Digital-twin-data-mechanism-driven transcranial magnetic stimulation method and device

Abstract

The application discloses a transcranial magnetic stimulation method and device based on digital twin data-mechanism driving. The application realizes the synchronous evolution of the brain function model and the real brain state information by using the field self-adaptive brain function model synchronous evolution method, thereby reducing the lagged perception of the patient brain state change information in the treatment process, adjusting the coil parameters according to the treatment effect evaluation results, realizing the mechanism-driven closed-loop control, and using the dynamic simulation results to perform the stimulation scheme virtual pre-performance, realizing the closed-loop treatment strategy optimization of the virtual-real bidirectional feedback, and solving the technical problems that the open-loop targeted stimulation method has the lagged nature for the treatment effect evaluation, and cannot make the optimization adjustment in time for the stimulation effect. In addition, the real-time target point registration method based on visual servo is used to realize the space position information registration of the virtual head model and the patient head, thereby realizing the target point virtual-real space position information closed-loop interaction, and ensuring that each pulse stimulation can accurately target the stimulation point.

Description

Technical Field

[0001] The present invention relates to the field of biomedical engineering technology, and in particular to a transcranial magnetic stimulation method and device driven by digital twin data-mechanism. Background Art

[0002] Neuromodulation technology uses electrical stimulation or chemical reactions to modulate the nervous system (central nervous system, peripheral nervous system, and autonomic nervous system), either through stimulation or inhibition, and can improve neurological symptoms to a certain extent. Current neuromodulation technologies primarily include non-invasive methods such as transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), and focused ultrasound stimulation (FUS), as well as invasive methods such as deep brain stimulation (DBS) and infrared neurostimulation (INS). The emergence of these neuromodulation technologies will bring new avenues for rehabilitation for patients with neurological injuries.

[0003] Transcranial magnetic stimulation (TMS) is a noninvasive neuromodulation technique. It involves instantaneous, high-voltage pulses generated by a magnetic coil placed on the scalp, creating a magnetic field perpendicular to the coil's plane. This pulse acts on brain tissue, inducing currents that depolarize nerve cells and generate evoked potentials. This technique can be used to evaluate neurophysiological pathways and has been used in neurorehabilitation treatments for conditions such as stroke, depression, and autism.

[0004] Traditional transcranial magnetic stimulation (TMS) uses an open-loop targeted stimulation method, which results in a lag in the evaluation of treatment effects. It neglects real-time brain status monitoring and is unable to optimize and adjust the stimulation effects in a timely manner. Furthermore, since the patient's head inevitably moves during treatment, and traditional TMS uses a fixed device to prevent head movement, it is impossible to keep the patient's head absolutely still during stimulation. This results in the inability to accurately target the stimulation points with pulse stimulation, resulting in suboptimal TMS treatment results. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a transcranial magnetic stimulation method and device driven by digital twin data-mechanism, which can effectively solve the technical problems that the open-loop targeted stimulation method has a lag in the evaluation of treatment effects and cannot make timely optimization adjustments to the stimulation effects, as well as solve the technical problem that the open-loop targeted stimulation method cannot keep the patient's head absolutely still, resulting in the inability of pulse stimulation to accurately target the stimulation point.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0007] A transcranial magnetic stimulation method driven by digital twin data-mechanism includes the following steps: initializing spatial position information and coil parameters of a virtual head model; establishing a virtual coil in a virtual space and performing electromagnetic field simulation, and using a simulation grid search algorithm to find the optimal stimulation position of the coil for transcranial magnetic stimulation in the virtual space; controlling a therapeutic device according to the coil parameters and the optimal stimulation position of the coil to perform transcranial magnetic stimulation treatment on the patient; obtaining the patient's real-time brain state data and obtaining the patient's head image; based on the obtained patient's real-time brain state data, calculating the current virtual brain state information using a domain-adaptive brain function model synchronous evolution method, and updating the current virtual brain state information to the brain function model; based on the obtained patient's head image, calculating the current spatial position information of the virtual head model using a real-time target registration method based on visual servoing; evaluating the treatment effect according to the patient's real-time brain state data and the current virtual brain state information, adjusting the coil parameters according to the evaluation results, and updating the spatial position information of the virtual head model; repeating steps S20 to S70 until the transcranial magnetic stimulation treatment is completed;

[0008] The specific steps of calculating the current virtual brain state information using the domain-adaptive brain function model synchronous evolution method include: based on the acquired real-time brain state data of the patient, introducing incremental learning theory, combining the virtual-real state mapping mechanism constructed through domain adversarial training, updating dynamic data through the ENTM method, iterating the brain function model and its mechanism model, and obtaining the current virtual brain state information;

[0009] The specific steps of calculating the current spatial position information of the virtual head model using the real-time target registration method based on visual servoing include: using a deep network framework with a variable number of trained facial feature points to extract a virtual facial feature point cloud and a patient facial feature point cloud from the virtual head model and the patient's head image respectively; using a point cloud matching algorithm based on a Gaussian mixture model to minimize the gap between the virtual facial feature point cloud set and the patient's facial feature point cloud set, and combining the EM method to iteratively solve the mixed Gaussian problem to achieve spatial position information registration of the virtual head model and the patient's head, thereby obtaining the current spatial position information of the virtual head model.

[0010] A transcranial magnetic stimulation device driven by digital twin data-mechanism includes: an initialization module for initializing the spatial position information and coil parameters of a virtual head model; a virtual simulation module for establishing a virtual coil in a virtual space and performing electromagnetic field simulation, and using a simulated grid search algorithm to find the optimal stimulation position of the coil for transcranial magnetic stimulation in the virtual space; a treatment control module for controlling the treatment device according to the coil parameters and the optimal stimulation position of the coil to perform transcranial magnetic stimulation treatment on the patient; a data acquisition module for acquiring the patient's real-time brain state data and acquiring the patient's head image; a first calculation module for using the domain-adaptive brain function algorithm based on the acquired real-time brain state data of the patient The method can calculate the current virtual brain state information by using the synchronous evolution method of the model, and update the current virtual brain state information to the brain function model; the second calculation module is used to calculate the current spatial position information of the virtual head model based on the acquired patient head image using the real-time target registration method based on visual servoing; the parameter adjustment module is used to evaluate the treatment according to the patient's real-time brain state data and the current virtual brain state information, and adjust the coil parameters according to the evaluation results, and update the spatial position information of the virtual head model; the repeated calling module is used to cyclically repeatedly call the virtual simulation module, the treatment control module, the data acquisition module, the first calculation module, the second calculation module, and the parameter adjustment module until the transcranial magnetic stimulation treatment is completed;

[0011] The specific steps of calculating the current virtual brain state information using the domain-adaptive brain function model synchronous evolution method include: based on the acquired real-time brain state data of the patient, introducing incremental learning theory, combining the virtual-real state mapping mechanism constructed through domain adversarial training, updating dynamic data through the ENTM method, iterating the brain function model and its mechanism model, and obtaining the current virtual brain state information;

[0012] The specific steps of calculating the current spatial position information of the virtual head model using the real-time target registration method based on visual servoing include: using a deep network framework with a variable number of trained facial feature points to extract a virtual facial feature point cloud and a patient facial feature point cloud from the virtual head model and the patient's head image respectively; using a point cloud matching algorithm based on a Gaussian mixture model to minimize the gap between the virtual facial feature point cloud set and the patient's facial feature point cloud set, and combining the EM method to iteratively solve the mixed Gaussian problem to achieve spatial position information registration of the virtual head model and the patient's head, thereby obtaining the current spatial position information of the virtual head model.

[0013] The beneficial technical effect of the present invention is that the digital twin data-mechanism driven transcranial magnetic stimulation method of the present invention calculates the current virtual brain state information based on the patient's real-time brain state data and utilizes the domain-adaptive brain function model synchronous evolution method to achieve synchronous evolution of the brain function model and the real brain state information, thereby reducing the delayed perception of the patient's brain state change information during the treatment process, and then evaluates the treatment effect based on the patient's real-time brain state data and the current virtual brain state information, and adjusts the coil parameters according to the evaluation results to achieve mechanism-driven closed-loop control, and uses dynamic simulation results to conduct virtual preview of the stimulation scheme to achieve virtual and real two-way feedback The closed-loop treatment strategy is optimized, which solves the technical problem that the open-loop targeted stimulation method has a lag in the evaluation of treatment effects and cannot make timely optimization adjustments to the stimulation effects. In addition, by obtaining the patient's head image and using the real-time target registration method based on visual servoing to calculate the current spatial position information of the virtual head model, the spatial position information of the virtual head model and the patient's head are aligned, thereby realizing a closed-loop interaction of the virtual and real spatial position information of the target, ensuring that each pulse stimulation can accurately target the stimulation point, and solving the technical problem that the open-loop targeted stimulation method cannot keep the patient's head absolutely still, resulting in the inability of pulse stimulation to accurately target the stimulation point. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 Schematic diagram of the process of the digital twin data-mechanism-driven transcranial magnetic stimulation method of the present invention;

[0015] Figure 2 Schematic diagram of the process of calculating the current virtual brain state information using the domain-adaptive brain function model synchronous evolution method of the present invention;

[0016] Figure 3 A schematic diagram of the process of constructing a virtual-real state mapping mechanism for the present invention;

[0017] Figure 4 A schematic diagram of a flow chart of calculating the current spatial position information of a virtual head model using a real-time target registration method based on visual servoing according to the present invention;

[0018] Figure 5 A schematic diagram of the process of training a deep network framework with a variable number of facial feature points according to the present invention;

[0019] Figure 6 This is a structural block diagram of the transcranial magnetic stimulation device driven by digital twin data-mechanism of the present invention. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to more clearly understand the objectives, technical solutions and advantages of the present invention, the present invention is further described below with reference to the accompanying drawings and embodiments.

[0021] like Figure 1 As shown, in one embodiment of the present invention, the transcranial magnetic stimulation method based on digital twin data-mechanism drive includes steps S10 to S80:

[0022] S10: Initialize the spatial position information and coil parameters of the virtual head model.

[0023] S20. Establish a virtual coil in the virtual space and perform electromagnetic field simulation, and use a simulated grid search algorithm to find the optimal stimulation position of the coil for transcranial magnetic stimulation in the virtual space.

[0024] During implementation, finite element simulation of the brain's electromagnetic field in virtual space eliminates the influence of singularities in the finite element electromagnetic field simulation, improves the accuracy of brain electromagnetic field simulation, and makes the simulated treatment closer to real transcranial magnetic brain stimulation. The spatial position information of the virtual head model determines the position of the virtual head model in virtual space, which corresponds to the real spatial position of the patient's head. The coil parameters determine the power, pulse, current change rate, and other parameters of the virtual coil. Using a simulation grid search algorithm, the virtual coil position is found in virtual space when the target brain area of ​​the virtual head model is maximally covered by the electromagnetic field. This position is the optimal stimulation position of the coil for transcranial magnetic stimulation.

[0025] S30. Control the therapeutic device according to the coil parameters and the optimal stimulation position of the coil to perform transcranial magnetic stimulation treatment on the patient.

[0026] In one embodiment of the present invention, step S30 further includes the following steps:

[0027] S31. Based on the current position of the coil and the optimal stimulation position of the coil, the coil trajectory planning path is calculated using the reinforcement learning algorithm of the joint spatiotemporal state vector to ensure the safety of the transcranial magnetic coil movement.

[0028] S32. Control the power, pulse waveform, and current change rate of the coil of the therapeutic device according to the coil parameters to perform transcranial magnetic stimulation treatment on the patient; control the coil movement according to the coil trajectory planning path until the coil reaches the optimal stimulation position to ensure that each pulse stimulation can accurately target the stimulation point.

[0029] S40: Acquire the patient's real-time brain status data and the patient's head image.

[0030] During the transcranial magnetic stimulation treatment of the patient by the therapeutic device, the patient's real-time brain state data is collected through sensors as feedback data for transcranial magnetic stimulation. At the same time, the patient's head image is collected through the camera as reference data for subsequent adjustment of the coil position.

[0031] S50. Based on the acquired real-time brain state data of the patient, the current virtual brain state information is calculated using the domain-adaptive brain function model synchronous evolution method, and the current virtual brain state information is updated to the brain function model.

[0032] like Figure 2 As shown, the specific steps of calculating the current virtual brain state information using the domain-adaptive brain function model synchronous evolution method include: based on the acquired real-time brain state data of the patient, introducing incremental learning theory to monitor the time-varying characteristics of the brain state, combining the virtual-real state mapping mechanism constructed through domain adversarial training, updating dynamic data through the ENTM (Evolutionary neural turing machine) method, preventing the concept drift problem caused by the dynamic update of brain state data, avoiding the catastrophic forgetting phenomenon of the brain function model and its mechanism model, iterating the brain function model and its mechanism model, realizing the synchronous evolution of the brain function model and the real brain state information, thereby obtaining the current virtual brain state information, and updating the current virtual brain state information to the brain function model.

[0033] like Figure 3 As shown, in one embodiment of the present invention, the specific steps of constructing the virtual-real state mapping mechanism include:

[0034] S51. Construct a feature mapping network based on the brain state information set, and design a feature extractor according to the brain state information, and map the feature data generated by the feature extractor to a feature space, where the feature space includes a real domain feature distribution and a virtual domain feature distribution.

[0035] S52. Establish a label classification network based on the real-domain features and their distribution, and classify the real-domain data in the feature space in combination with the optimized residual network;

[0036] S53. Using the virtual domain features and their distribution, a domain discrimination network is set up. MMD (Maximum Mean Discrepancy) is used as a metric to construct a discriminator. The discriminator is used to determine whether the feature data comes from the virtual domain or the real domain.

[0037] S54. Based on the feature extractor and the discriminator, domain adversarial training is carried out to achieve alignment of the feature space, derive the state mapping relationship between the virtual domain and the real domain, and construct a mapping mechanism between the brain functional connection network and the real brain, that is, to construct a virtual-real state mapping mechanism.

[0038] In this embodiment, a feature extractor is designed based on brain state information, so that the feature extractor generates feature data that is conducive to maximizing the label classification network's recognition of real-domain data and minimizing the domain discrimination network's resolution of the brain state information set; by using an optimized residual network (ResNet) combined with a label classification network to classify real-domain data in the feature space, the network degradation problem caused by too many hidden layers in the deep neural network during feature classification can be solved, and the real-domain data in the feature space can be correctly classified as much as possible; by constructing a virtual-real state mapping mechanism, a clear consistency is established between virtual and reality, which can narrow the gap between virtual and reality.

[0039] S60 : Based on the acquired patient head image, calculate the current spatial position information of the virtual head model using a real-time target registration method based on visual servoing.

[0040] like Figure 4 As shown, the specific steps of calculating the current spatial position information of the virtual head model using the real-time target registration method based on visual servoing include:

[0041] S61. Utilize a trained deep network framework with a variable number of facial feature points to extract a virtual facial feature point cloud and a patient facial feature point cloud from the virtual head model and the patient's head image, respectively.

[0042] S62. A point cloud matching algorithm based on a Gaussian mixture model (GMM) is used to minimize the gap between the virtual facial feature point cloud set and the patient's facial feature point cloud set. The EM (Expectation-Maximum) method is used to iteratively solve the mixed Gaussian problem to achieve spatial position information alignment between the virtual head model and the patient's head, and the current spatial position information of the virtual head model is obtained.

[0043] like Figure 5 As shown, in one embodiment of the present invention, the specific steps of training a deep network framework with a variable number of facial feature points include:

[0044] S601. Extract facial feature points from a face image using a deep learning-based face recognition algorithm. Facial feature points include contour feature points and internal feature points. The face image is obtained from a high-quality public dataset. By performing refined facial feature annotation on the face image, contour feature points such as the jawline and forehead contour are extracted, as well as internal feature points such as the nose, eyes, mouth, and eyebrows. This allows for accurate recognition of key facial features, thereby achieving calibration-free visual positioning.

[0045] S602: Estimate the facial pose using a pose network (PoseNet) to obtain facial pose estimation information (quaternion of facial pose).

[0046] S603: Constructing a dendritic structure using facial feature points, combining this with facial pose estimation information, and utilizing a pose-conditioned dendritic convolutional neural network based on the Bayesian formula to separate the three-dimensional pose from the facial image, thereby reducing positioning errors during pose adjustment. The specific steps of constructing the dendritic structure include: constructing a dendritic structure with the nose tip feature point as the root node, and orderly placing the remaining facial feature points under the dendritic structure.

[0047] S604: Using the face image and facial feature points after stripping the three-dimensional posture as training data, a convolutional neural network is trained to obtain a deep network framework with a variable number of facial feature points.

[0048] S70 , evaluating the treatment effect based on the patient's real-time brain state data and the current virtual brain state information, adjusting the coil parameters based on the evaluation results, and updating the spatial position information of the virtual head model.

[0049] During the specific implementation process, the current virtual brain state information calculated in step S50 is compared with the patient's real-time brain state data to evaluate the treatment effect, and the coil parameters are adjusted according to the evaluation results. For example, the power, pulse waveform, and current change rate of the coil of the therapeutic device are adjusted to optimize the stimulation intensity level of the targeted area in the target brain area; at the same time, the spatial position information of the virtual head model is updated to prepare for the next stage of simulation treatment.

[0050] S80: Determine whether to terminate transcranial magnetic stimulation treatment. If so, terminate the entire process of the digital twin data-driven transcranial magnetic stimulation method. If not, return to step S20 and execute steps S20 to S70 again. By repeatedly executing steps S20 to S70, the patient's real-time brain state data is continuously compared with the current virtual brain state information, thereby continuously optimizing the target stimulation strategy, thereby achieving optimization of the closed-loop treatment strategy with virtual and real bidirectional feedback, and improving the effectiveness of treatment.

[0051] The digital twin data-mechanism-driven transcranial magnetic stimulation method of the present invention is based on the patient's real-time brain state data and uses the domain-adaptive brain function model synchronous evolution method to calculate the current virtual brain state information, thereby realizing the synchronous evolution of the brain function model and the real brain state information, thereby reducing the delayed perception of the patient's brain state change information during the treatment process, and then evaluating the treatment effect based on the patient's real-time brain state data and the current virtual brain state information, and adjusting the coil parameters according to the evaluation results to realize mechanism-driven closed-loop control, and using the dynamic simulation results to conduct a virtual preview of the stimulation plan, realizing a closed-loop treatment strategy with virtual and real two-way feedback. The method is slightly optimized to solve the technical problem that the open-loop targeted stimulation method has a lag in the evaluation of the treatment effect and cannot make timely optimization adjustments to the stimulation effect. In addition, by obtaining the patient's head image and using the real-time target registration method based on visual servoing to calculate the current spatial position information of the virtual head model, the spatial position information of the virtual head model and the patient's head are aligned, thereby realizing a closed-loop interaction of the virtual and real spatial position information of the target, ensuring that each pulse stimulation can accurately target the stimulation point, and solving the technical problem that the open-loop targeted stimulation method cannot keep the patient's head absolutely still, resulting in the inability of pulse stimulation to accurately target the stimulation point.

[0052] Based on the above-mentioned digital twin data-mechanism driven transcranial magnetic stimulation method, the present invention provides a digital twin data-mechanism driven transcranial magnetic stimulation device. The digital twin data-mechanism driven transcranial magnetic stimulation device can perform the following Figure 1 The digital twin data-mechanism-driven transcranial magnetic stimulation method in the illustrated embodiment has functional modules and beneficial effects corresponding to the execution method.

[0053] like Figure 6 As shown, in one embodiment of the present invention, a transcranial magnetic stimulation device driven by digital twin data-mechanism includes an initialization module 10, a virtual simulation module 20, a treatment control module 30, a data acquisition module 40, a first calculation module 50, a second calculation module 60, a parameter adjustment module 70 and a repeated call module 80.

[0054] Initialization module 10 is used to initialize the spatial position information and coil parameters of the virtual head model, that is, to perform the following operations: Figure 1 Step S10 in the digital twin data-mechanism-driven transcranial magnetic stimulation method in the illustrated embodiment.

[0055] The virtual simulation module 20 is used to establish a virtual coil in the virtual space and perform electromagnetic field simulation, and use the simulation grid search algorithm to find the optimal stimulation position of the coil for transcranial magnetic stimulation in the virtual space, that is, to perform the following steps: Figure 1 Step S20 in the digital twin data-mechanism-driven transcranial magnetic stimulation method in the illustrated embodiment.

[0056] The treatment control module 30 is used to control the treatment device according to the coil parameters and the coil's optimal stimulation posture to perform transcranial magnetic stimulation treatment on the patient, that is, to perform the following steps: Figure 1 Step S30 in the digital twin data-mechanism-driven transcranial magnetic stimulation method in the illustrated embodiment.

[0057] The data acquisition module 40 is used to acquire the patient's real-time brain status data and the patient's head image, that is, to perform the following operations: Figure 1 Step S40 in the digital twin data-mechanism-driven transcranial magnetic stimulation method in the illustrated embodiment.

[0058] The first calculation module 50 is used to calculate the current virtual brain state information based on the acquired real-time brain state data of the patient using the domain-adaptive brain function model synchronous evolution method, and update the current virtual brain state information to the brain function model, that is, to perform the following steps: Figure 1 Step S50 of the digital twin data-mechanism driven transcranial magnetic stimulation method in the illustrated embodiment. Specifically, the specific steps of calculating the current virtual brain state information using the domain-adaptive synchronous evolution method of the brain function model include: based on the acquired real-time brain state data of the patient, introducing incremental learning theory, combining the virtual-to-real state mapping mechanism constructed through domain adversarial training, updating dynamic data through the ENTM method, iterating the brain function model and its mechanism model, and obtaining the current virtual brain state information.

[0059] The second calculation module 60 is used to calculate the current spatial position information of the virtual head model based on the acquired patient head image using a real-time target registration method based on visual servoing, that is, to perform the following steps: Figure 1 Step S60 of the digital twin data-mechanism-driven transcranial magnetic stimulation method in the illustrated embodiment. Specifically, the specific steps of calculating the current spatial position information of the virtual head model using the real-time target registration method based on visual servoing include: utilizing a trained deep network framework with a variable number of facial feature points to extract a virtual facial feature point cloud and a patient facial feature point cloud from the virtual head model and the patient's head image, respectively; utilizing a point cloud matching algorithm based on a Gaussian mixture model to minimize the gap between the virtual facial feature point cloud set and the patient's facial feature point cloud set, and combining the EM method to iteratively solve the Gaussian mixture problem to achieve spatial position information registration of the virtual head model and the patient's head, thereby obtaining the current spatial position information of the virtual head model.

[0060] The parameter adjustment module 70 is used to evaluate the treatment based on the patient's real-time brain state data and the current virtual brain state information, and adjust the coil parameters according to the evaluation results, and update the spatial position information of the virtual head model, that is, to perform the following steps: Figure 1 Step S70 in the digital twin data-mechanism-driven transcranial magnetic stimulation method in the illustrated embodiment.

[0061] The repeated calling module 80 is used to cyclically and repeatedly call the virtual simulation module, the treatment control module, the data acquisition module, the first calculation module, the second calculation module, and the parameter adjustment module until the transcranial magnetic stimulation treatment is completed.

[0062] The digital twin data-mechanism driven transcranial magnetic stimulation device of the present invention calculates the current virtual brain state information based on the patient's real-time brain state data and the domain-adaptive brain function model synchronous evolution method, thereby realizing the synchronous evolution of the brain function model and the real brain state information, thereby reducing the delayed perception of the patient's brain state change information during the treatment process, and then evaluates the treatment effect based on the patient's real-time brain state data and the current virtual brain state information, and adjusts the coil parameters according to the evaluation results to realize mechanism-driven closed-loop control, and uses the dynamic simulation results to conduct a virtual preview of the stimulation plan, realizing a closed-loop treatment strategy with virtual and real two-way feedback. The method is slightly optimized to solve the technical problem that the open-loop targeted stimulation method has a lag in the evaluation of the treatment effect and cannot make timely optimization adjustments to the stimulation effect. In addition, by obtaining the patient's head image and using the real-time target registration method based on visual servoing to calculate the current spatial position information of the virtual head model, the spatial position information of the virtual head model and the patient's head are aligned, thereby realizing a closed-loop interaction of the virtual and real spatial position information of the target, ensuring that each pulse stimulation can accurately target the stimulation point, and solving the technical problem that the open-loop targeted stimulation method cannot keep the patient's head absolutely still, resulting in the inability of pulse stimulation to accurately target the stimulation point.

[0063] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any form. Those skilled in the art may make various equivalent changes and improvements based on the above embodiment. Any equivalent changes or modifications made within the scope of the claims shall fall within the scope of protection of the present invention.

Claims

1. A transcranial magnetic stimulation method based on digital twin data-mechanism drive, characterized in that: The steps include: S10, initializing the spatial position information and coil parameters of the virtual head model; S20, establishing a virtual coil in a virtual space and performing electromagnetic field simulation, and using a simulated grid search algorithm to find the optimal stimulation position of the coil for transcranial magnetic stimulation in the virtual space; S30, controlling the therapeutic apparatus according to the coil parameters and the optimal stimulation position of the coil to perform transcranial magnetic stimulation treatment on the patient; S40, obtaining the patient's real-time brain status data and the patient's head image; S50, based on the acquired real-time brain state data of the patient, using a domain-adaptive brain function model synchronous evolution method to calculate current virtual brain state information, and updating the current virtual brain state information to the brain function model; S60, based on the acquired patient head image, using a real-time target registration method based on visual servoing to calculate the current spatial position information of the virtual head model; S70, evaluating the treatment effect based on the patient's real-time brain state data and the current virtual brain state information, adjusting the coil parameters based on the evaluation results, and updating the spatial position information of the virtual head model; Repeat steps S20 to S70 until the transcranial magnetic stimulation treatment is completed; The specific steps of calculating the current virtual brain state information using the domain-adaptive brain function model synchronous evolution method include: based on the acquired real-time brain state data of the patient, introducing incremental learning theory, combining the virtual-real state mapping mechanism constructed through domain adversarial training, updating dynamic data through the ENTM method, iterating the brain function model and its mechanism model, and obtaining the current virtual brain state information; The specific steps of calculating the current spatial position information of the virtual head model using the real-time target registration method based on visual servoing include: Using a deep network framework with a variable number of trained facial feature points, the virtual facial feature point cloud and the patient's facial feature point cloud are extracted from the virtual head model and the patient's head image respectively; A point cloud matching algorithm based on a Gaussian mixture model is used to minimize the gap between the virtual facial feature point cloud set and the patient's facial feature point cloud set. The EM method is combined to iteratively solve the Gaussian mixture problem to achieve spatial position information alignment between the virtual head model and the patient's head, and the current spatial position information of the virtual head model is obtained.

2. The digital twin data-mechanism driven transcranial magnetic stimulation method according to claim 1, characterized in that: The specific steps to build a virtual-real state mapping mechanism include: A feature mapping network is constructed based on the brain state information set, and a feature extractor is designed based on the brain state information. The feature data generated by the feature extractor is mapped to a feature space, which contains the feature distribution of the real domain and the feature distribution of the virtual domain. A label classification network is established based on the real-domain features and their distribution, and the optimized residual network is used to classify the real-domain data in the feature space. The domain discrimination network is set up using virtual domain features and their distribution, and MMD is used as the metric to build a discriminator, which is used to determine whether the feature data comes from the virtual domain or the real domain. Based on the feature extractor and discriminator, domain adversarial training is carried out to achieve alignment of feature space, derive the state mapping relationship between the virtual domain and the real domain, and construct a virtual-reality state mapping mechanism.

3. The digital twin data-mechanism driven transcranial magnetic stimulation method according to claim 1, characterized in that: The specific steps of training a deep network framework with a variable number of facial landmarks include: Extract facial feature points of face images through face recognition algorithm based on deep learning, which include contour feature points and internal feature points; Use the posture network to estimate the face posture and obtain the facial posture estimation information; The dendritic structure is constructed using facial feature points, combined with facial pose estimation information, and a pose-conditioned dendritic convolutional neural network based on the Bayesian formula is used to separate the three-dimensional pose from the face image. The face images and facial feature points after stripping the 3D posture are used as training data to train the convolutional neural network to obtain a deep network framework with a variable number of facial feature points.

4. The digital twin data-mechanism driven transcranial magnetic stimulation method according to claim 3, characterized in that: The specific steps of constructing the dendritic structure include: constructing the dendritic structure with the nose tip feature point as the root node, and placing the remaining facial feature points in order under the dendritic structure.

5. The digital twin data-mechanism driven transcranial magnetic stimulation method according to claim 1, characterized in that: The step S30 further includes: S31, based on the current coil position and the optimal stimulation position of the coil, the coil trajectory planning path is calculated using the reinforcement learning algorithm of the joint spatiotemporal state vector; S32. Control the power, pulse waveform, and current change rate of the coil of the therapeutic device according to the coil parameters, and control the coil movement according to the coil trajectory planning path until the coil reaches the optimal stimulation position.

6. A transcranial magnetic stimulation device driven by digital twin data-mechanism, characterized in that: Includes: Initialization module, used to initialize the spatial position information and coil parameters of the virtual head model; The virtual simulation module is used to establish a virtual coil in a virtual space and perform electromagnetic field simulation, and use a simulated grid search algorithm to find the optimal stimulation position of the coil for transcranial magnetic stimulation in the virtual space; A treatment control module is used to control the treatment device according to the coil parameters and the optimal stimulation position of the coil to perform transcranial magnetic stimulation treatment on the patient; A data acquisition module is used to obtain the patient's real-time brain status data and obtain the patient's head image; A first calculation module is configured to calculate current virtual brain state information based on the acquired real-time brain state data of the patient using a domain-adaptive synchronous evolution method of the brain function model, and update the current virtual brain state information to the brain function model; A second calculation module is used to calculate the current spatial position information of the virtual head model based on the acquired patient head image using a real-time target registration method based on visual servoing; A parameter adjustment module is used to evaluate the treatment based on the patient's real-time brain state data and the current virtual brain state information, adjust the coil parameters according to the evaluation results, and update the spatial position information of the virtual head model; A repeated calling module, used for cyclically and repeatedly calling the virtual simulation module, the treatment control module, the data acquisition module, the first calculation module, the second calculation module, and the parameter adjustment module until the transcranial magnetic stimulation treatment is completed; The specific steps of calculating the current virtual brain state information using the domain-adaptive brain function model synchronous evolution method include: based on the acquired real-time brain state data of the patient, introducing incremental learning theory, combining the virtual-real state mapping mechanism constructed through domain adversarial training, updating dynamic data through the ENTM method, iterating the brain function model and its mechanism model, and obtaining the current virtual brain state information; The specific steps of calculating the current spatial position information of the virtual head model using the real-time target registration method based on visual servoing include: Using a deep network framework with a variable number of trained facial feature points, the virtual facial feature point cloud and the patient's facial feature point cloud are extracted from the virtual head model and the patient's head image respectively; A point cloud matching algorithm based on a Gaussian mixture model is used to minimize the gap between the virtual facial feature point cloud set and the patient's facial feature point cloud set. The EM method is combined to iteratively solve the Gaussian mixture problem to achieve spatial position information alignment between the virtual head model and the patient's head, and the current spatial position information of the virtual head model is obtained.

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