the brain activity behavior of the key brain area based on the attention pattern; collecting brain imaging data corresponding to the brain activity behavior, and performing time series analysis on the brain imaging data to obtain time series data; analyzing the behavioral change trajectory of the brain activity behavior based on the time series data; identifying the attention task type of the key brain area based on the attention task type; analyzing the attention influencing factors of the key brain area based on the attention influencing factors; and analyzing the attention change trend of the key brain area based on the attention influencing factors.

[0087] Among them, the brain activity behavior refers to the activity pattern of the brain when performing a specific task or responding to a specific stimulus, the brain imaging data refers to the data obtained using brain imaging technology, such as EGG imaging technology, the time series data refers to a set of data points arranged in chronological order, the behavior change trajectory refers to the changing path of an individual's behavioral performance when performing a series of tasks or over a period of time, the attention task type refers to tasks that require different types of attention (such as selective attention), and the attention influencing factors refer to various factors that affect the concentration and allocation of an individual's attention, such as emotional state.

[0088] Optionally, the analysis of the behavioral change trajectory of the brain activity behavior based on the time series data can be achieved using the immediate early gene (IEG) marking method, and the identification of the attention task type of the key brain area based on the behavioral change trajectory can be determined through behavioral tests, such as reaction time tests, and the analysis of the attention influencing factors of the key brain area based on the attention task type can be achieved using psychological experiments, such as attention concentration experiments.

[0089] The embodiment of the present invention sets a closed-loop feedback mechanism of magnetic stimulation for the key brain area according to the attention change trend and the stimulation effect, and can adjust the parameters of transcranial magnetic stimulation (TMS) in real time according to the attention change trend and the stimulation effect, so that transcranial magnetic stimulation can better adapt to the dynamic changes of the brain, realize real-time adjustment of the attention network, reduce unnecessary side effects, and improve the safety of treatment, thereby improving cognitive function and treatment effect. The closed-loop feedback mechanism of magnetic stimulation refers to an automatic control method for optimizing the stimulation effect by real-time monitoring and adjusting transcranial magnetic stimulation (TMS) parameters.

[0090] As an embodiment of the present invention, the closed-loop feedback mechanism of magnetic stimulation of the key brain area is set according to the attention change trend and the stimulation effect, including: identifying the current stimulation parameters of the key brain area according to the attention change trend and the stimulation effect; defining the target stimulation parameters of the key brain area, and calculating the error signal of the current stimulation parameters based on the target stimulation parameters; setting the control signal of the current stimulation parameters according to the error signal; identifying the adjustment threshold of the current stimulation parameters based on the control signal; setting the parameter feedback loop of the key brain area according to the adjustment threshold and the control signal; and setting the closed-loop feedback mechanism of magnetic stimulation of the key brain area based on the parameter feedback loop.

[0091] Among them, the current stimulation parameters refer to the parameters actually applied by the transcranial magnetic stimulation technology at a specific moment, the target stimulation parameters refer to the ideal stimulation parameters set based on factors such as treatment effect, safety considerations and individual differences, the error signal refers to the difference between the current stimulation parameters and the target stimulation parameters, the control signal refers to the adjustment instruction calculated based on the error signal, the adjustment threshold refers to the maximum range of stimulation parameter changes allowed when executing the control signal, and the parameter feedback loop refers to the link in which the system performs cyclic control of the stimulation parameters by monitoring and evaluating the difference between the stimulation parameter output and the expected target.

[0092] Optionally, the error signal calculation of the current stimulation parameters based on the target stimulation parameters can be determined by the difference between the target stimulation parameters and the current stimulation parameters, and the parameter feedback loop setting of the key brain area based on the adjustment threshold and the control signal can be obtained by a fuzzy controller.

[0093] In an optional embodiment of the present invention, the control signal of the current stimulation parameter is set according to the error signal using the following formula:

[0094]

[0095] Where h(t) represents the control signal of the current stimulation parameters, M p represents the proportional gain of the error signal to the current stimulation parameters; M r represents the integral gain of the error signal to the current stimulation parameters, M c represents the differential gain of the error signal to the current stimulation parameters, E(t) represents the error signal corresponding to the current stimulation parameters, cE(t) represents the rate of change of the error signal over time, and ct represents the time length corresponding to the error signal.

[0096] The magnetic stimulation processing module 106 is used to perform magnetic stimulation processing on the brain stimulation patient in combination with the magnetic stimulation closed-loop feedback mechanism, the personalized adjustment mechanism and the adaptive control unit to obtain a magnetic stimulation processing result.

[0097] The embodiment of the present invention combines the magnetic stimulation closed-loop feedback mechanism, the personalized adjustment mechanism and the adaptive control unit to perform magnetic stimulation treatment on the brain stimulation patient and obtain the magnetic stimulation treatment result. The stimulation parameters can be automatically adjusted according to the patient's real-time data and feedback, thereby improving the convenience and efficiency of treatment, reducing unnecessary side effects, improving the safety of treatment, and enhancing patient comfort. At the same time, it can also ensure that transcranial magnetic stimulation treatment can achieve the expected effect, improve the repeatability of treatment, and save medical resources. The magnetic stimulation treatment refers to the process of treating the brain using magnetic stimulation technology, and the magnetic stimulation treatment result refers to the effect or data obtained after the magnetic stimulation treatment, such as improvement of patient behavior or cognitive function.

[0098] The embodiment of the present invention can identify the current state of the patient's brain by identifying the brain activity pattern of the brain stimulation patient based on the neural imaging data, provide a basis for subsequent stimulation parameter adjustment, identify the neural signal transmission path of the brain stimulation patient, and ensure that the stimulation can reach the most effective site; further, the embodiment of the present invention can ensure the safety of treatment and avoid damage to important brain structures by determining the key brain areas of the brain stimulation patient according to the associative neural network and identifying the stimulation points of the key brain areas; the embodiment of the present invention can identify the personalized differences between patients by identifying the pathological manifestations of the brain stimulation patient based on the disease type, extract the personalized characteristics of the brain stimulation patient, set the personalized adjustment mode of the stimulation points, adapt to the patient's response changes, improve the patient's compliance with treatment, and achieve more humane and precise brain stimulation treatment; further, the embodiment of the present invention can evaluate the patient's concentration, distraction or transfer of attention by extracting the activity characteristics of the associative neural network and identifying the attention pattern of the brain stimulation patient according to the activity characteristics. The brain activity of the patient is predicted to predict the patient's response to the treatment, so as to accurately determine the brain area that needs to be stimulated, thereby realizing intelligent control of transcranial magnetic stimulation; the embodiment of the present invention sets the magnetic stimulation closed-loop feedback mechanism of the key brain area according to the attention change trend and the stimulation effect, and can adjust the parameters of transcranial magnetic stimulation (TMS) in real time according to the attention change trend and the stimulation effect, so that transcranial magnetic stimulation can better adapt to the dynamic changes of the brain, realize real-time adjustment of the attention network, reduce unnecessary side effects, and improve the safety of treatment, thereby improving cognitive function and treatment effect; further, the embodiment of the present invention combines the magnetic stimulation closed-loop feedback mechanism, the personalized adjustment mechanism and the adaptive control unit to perform the magnetic stimulation treatment of the brain stimulation patient, obtain the magnetic stimulation treatment result, and can automatically adjust the stimulation parameters according to the patient's real-time data and feedback, improve the convenience and efficiency of treatment, reduce unnecessary side effects, improve the safety of treatment, enhance the comfort of patients, and also ensure that the transcranial magnetic stimulation treatment can achieve the expected effect, improve the repeatability of treatment, and save medical resources. Therefore, the intelligent control of transcranial magnetic stimulation treatment is realized, and the treatment effect of patients is improved.

[0099] like Figure 2 FIG. 1 is a flow chart of a closed-loop transcranial magnetic stimulation method with intelligent control provided by an embodiment of the present invention. In this embodiment, the closed-loop transcranial magnetic stimulation method with intelligent control includes:

[0100] Acquire a brain stimulation patient to be controlled and the corresponding disease type, collect the neuroimaging data of the brain stimulation patient in real time, identify the brain activity pattern of the brain stimulation patient based on the neuroimaging data, and identify the neural signal transmission path of the brain stimulation patient according to the brain activity pattern;

[0101] Identify the associated neural network of the neural signal transmission path, determine the key brain area of ​​the brain stimulation patient according to the associated neural network, identify the stimulation points of the key brain area, and set the stimulation parameters of the brain stimulation patient according to the key brain area and the stimulation points;

[0102] Based on the disease type, identifying the pathological manifestations of the brain stimulation patient, extracting personalized characteristics of the brain stimulation patient according to the pathological manifestations, and setting a personalized adjustment mode of the stimulation point based on the personalized characteristics;

[0103] Extracting the activity features of the associative neural network, and identifying the attention mode of the brain stimulation patient according to the activity features, analyzing the brain function performance of the key brain area under the attention mode, monitoring the physiological state of the brain stimulation patient in real time, and setting the adaptive control unit of the stimulation parameters based on the brain function performance and the physiological state;

[0104] Analyzing the stimulation effect of the key brain area according to the personalized adjustment mechanism and the adaptive control unit, analyzing the attention change trend of the key brain area based on the attention pattern, and setting a magnetic stimulation closed-loop feedback mechanism for the key brain area according to the attention change trend and the stimulation effect;

[0105] In combination with the magnetic stimulation closed-loop feedback mechanism, the personalized adjustment mechanism and the adaptive control unit, the magnetic stimulation treatment of the brain stimulation patient is performed to obtain a magnetic stimulation treatment result.

[0106] In the several embodiments provided by the present invention, it should be understood that the provided system and method can be implemented in other ways. For example, the system embodiment described above is only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0107] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0108] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. An intelligently controlled closed-loop transcranial magnetic stimulation system, characterized in that: The closed-loop transcranial magnetic stimulation controlled by the system intelligently comprises: a neural pathway identification module, a parameter setting module, a point adjustment module, a parameter control module, a closed-loop feedback module and a magnetic stimulation processing module; The neural pathway identification module is used to obtain the brain stimulation patient to be controlled and the corresponding disease type, collect the neural imaging data of the brain stimulation patient in real time, identify the brain activity pattern of the brain stimulation patient based on the neural imaging data, and identify the neural signal transmission path of the brain stimulation patient according to the brain activity pattern; The parameter setting module is used to identify the associated neural network of the neural signal transmission path, determine the key brain area of ​​the brain stimulation patient according to the associated neural network, identify the stimulation points of the key brain area, and set the stimulation parameters of the brain stimulation patient according to the key brain area and the stimulation points; The point adjustment module is used to identify the pathological manifestations of the brain stimulation patient based on the disease type, extract the personalized characteristics of the brain stimulation patient according to the pathological manifestations, and set the personalized adjustment mode of the stimulation point based on the personalized characteristics; The parameter control module is used to extract the activity characteristics of the associated neural network, and identify the attention mode of the brain stimulation patient according to the activity characteristics, analyze the brain function performance of the key brain area under the attention mode, monitor the physiological state of the brain stimulation patient in real time, and set the adaptive control unit of the stimulation parameters based on the brain function performance and the physiological state; The closed-loop feedback module is used to analyze the stimulation effect of the key brain area according to the personalized adjustment mechanism and the adaptive control unit, analyze the attention change trend of the key brain area based on the attention pattern, and set the magnetic stimulation closed-loop feedback mechanism of the key brain area according to the attention change trend and the stimulation effect; The magnetic stimulation processing module is used to combine the magnetic stimulation closed-loop feedback mechanism, the personalized adjustment mechanism and the adaptive control unit to perform magnetic stimulation processing on the brain stimulation patient and obtain a magnetic stimulation processing result.

2. The intelligent controlled closed-loop transcranial magnetic stimulation system according to claim 1, characterized in that: The step of identifying the brain activity pattern of the brain stimulation patient based on the neuroimaging data comprises: Based on the neuroimaging data, analyzing the classified brain regions of the brain stimulation patient and their corresponding electroencephalogram signals; Calculating the power spectral density of the EEG signal; Performing time series analysis on the EEG signal to obtain a time series analysis result; Based on the time series analysis result, identifying the frequency response characteristics of the EEG signal; Determining an associated event corresponding to the frequency response feature; Analyzing functional changes between the classified brain regions according to the frequency response characteristics; The brain activity pattern of the brain stimulation patient is identified by combining the frequency characteristics, the response events and the functional changes.

3. The intelligently controlled closed-loop transcranial magnetic stimulation system according to claim 1, characterized in that: Determining the key brain regions of the brain stimulation patient according to the associative neural network includes: Locating the brain region position of the associated neural network, and analyzing the interaction of the associated neural network according to the brain region position; detecting a cognitive response in the brain stimulated patient based on the interaction; analyzing a brain activity pattern of the brain stimulation patient based on the cognitive response; Based on the brain activity pattern, identifying the active brain regions of the brain stimulation patient under the cognitive response; Identify the key role of the active brain regions in the cognitive response; Based on the key effects, key brain areas of the brain stimulation patient are determined.

4. The intelligently controlled closed-loop transcranial magnetic stimulation system according to claim 1, characterized in that: The step of extracting personalized features of the brain stimulation patient according to the pathological manifestations includes: identifying a pathological pattern of the brain stimulation patient according to the pathological manifestations; calculating the similarity of the brain stimulation patients based on the pathological pattern; According to the similarity level, the brain stimulation patients are grouped to obtain grouped patients; Collecting medical data of the grouped patients and screening out key attribute data of the medical data; Based on the key attribute data, personalized characteristics of the brain stimulation patient are extracted.

5. The intelligently controlled closed-loop transcranial magnetic stimulation system according to claim 1, characterized in that: The step of setting the personalized adjustment mode of the stimulation point based on the personalized feature includes: identifying a brain stimulation patient corresponding to the personalized feature; Based on the personalized features, extracting electrophysiological features and behavioral features corresponding to the brain stimulation patient; identifying electrophysiological activities of the brain stimulation patient according to the electrophysiological characteristics and the behavioral characteristics; Based on the electrophysiological activity, extracting the active brain area corresponding to the brain stimulation patient, and analyzing the response performance of the active brain area; According to the active brain area and the response performance, a personalized adjustment mode of the stimulation point is set.

6. The intelligently controlled closed-loop transcranial magnetic stimulation system according to claim 1, characterized in that: The step of identifying the attention pattern of the brain stimulation patient according to the activity characteristics comprises: Extracting the brain neural network corresponding to the activity feature; According to the activity characteristics, collecting activity data of the brain neural network; Identifying brain functional areas of the brain stimulation patient based on the activity data; identifying the response function of the brain functional area and analyzing the evoked behavior corresponding to the response function; Based on the response function and the evoked behavior, identifying an activation pattern of the brain neural network; extracting attention features of the brain stimulation patient based on the activation pattern; Based on the attention characteristics, an attention pattern of the brain stimulation patient is identified.

7. The intelligently controlled closed-loop transcranial magnetic stimulation system according to claim 1, characterized in that: The adaptive control unit for setting the stimulation parameters based on the brain function performance and the physiological state comprises: identifying a treatment patient corresponding to the stimulation parameters; Analyzing the current health status of the treated patient based on the brain function performance and the physiological state; A control unit for setting the stimulation parameters according to the current health condition; Based on the control unit, defining control rules for the stimulation parameters; According to the control rule, setting a safety range of the stimulation parameter; Based on the safety range, monitoring parameter changes of the stimulation parameters in real time; According to the parameter changes, setting the abnormal feedback node of the stimulation parameter and setting the learning mechanism of the control unit; In combination with the control unit, the abnormal feedback node and the learning mechanism, an adaptive control unit of the stimulation parameters is set.

8. The intelligently controlled closed-loop transcranial magnetic stimulation system according to claim 1, characterized in that: The analyzing the attention change trend of the key brain area based on the attention pattern includes: Based on the attention pattern, identifying brain activity behavior of the key brain area; Collecting brain imaging data corresponding to the brain activity behavior, and performing time series analysis on the brain imaging data to obtain time series data; Analyzing the behavior change trajectory of the brain activity behavior according to the time series data; Based on the behavioral change trajectory, identifying the attention task type of the key brain area; Analyzing the attention-influencing factors of the key brain regions according to the attention task type; Based on the attention influencing factors, the attention change trend of the key brain area is analyzed.

9. The intelligently controlled closed-loop transcranial magnetic stimulation system according to claim 1, characterized in that: The method of setting a closed-loop feedback mechanism of magnetic stimulation of the key brain area according to the attention change trend and the stimulation effect includes: identifying current stimulation parameters of the key brain area according to the attention change trend and the stimulation effect; defining target stimulation parameters of the key brain area, and calculating an error signal of the current stimulation parameters based on the target stimulation parameters; Setting a control signal of the current stimulation parameter according to the error signal; Based on the control signal, identifying an adjustment threshold of the current stimulation parameter; Setting a parameter feedback loop of the key brain region according to the adjustment threshold and the control signal; Based on the parameter feedback loop, a magnetic stimulation closed-loop feedback mechanism for the key brain area is set.

10. An intelligently controlled closed-loop transcranial magnetic stimulation method, characterized in that: The method comprises: Acquire a brain stimulation patient to be controlled and the corresponding disease type, collect the neuroimaging data of the brain stimulation patient in real time, identify the brain activity pattern of the brain stimulation patient based on the neuroimaging data, and identify the neural signal transmission path of the brain stimulation patient according to the brain activity pattern; Identify the associated neural network of the neural signal transmission path, determine the key brain area of ​​the brain stimulation patient according to the associated neural network, identify the stimulation points of the key brain area, and set the stimulation parameters of the brain stimulation patient according to the key brain area and the stimulation points; Based on the disease type, identifying the pathological manifestations of the brain stimulation patient, extracting personalized characteristics of the brain stimulation patient according to the pathological manifestations, and setting a personalized adjustment mode of the stimulation point based on the personalized characteristics; Extracting the activity features of the associative neural network, and identifying the attention mode of the brain stimulation patient according to the activity features, analyzing the brain function performance of the key brain area under the attention mode, monitoring the physiological state of the brain stimulation patient in real time, and setting the adaptive control unit of the stimulation parameters based on the brain function performance and the physiological state; Analyzing the stimulation effect of the key brain area according to the personalized adjustment mechanism and the adaptive control unit, analyzing the attention change trend of the key brain area based on the attention pattern, and setting a magnetic stimulation closed-loop feedback mechanism for the key brain area according to the attention change trend and the stimulation effect; In combination with the magnetic stimulation closed-loop feedback mechanism, the personalized adjustment mechanism and the adaptive control unit, the magnetic stimulation treatment of the brain stimulation patient is performed to obtain a magnetic stimulation treatment result.

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