Design method of wearable helmet, regulation and control method of transcranial magnetic stimulation TMS coil applied to wearable helmet and related equipment

Abstract

The invention provides a wearable helmet design method, a transcranial magnetic stimulation TMS coil regulation and control method applied to a wearable helmet and related equipment, and the method comprises the steps: calculating a brain network target based on head image data; calculating a scalp regulation and control target spot of the TMS coil; calculating an individual head model; calculating a site of an electroencephalogram sensor, a site of a functional near-infrared light source, a site of a functional near-infrared probe and a helmet body model based on the individual head model; and determining a corresponding mounting hole site on the helmet body model based on the scalp regulation and control target of the TMS coil, the site of the electroencephalogram sensor, the site of the functional near-infrared light source and the site of the functional near-infrared probe. The TMS coil, the electroencephalogram sensor, the functional near-infrared light source and the functional near-infrared probe are all arranged on the same wearable helmet, the accuracy of a brain signal acquisition sensor and treatment positioning can be improved, the TMS regulation and control process is optimized through brain signals, and then the treatment effect can be improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of medical equipment, and more specifically, to a design method for a wearable helmet, a control method for a transcranial magnetic stimulation (TMS) coil applied to a wearable helmet, and related equipment. Background Art

[0002] Transcranial magnetic stimulation (TMS) is widely used clinically to treat neurological and psychiatric disorders. Its working principle is to pass a high current through a coil to instantly generate an alternating magnetic field, inducing induced currents in the cerebral cortex or tissue, thereby activating neurons. TMS coils used in related technologies are generally handheld, with an effective stimulation area of ​​only a few square centimeters. However, these coils are relatively large, making them inconvenient to use and even resulting in poor positioning accuracy.

[0003] To address these issues, related technologies primarily calculate the control target based on magnetic resonance imaging data, then use a robotic arm to position and clamp the TMS coil. Furthermore, neuronavigation is used to control the positional error between the TMS coil and the control target. While this method solves the TMS coil positioning problem during control, it is prohibitively expensive, requires lengthy experimental preparation, and requires high-quality operators, limiting its adoption.

[0004] Related technologies also include methods for reducing posture errors through fixed or adjustable TMS positioning helmets. However, because each patient's head shape may vary, the TMS coil is currently positioned by tightening the helmet with straps or buckles. However, this positioning method has poor fit, resulting in a strong sense of pressure on the patient, making it difficult for patients to persist in clinical treatment for a long time.

[0005] Another related technique involves manually extracting the skull contour from image data to generate the mounting position for the TMS coil's clamping device, and then using 3D printing technology to create a helmet. However, this method is only applicable to figure-eight and circular TMS coils, and cannot secure V-shaped TMS coils. Furthermore, figure-eight and circular TMS coils are often very heavy, making them difficult to wear.

[0006] Currently, there is also a method that uses real-time brain signal acquisition to decode brain states and dynamically control TMS coils. However, the acquisition of these brain signals requires additional acquisition devices, such as EEG sensors or functional near-infrared acquisition hats. In related technologies, it is often difficult to accurately position these additional acquisition devices. In addition, patients are required to wear the above-mentioned devices in turn to collect the corresponding signals, and the operation process is cumbersome. In addition, the experimental preparation time is also long, which limits its clinical application. Therefore, in related technologies, due to the difficulty in accurately positioning the TMS coil, its treatment effect is poor, making it difficult to meet actual treatment needs. Summary of the Invention

[0007] The present disclosure provides a design method for a wearable helmet, a control method for a transcranial magnetic stimulation (TMS) coil applied to a wearable helmet, and related equipment, in order to at least solve the problem in the above-mentioned related technologies that it is difficult to accurately position the TMS coil, resulting in poor therapeutic effects and thus difficulty in meeting actual treatment needs.

[0008] According to a first aspect of an embodiment of the present disclosure, a method for designing a wearable helmet is provided, comprising: acquiring image data of a user's head, wherein the image data is data obtained by performing a magnetic resonance scan on the user's head; calculating brain network targets based on the image data; calculating scalp control targets of a transcranial magnetic stimulation (TMS) coil based on the brain network targets; calculating an individual head model based on the image data; calculating locations of an electroencephalogram (EEG) sensor, a functional near-infrared light source, a functional near-infrared probe, and a helmet body model based on the individual head model; and determining corresponding mounting hole locations on the helmet body model based on the scalp control targets of the TMS coil, the locations of the EEG sensor, the functional near-infrared light source, and the functional near-infrared probe.

[0009] Optionally, the image data includes brain structure data and brain function data, and calculating the brain network target based on the image data includes: segmenting the brain structure data to obtain scalp data, skull data, and gray matter data; calculating brain structure connection data based on the gray matter data; calculating brain function connection data based on the brain function data; and selecting nodes from the brain structure connection data or the brain function connection data as the brain network target.

[0010] Optionally, the calculating the individual head model based on the image data includes: performing triangular facet reconstruction, parameterized fitting and scalp model smoothing on the scalp data to obtain the individual head model.

[0011] Optionally, the calculating of the location of the EEG sensor, the location of the functional near-infrared light source, and the location of the functional near-infrared probe based on the individual head model includes: calculating the location of the EEG sensor based on the individual head model; calculating the location of the functional near-infrared light source and the location of the functional near-infrared probe based on the location of the EEG sensor.

[0012] According to a second aspect of an embodiment of the present disclosure, a control method for a transcranial magnetic stimulation (TMS) coil applied to a wearable helmet is provided, the wearable helmet being designed according to the design method of the present disclosure, the control method comprising: acquiring multimodal brain signals, wherein the multimodal brain signals include electroencephalogram (EEG) signals and functional near-infrared signals, the EEG signals being signals obtained by collecting the induced current through the EEG sensor after the current is induced in the user's head by the TMS coil, and the functional near-infrared signals being signals reflected by the user's head and collected by the functional near-infrared probe after the functional near-infrared light source emits infrared light to the user's head; and optimizing and adjusting the stimulation time, stimulation intensity, and stimulation frequency of the TMS coil based on the multimodal brain signals.

[0013] Optionally, the optimizing and adjusting the stimulation time, stimulation intensity and stimulation frequency of the TMS coil based on the multimodal brain signals includes: extracting EEG features and brain oxygen features from the multimodal brain signals, wherein the EEG features include at least one of the following items: EEG phase features, rhythm energy features and signal connection features, and the brain oxygen features include at least one of the following items: blood oxygen value, activation features and functional network features; based on the EEG features and the brain oxygen features, adjusting the stimulation time, stimulation intensity and stimulation frequency of the TMS coil.

[0014] According to a third aspect of an embodiment of the present disclosure, a design device for a wearable helmet is provided, comprising: an image data acquisition module configured to acquire image data of a user's head, wherein the image data is data obtained by performing a magnetic resonance scan on the user's head; a brain network target calculation module configured to calculate brain network targets based on the image data; a scalp regulation target calculation module configured to calculate the scalp regulation targets of a transcranial magnetic stimulation (TMS) coil based on the brain network targets; an individual head model calculation module configured to calculate an individual head model based on the image data; a site and helmet body calculation module configured to calculate the sites of an EEG sensor, a functional near-infrared light source, a functional near-infrared probe, and a helmet body model based on the individual head model; and a mounting hole determination module configured to determine corresponding mounting hole sites on the helmet body model based on the scalp regulation targets of the TMS coil, the sites of the EEG sensor, the sites of the functional near-infrared light source, and the sites of the functional near-infrared probe.

[0015] Optionally, the brain network target calculation module is configured to: segment the brain structure data to obtain scalp data, skull data, and gray matter data; calculate brain structure connection data based on the gray matter data; calculate brain function connection data based on the brain function data; and select nodes from the brain structure connection data or the brain function connection data as the brain network targets.

[0016] Optionally, the individual head model calculation module is configured to: perform triangular patch reconstruction, parameterized fitting and smooth scalp model processing on the scalp data to obtain the individual head model.

[0017] Optionally, the location and helmet body calculation module are configured to: calculate the location of the EEG sensor based on the individual head model; calculate the location of the functional near-infrared light source and the location of the functional near-infrared probe based on the location of the EEG sensor.

[0018] According to a fourth aspect of an embodiment of the present disclosure, a control device for a transcranial magnetic stimulation (TMS) coil for a wearable helmet is provided, the wearable helmet being designed according to the design method of the present disclosure, the control device comprising: a multimodal brain signal acquisition module configured to acquire multimodal brain signals, wherein the multimodal brain signals include electroencephalogram (EEG) signals and functional near-infrared signals, the EEG signals being signals obtained by collecting the induced current through the EEG sensor after the TMS coil induces current in the user's head, and the functional near-infrared signals being signals reflected by the user's head and collected by the functional near-infrared probe after the functional near-infrared light source emits infrared light to the user's head; and an optimization and adjustment module being configured to optimize and adjust the stimulation time, stimulation intensity and stimulation frequency of the TMS coil based on the multimodal brain signals.

[0019] Optionally, the optimization and adjustment module is configured to: extract EEG features and brain oxygen features from the multimodal brain signals, wherein the EEG features include at least one of the following items: EEG phase features, rhythm energy features, and signal connection features, and the brain oxygen features include at least one of the following items: blood oxygen value, activation features, and functional network features; based on the EEG features and the brain oxygen features, adjust the stimulation time, stimulation intensity, and stimulation frequency of the TMS coil.

[0020] According to a fifth aspect of an embodiment of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to execute the instructions to implement a design method for a wearable helmet according to the present disclosure or a control method for a transcranial magnetic stimulation (TMS) coil applied to a wearable helmet.

[0021] According to a sixth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided. When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the design method of a wearable helmet according to the present disclosure or the control method of a transcranial magnetic stimulation (TMS) coil applied to a wearable helmet.

[0022] According to a seventh aspect of an embodiment of the present disclosure, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements a design method for a wearable helmet according to the present disclosure or a control method for a transcranial magnetic stimulation (TMS) coil applied to a wearable helmet.

[0023] The technical solutions provided by the embodiments of the present disclosure bring at least the following beneficial effects: In the present disclosure, by placing the TMS coil, EEG sensor, functional near-infrared light source, and functional near-infrared probe on the same wearable helmet, positioning accuracy can be improved, operational difficulty can be reduced, and treatment efficacy can be enhanced. Furthermore, the tedious operation process of patients frequently wearing different devices to collect corresponding signals can be avoided, simplifying the operation process. Furthermore, by adopting a closed-loop control method for the TMS coil, the control process of the TMS coil can be made more intelligent, meeting the medical needs of individualized and precise neuromodulation.

[0024] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute an improper limitation of the present disclosure.

[0026] Figure 1 is a flow chart illustrating a design method of a wearable helmet according to an exemplary embodiment of the present disclosure; Figure 2 is a schematic diagram illustrating scalp data, skull data, and gray matter data according to an exemplary embodiment of the present disclosure; Figure 3 is a schematic diagram illustrating individualized brain structure network partitioning according to an exemplary embodiment of the present disclosure; Figure 4 is a schematic diagram illustrating individualized brain functional network modeling according to an exemplary embodiment of the present disclosure; Figure 5 is a schematic structural diagram illustrating a TMS coil according to an exemplary embodiment of the present disclosure; Figure 6 is a schematic diagram illustrating digital modeling of a wearable helmet and calculation of a TMS coil posture according to an exemplary embodiment of the present disclosure; Figure 7 is a schematic diagram illustrating a multimodal brain signal acquisition sensor according to an exemplary embodiment of the present disclosure; Figure 8 is a schematic diagram illustrating various types of mounting holes provided on a helmet body according to an exemplary embodiment of the present disclosure; Figure 9 is a schematic diagram illustrating a wearable helmet integrating brain signal detection and transcranial magnetic stimulation according to an exemplary embodiment of the present disclosure; Figure 10 is a flow chart illustrating a method for controlling a transcranial magnetic stimulation (TMS) coil applied to a wearable helmet according to an exemplary embodiment of the present disclosure; Figure 11 is a block diagram illustrating a design apparatus for a wearable helmet according to an exemplary embodiment of the present disclosure; Figure 12 is a block diagram illustrating a control device for a transcranial magnetic stimulation (TMS) coil applied to a wearable helmet according to an exemplary embodiment of the present disclosure; Figure 13 is a block diagram illustrating an electronic device according to an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0027] In order to enable ordinary persons in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0028] It should be noted that the terms "first," "second," and the like in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the numbers used in this manner are interchangeable where appropriate so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The implementation methods described in the following examples do not represent all implementation methods consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with certain aspects of the present disclosure as detailed in the appended claims.

[0029] It should be noted that the phrase "at least one of the several items" in this disclosure includes three types of parallel situations: "any one of the several items", "a combination of any multiple of the several items", and "all of the several items". For example, "including at least one of A and B" includes the following three parallel situations: (1) including A; (2) including B; (3) including A and B. For another example, "performing at least one of step 1 and step 2" means the following three parallel situations: (1) performing step 1; (2) performing step 2; and (3) performing both step 1 and step 2.

[0030] Figure 1 is a flowchart illustrating a design method of a wearable helmet according to an exemplary embodiment of the present disclosure.

[0031] Reference Figure 1 In step 101, image data of the user's head may be obtained, wherein the image data may be data obtained by performing a magnetic resonance scan on the user's head.

[0032] In step 102, a brain network target can be calculated based on the imaging data. The brain network target is the core brain area that needs to be treated. For example, when a TMS coil is used to stimulate the user's head, the specific location where the stimulation is applied is the brain network target.

[0033] According to exemplary embodiments of the present disclosure, the aforementioned imaging data may include brain structural data and brain functional data. The brain structural data may include, but is not limited to, T1, T2, and DTI sequences; the brain functional data may include neuronal metabolic data or activity data. For example, it may include, but is not limited to, blood oxygen level dependent (BOLD) signals.

[0034] The above brain structure data can be segmented to obtain scalp data, skull data, and gray matter data. Figure 2 Schematic diagram showing scalp data, skull data, and gray matter data according to an exemplary embodiment of the present disclosure. Figure 2 After segmenting the user's brain structure data, scalp data 201, skull data 202, and gray matter data 203 can be obtained.

[0035] Then, brain structure connection data can be calculated based on the gray matter data 203 obtained by segmentation. Figure 3 is a schematic diagram illustrating individualized brain structure network partitioning according to an exemplary embodiment of the present disclosure. Figure 3 , brain structure connectivity data 301 can be calculated based on the gray matter data 203. In addition, the division of the brain region structure network can adopt 246 fine partitioning templates 302 of brain network omics.

[0036] Next, brain function connectivity data may be calculated based on the above-mentioned brain function data. Figure 4 is a schematic diagram illustrating individualized brain function network modeling according to an exemplary embodiment of the present disclosure. Figure 4 By processing the brain function data, a BOLD activation map 401 and brain functional connectivity 402 can be obtained.

[0037] Then, nodes can be selected from the brain structural connectivity data or the brain functional connectivity data as the brain network target. For example, according to clinical needs, the primary motor area M1 or the dorsolateral prefrontal cortex (DLPFC) in the brain structural connectivity data or the brain functional connectivity data can be selected as the brain network target.

[0038] In step 103, the scalp control target of the transcranial magnetic stimulation (TMS) coil can be calculated based on the aforementioned brain network targets. The scalp control target of the TMS coil is primarily used to indicate the installation position of the TMS coil. For example, based on the aforementioned brain network targets, individualized electric field simulation technology can be used to calculate parameters such as the scalp control target and orientation of the TMS coil to determine the TMS coil's posture.

[0039] Figure 5 : is a schematic diagram showing the structure of a TMS coil according to an exemplary embodiment of the present disclosure. Figure 5 The TMS coil may be a V-shaped coil, which may include components such as a coil body 501 , accessories 502 , and connecting wires 503 .

[0040] In step 104 , an individual head model may be calculated based on the image data.

[0041] According to an exemplary embodiment of the present disclosure, the scalp data may be subjected to triangular patch reconstruction, parameterized fitting, and scalp model smoothing processing to obtain an individual head model. Figure 6 : is a schematic diagram showing digital modeling of a wearable helmet and TMS coil posture calculation according to an exemplary embodiment of the present disclosure. Figure 6 , the scalp data can be triangulated to obtain triangular facets 601. Triangular facet reconstruction primarily reconstructs the point cloud into three-dimensional data. The obtained triangular facets 601 can then be parameterized and smoothed to create a scalp model, resulting in a smooth individual head model 602. This smoothed scalp model can be understood as a filter. Next, based on the aforementioned brain network targets, individualized electric field simulation technology 603 can be used to calculate parameters such as the scalp control target and direction of the TMS coil to determine the TMS coil's control posture 604.

[0042] In step 105, the locations of the EEG sensor, the functional near-infrared light source, the functional near-infrared probe, and the helmet body model can be calculated based on the individual head model. For example, a single, oval-shaped helmet body that perfectly matches the head can be generated based on the individual head model through offsetting, shelling, and thickening operations. Furthermore, the offset distance can range from -2 mm to 2 mm, and the thickness of the helmet body can range from 1 mm to 5 mm. Figure 7 Schematic diagram showing a multimodal brain signal acquisition sensor according to an exemplary embodiment of the present disclosure. Figure 7 The multimodal brain signal acquisition sensor may include: a functional near-infrared light source 701 , a functional near-infrared probe 702 and an EEG sensor 703 .

[0043] According to an exemplary embodiment of the present disclosure, the location of the EEG sensor can be calculated based on the above-mentioned individual head model. For example, the EEG sensor collection location can be generated based on the above-mentioned individual head model using an EEG 10-10 arrangement. Next, the location of the functional near-infrared light source and the location of the functional near-infrared probe can be calculated based on the location of the EEG sensor. Moreover, the distance between the location of the functional near-infrared light source and the location of the functional near-infrared probe can be 3 cm, and the location of the EEG sensor can be located in the middle between the location of the functional near-infrared light source and the location of the functional near-infrared probe.

[0044] In step 106, the corresponding mounting hole positions can be determined on the helmet body model based on the scalp control target of the TMS coil, the position of the EEG sensor, the position of the functional near-infrared light source, and the position of the functional near-infrared probe.

[0045] Figure 8 Schematic diagram showing various types of mounting holes provided on a helmet body according to an exemplary embodiment of the present disclosure. Figure 8 , the multimodal brain signal acquisition sensor mounting holes 802 can be determined on the helmet body model 801. Furthermore, the multimodal brain signal acquisition sensor mounting holes 802 can include: a functional near-infrared light source mounting hole 8021 determined on the helmet body model 801 based on the location of the functional near-infrared light source; an EEG sensor mounting hole 8022 determined on the helmet body model 801 based on the location of the EEG sensor; and a functional near-infrared probe mounting hole 8023 determined on the helmet body model 801 based on the location of the functional near-infrared probe. Furthermore, the distance between the mounting holes for the functional near-infrared light source and the mounting holes for the functional near-infrared probe can be 3 cm, and the mounting hole for the EEG sensor can be located midway between the mounting holes for the functional near-infrared light source and the mounting holes for the functional near-infrared probe.

[0046] In addition, the TMS coil mounting hole locations 803 can be determined on the helmet body model 801 based on the scalp control target of the TMS coil. Furthermore, the mounting hole locations can be obtained by taking the difference between the TMS coil model, the multimodal brain signal acquisition sensor model, and the helmet body.

[0047] Furthermore, the helmet body model 801 can be used to determine the helmet wearing positioning holes and buckle mounting holes, and a three-dimensional digital model can be derived. There can be three helmet wearing positioning holes, located at: the upper part of the left ear, the upper part of the right ear, and the center of the eyebrows. The shape of the positioning holes can be a semicircular notch, and the diameter can be, but is not limited to, 5mm to 20mm. For example, refer to Figure 8According to the upper contour of the patient's ear 804 and the front position of the patient's forehead 805, a concave, semicircular wearing positioning hole can be cut out on the helmet body model 801 for the patient or medical staff to confirm whether the helmet is worn correctly. Figure 8 Two snap-on mounting holes 806 and 807 can also be generated at the left / right ear positions of the helmet body model 801, and the diameter of the through hole can be, but not limited to, 3 mm to 9 mm, and the hole spacing can be, but not limited to, 10 mm to 50 mm. It should be noted that both contact surfaces of the V-shaped coil can be tangent to the inner surface of the helmet. Furthermore, the V-shaped coil's center of gravity, the TMS coil's scalp modulation target, and the brain network target (gray matter target in the cerebral cortex) can be collinear. The V-shaped coil can be oriented in a direction that maximizes the electromagnetic field intensity at the gray matter target. Furthermore, to reduce the distance from the stimulation coil to the scalp and increase the intensity of the effect, the mounting holes for the TMS coil need to be tailored to the patient's skull shape and coil position.

[0048] Next, non-toxic, high-strength materials can be selected and 3D printed to manufacture the helmet. TMS coils and multimodal brain signal acquisition sensors can be installed on the manufactured helmet. Specifically: First, based on the various mounting holes identified on the helmet body, a helmet can be manufactured using non-toxic, medical-grade materials, such as medical nylon, through 3D printing technology. The manufactured helmet can then undergo secondary processing. For example, the manufactured helmet can be polished and deburred. Furthermore, an annealing process can be employed to enhance the helmet's strength and hardness.

[0049] Next, the dimensions of the helmet after secondary processing can be verified using equipment such as a three-dimensional scanner. For example, a helmet with a dimensional error of less than 2mm can be considered a qualified product. Then, the qualified product can be installed with a multimodal brain signal acquisition sensor and a TMS stimulation coil. As mentioned above, the multimodal brain signal acquisition sensor can include a functional near-infrared light source, a functional near-infrared probe, and an EEG sensor. Figure 9 is a schematic diagram illustrating a wearable helmet that integrates brain signal detection and transcranial magnetic stimulation according to an exemplary embodiment of the present disclosure. It should be noted that the design method for the wearable helmet provided by the present disclosure can be a customized method, that is, a wearable helmet can be tailored for a specific patient.

[0050] Figure 10 1 is a flow chart illustrating a method for controlling a transcranial magnetic stimulation (TMS) coil for a wearable helmet according to an exemplary embodiment of the present disclosure. The wearable helmet may be designed according to the design method for a wearable helmet provided in the present disclosure.

[0051] Reference Figure 10 In step 1001, a multimodal brain signal may be acquired, that is, an individual's endogenous brain signal may be collected. The multimodal brain signal may include an electroencephalogram (EEG) signal and a functional near-infrared (FNIR) signal, and the acquired EEG signal and FNIR signal may be aligned at the data source.

[0052] An EEG signal can be obtained by induced current in the user's head through a TMS coil and then collected by an EEG sensor. A functional near-infrared signal can be a reflected signal collected by a functional near-infrared probe after infrared light is emitted from a functional near-infrared light source. It should be noted that the functional near-infrared signal can be a laser that can penetrate the skull, reach the cerebral cortex, and be reflected back. By detecting the reflected functional near-infrared signal, the current state of the brain can be observed.

[0053] Furthermore, in addition to collecting the current induced by the TMS coil, the EEG sensor can also collect the resting-state electrical signals of the user's brain. The resting-state electrical signals may refer to the EEG signals generated by the user's brain when the user is in a resting or thinking state.

[0054] In step 1002, the stimulation time, intensity, and frequency of the TMS coil can be optimized and adjusted based on the multimodal brain signals. Specifically, the individual's brain state can be calculated based on the multimodal brain signals, and the stimulation parameters of the TMS coil can be optimized in real time based on the calculated brain state, thereby achieving closed-loop wearable TMS neural regulation.

[0055] According to exemplary embodiments of the present disclosure, EEG and brain oxygenation features can be extracted from multimodal brain signals. EEG features can include at least one of the following: EEG phase features, rhythmic energy features, and signal connectivity features; brain oxygenation features can include at least one of the following: blood oxygen levels, activation features, and functional network features. Based on these EEG and brain oxygenation features, the stimulation time, intensity, and frequency of the TMS coil can then be adjusted.

[0056] It should be noted that the stimulation time of the TMS coil can be dynamically adjusted according to the real-time brain state value, thereby forming a state-dependent, dynamically variable optimization control; the stimulation intensity of the TMS coil can be x times the stimulation intensity of the motor evoked potential. For example, x can be 0.7 to 1.3 times; The stimulation frequency of the TMS coil can be optimized and adjusted according to the functional network characteristics and clinical needs.

[0057] The wearable neuromodulation helmet modeling and control method provided by the present disclosure, through medical imaging data processing, mechanical structure design and manufacturing, and closed-loop neuromodulation technology based on real-time brain signals, can dynamically decode brain signals in real time and thus optimize the control process of the wearable helmet. That is, the present disclosure can realize a precise closed-loop neuromodulation treatment system integrating transcranial magnetic stimulation and multimodal brain signal detection, which can solve the problems of rough neuromodulation positioning, poor control effect, and difficulty in adapting the positioning device to the wearable TMS coil in the related art. In other words, the control method of the transcranial magnetic stimulation TMS coil for the wearable helmet provided by the present disclosure can improve positioning accuracy and treatment effect while reducing the difficulty of operation, meeting the medical demand for individualized and precise neuromodulation.

[0058] Figure 11 1 is a block diagram illustrating a design apparatus 1100 for a wearable helmet according to an exemplary embodiment of the present disclosure.

[0059] Reference Figure 11 The wearable helmet design device 1100 may include an image data acquisition module 1101, a brain network target calculation module 1102, a scalp regulation target calculation module 1103, an individual head model calculation module 1104, a site and helmet body calculation module 1105 and an installation hole determination module 1106.

[0060] The image data acquisition module 1101 may acquire image data of the user's head, wherein the image data may be data obtained by performing a magnetic resonance scan on the user's head.

[0061] The brain network target calculation module 1102 can calculate the brain network target based on the image data. The brain network target is the core brain area that needs to be treated. For example, when a TMS coil is used to stimulate the user's head, the specific location where the stimulation is applied is the brain network target.

[0062] According to an exemplary embodiment of the present disclosure, the above-mentioned imaging data may include brain structure data and brain function data. Brain structure data may include, but is not limited to, T1, T2, DTI, and other sequences; brain function data may include neuronal metabolic data or activity data. For example, it may include, but is not limited to, BOLD signals. The brain network target calculation module 1102 may segment the above-mentioned brain structure data to obtain scalp data, skull data, and gray matter data. The brain network target calculation module 1102 may then calculate brain structure connectivity data based on the segmented gray matter data. Next, the brain network target calculation module 1102 may calculate brain function connectivity data based on the above-mentioned brain function data. The brain network target calculation module 1102 may then select nodes from the above-mentioned brain structure connectivity data or brain function connectivity data as the above-mentioned brain network targets. For example, according to clinical needs, the primary motor area M1 or the dorsolateral prefrontal cortex DLPFC in the brain structure connectivity data or brain function connectivity data may be selected as the brain network target.

[0063] The scalp control target calculation module 1103 can calculate the scalp control target of the transcranial magnetic stimulation (TMS) coil based on the aforementioned brain network targets. The scalp control target of the TMS coil is primarily used to indicate the installation position of the TMS coil. For example, based on the aforementioned brain network targets, individualized electric field simulation technology can be used to calculate parameters such as the scalp control target and orientation of the TMS coil to determine the TMS coil's posture.

[0064] The individual head model calculation module 1104 may calculate an individual head model based on the image data.

[0065] According to an exemplary embodiment of the present disclosure, the individual head model calculation module 1104 may perform triangular patch reconstruction, parameterized fitting, and scalp model smoothing processing on the scalp data to obtain an individual head model.

[0066] The location and helmet body calculation module 1105 can calculate the location of the EEG sensor, the location of the functional near-infrared light source, the location of the functional near-infrared probe and the helmet body model based on the above-mentioned individual head model.

[0067] According to an exemplary embodiment of the present disclosure, the location and helmet body calculation module 1105 can calculate the location of the EEG sensor based on the above-mentioned individual head model. Next, the location and helmet body calculation module 1105 can calculate the location of the functional near-infrared light source and the location of the functional near-infrared probe based on the location of the EEG sensor. In addition, the distance between the location of the functional near-infrared light source and the location of the functional near-infrared probe can be 3 cm, and the location of the EEG sensor can be located in the middle between the location of the functional near-infrared light source and the location of the functional near-infrared probe.

[0068] The mounting hole determination module 1106 can determine the corresponding mounting hole positions on the helmet body model based on the scalp control target of the TMS coil, the location of the EEG sensor, the location of the functional near-infrared light source, and the location of the functional near-infrared probe.

[0069] Figure 12 1 is a block diagram illustrating a control device 1200 for a transcranial magnetic stimulation (TMS) coil applied to a wearable helmet according to an exemplary embodiment of the present disclosure. The wearable helmet may be designed according to the design method of a wearable helmet of the present disclosure.

[0070] Reference Figure 12 The control device 1200 of the transcranial magnetic stimulation TMS coil applied to the wearable helmet may include a multimodal brain signal acquisition module 1201 and an optimization and adjustment module 1202.

[0071] The multimodal brain signal acquisition module 1201 can acquire multimodal brain signals, that is, can collect an individual's endogenous brain signals. The multimodal brain signals can include EEG signals and functional near-infrared signals, and the acquired EEG signals and functional near-infrared signals can be aligned at the data source.

[0072] An EEG signal can be obtained by induced current in the user's head through a TMS coil and then collected by an EEG sensor. A functional near-infrared signal can be a reflected signal collected by a functional near-infrared probe after infrared light is emitted from a functional near-infrared light source. It should be noted that the functional near-infrared signal can be a laser that can penetrate the skull, reach the cerebral cortex, and be reflected back. By detecting the reflected functional near-infrared signal, the current state of the brain can be observed.

[0073] Furthermore, in addition to collecting the current induced by the TMS coil, the EEG sensor can also collect the resting-state electrical signals of the user's brain. The resting-state electrical signals may refer to the EEG signals generated by the user's brain when the user is in a resting or thinking state.

[0074] The optimization and adjustment module 1202 can optimize and adjust the TMS coil's stimulation time, intensity, and frequency based on multimodal brain signals. Specifically, the individual's brain state can be calculated based on their multimodal brain signals, and the TMS coil's stimulation parameters can be optimized in real time based on the calculated brain state, thereby achieving closed-loop wearable TMS neural regulation.

[0075] According to an exemplary embodiment of the present disclosure, optimization and adjustment module 1202 can extract EEG and brain oxygenation features from multimodal brain signals. EEG features can include at least one of the following: EEG phase features, rhythm energy features, and signal connectivity features; brain oxygenation features can include at least one of the following: blood oxygen levels, activation features, and functional network features. Based on these EEG and brain oxygenation features, optimization and adjustment module 1202 can then adjust the TMS coil's stimulation time, intensity, and frequency.

[0076] Figure 13 is a block diagram illustrating an electronic device 1300 according to an exemplary embodiment of the present disclosure.

[0077] Reference Figure 13 The electronic device 1300 includes at least one memory 1301 and at least one processor 1302. The at least one memory 1301 stores instructions. When the instructions are executed by the at least one processor 1302, the design method of the wearable helmet according to the exemplary embodiment of the present disclosure or the control method of the transcranial magnetic stimulation TMS coil applied to the wearable helmet is executed.

[0078] As an example, electronic device 1300 may be a PC, tablet device, personal digital assistant, smartphone, or other device capable of executing the aforementioned instructions. Here, electronic device 1300 is not necessarily a single electronic device, but may also be any collection of devices or circuits capable of executing the aforementioned instructions (or instruction sets) individually or in combination. Electronic device 1300 may also be part of an integrated control system or system manager, or may be configured as a portable electronic device that interfaces with local or remote devices (e.g., via wireless transmission).

[0079] In electronic device 1300, processor 1302 may include a central processing unit (CPU), a graphics processing unit (GPU), a programmable logic device, a dedicated processor system, a microcontroller, or a microprocessor. By way of example and not limitation, the processor may also include an analog processor, a digital processor, a microprocessor, a multi-core processor, a processor array, a network processor, and the like.

[0080] The processor 1302 may execute instructions or codes stored in the memory 1301, which may also store data. Instructions and data may also be sent and received over a network via a network interface device, which may employ any known transmission protocol.

[0081] The memory 1301 may be integrated with the processor 1302, for example, by placing RAM or flash memory within an integrated circuit microprocessor or the like. Furthermore, the memory 1301 may comprise a separate device, such as an external disk drive, a storage array, or any other storage device usable by a database system. The memory 1301 and the processor 1302 may be operatively coupled or may communicate with each other, for example, via an I / O port, a network connection, or the like, such that the processor 1302 can access files stored in the memory.

[0082] In addition, the electronic device 1300 may further include a video display (such as a liquid crystal display) and a user interaction interface (such as a keyboard, a mouse, a touch input device, etc.) All components of the electronic device 1300 may be connected to each other via a bus and / or a network.

[0083] According to an exemplary embodiment of the present disclosure, a computer-readable storage medium may also be provided. When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the above-mentioned design method of a wearable helmet or the control method of a transcranial magnetic stimulation (TMS) coil applied to a wearable helmet. Examples of computer-readable storage media include: read-only memory (ROM), random access programmable read-only memory (PROM), electrically erasable programmable read-only memory (EEPROM), random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), flash memory, non-volatile memory, CD-ROM, CD-R, CD+R, CD-RW, CD+RW, DVD-ROM, DVD-R, DVD+R, DVD-RW, DVD+RW, DVD-RAM, BD-ROM, BD-R, BD-R LTH, BD-RE, Blu-ray or optical disk storage, hard disk drive (HDD), solid state drive (SSD), card storage (such as a multimedia card, secure digital (SD) card or extreme digital (XD) card), magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid state disk and any other device configured to store a computer program and any associated data, data files and data structures in a non-transitory manner and provide the computer program and any associated data, data files and data structures to a processor or computer so that the processor or computer can execute the computer program. The computer program in the above-mentioned computer-readable storage medium can be executed in an environment deployed in a computer device such as a client, a host, an agent device, a server, etc. In addition, in one example, the computer program and any associated data, data files and data structures are distributed on a networked computer system so that the computer program and any associated data, data files and data structures are stored, accessed and executed in a distributed manner by one or more processors or computers.

[0084] According to an exemplary embodiment of the present disclosure, a computer program product may also be provided, including a computer program, which, when executed by a processor, implements the design method of a wearable helmet according to the present disclosure or the control method of a transcranial magnetic stimulation (TMS) coil applied to a wearable helmet.

[0085] According to the design method of the wearable helmet, the control method of the transcranial magnetic stimulation (TMS) coil applied to the wearable helmet, and the related equipment disclosed in the present invention, by arranging the TMS coil, EEG sensor, functional near-infrared light source, and functional near-infrared probe on the same wearable helmet, positioning accuracy can be improved, operation difficulty can be reduced, and the treatment effect can be improved. In addition, the cumbersome operation process caused by the patient frequently wearing different devices to collect corresponding signals can be avoided, simplifying the operation process. In addition, by adopting a closed-loop control method for the TMS coil, the control process of the TMS coil can be made more intelligent, which can meet the medical needs of individualized and precise neural control.

[0086] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the following claims.

[0087] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A design method for a wearable helmet, characterized in that: include: Acquiring image data of a user's head, wherein the image data is data obtained by performing a magnetic resonance scan on the user's head; calculating brain network targets based on the imaging data; Based on the brain network targets, calculating the scalp regulation targets of the transcranial magnetic stimulation (TMS) coil; Calculating an individual head model based on the image data; Based on the individual head model, calculating the location of the EEG sensor, the location of the functional near-infrared light source, the location of the functional near-infrared probe, and the helmet body model; Based on the scalp control target of the TMS coil, the location of the EEG sensor, the location of the functional near-infrared light source, and the location of the functional near-infrared probe, corresponding mounting hole positions are determined on the helmet body model.

2. The design method according to claim 1, wherein: The image data includes brain structure data and brain function data, and the calculating of brain network targets based on the image data includes: Segmenting the brain structure data to obtain scalp data, skull data, and gray matter data; Calculating brain structure connectivity data based on the gray matter data; Calculating brain functional connectivity data based on the brain functional data; A node is selected from the brain structure connection data or the brain function connection data as the brain network target.

3. The design method according to claim 2, wherein: The step of calculating the individual head model based on the image data includes: The scalp data is subjected to triangular face reconstruction, parameter fitting and scalp model smoothing processing to obtain the individual head model.

4. The design method according to claim 1, wherein: The calculating, based on the individual head model, the location of the EEG sensor, the location of the functional near-infrared light source, and the location of the functional near-infrared probe includes: Calculating the location of the EEG sensor based on the individual head model; Based on the location of the EEG sensor, the location of the functional near-infrared light source and the location of the functional near-infrared probe are calculated.

5. A method for controlling a transcranial magnetic stimulation (TMS) coil for a wearable helmet, characterized in that: The wearable helmet is designed according to the design method according to any one of claims 1 to 4, and the control method includes: Acquiring multimodal brain signals, wherein the multimodal brain signals include electroencephalogram (EEG) signals and functional near-infrared (FNI) signals. The EEG signals are signals obtained by collecting the induced current by the EEG sensor after the TMS coil induces current in the user's head. The FNI signals are signals reflected by the user's head and collected by the FNI probe after the FNI light source emits infrared light toward the user's head. Based on the multimodal brain signals, the stimulation time, stimulation intensity and stimulation frequency of the TMS coil are optimized and adjusted.

6. The control method according to claim 5, wherein: The optimizing and adjusting the stimulation time, stimulation intensity, and stimulation frequency of the TMS coil based on the multimodal brain signal includes: Extracting EEG features and brain oxygen features from the multimodal brain signals, wherein the EEG features include at least one of the following: EEG phase features, rhythm energy features, and signal connectivity features; and the brain oxygen features include at least one of the following: blood oxygen value, activation features, and functional network features; Based on the EEG characteristics and the brain oxygen characteristics, the stimulation time, stimulation intensity and stimulation frequency of the TMS coil are adjusted.

7. A design device for a wearable helmet, characterized in that: include: an image data acquisition module, configured to acquire image data of a user's head, wherein the image data is data obtained by performing a magnetic resonance scan on the user's head; a brain network target calculation module, configured to calculate brain network targets based on the image data; a scalp control target calculation module, configured to calculate the scalp control target of the transcranial magnetic stimulation (TMS) coil based on the brain network target; an individual head model calculation module, configured to calculate an individual head model based on the image data; A location and helmet body calculation module is configured to calculate the location of the EEG sensor, the location of the functional near-infrared light source, the location of the functional near-infrared probe, and the helmet body model based on the individual head model; The installation hole position determination module is configured to determine the corresponding installation hole positions on the helmet body model based on the scalp control target of the TMS coil, the position of the EEG sensor, the position of the functional near-infrared light source and the position of the functional near-infrared probe.

8. A control device for a transcranial magnetic stimulation (TMS) coil used in a wearable helmet, characterized in that: The wearable helmet is designed according to the design method according to any one of claims 1 to 4, and the control device includes: a multimodal brain signal acquisition module configured to acquire multimodal brain signals, wherein the multimodal brain signals include electroencephalogram (EEG) signals and functional near-infrared (FNI) signals. The EEG signals are signals obtained by collecting the induced current through the EEG sensor after the TMS coil induces current in the user's head. The FNI signals are signals reflected by the user's head and collected by the FNI probe after the FNI light source emits infrared light toward the user's head. The optimization and adjustment module is configured to optimize and adjust the stimulation time, stimulation intensity and stimulation frequency of the TMS coil based on the multimodal brain signal.

9. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the design method of a wearable helmet as described in any one of claims 1 to 4, or to implement the control method of a transcranial magnetic stimulation (TMS) coil applied to a wearable helmet as described in any one of claims 5 to 6.

10. A computer-readable storage medium, characterized in that When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the design method of a wearable helmet as described in any one of claims 1 to 4, or to execute the control method of a transcranial magnetic stimulation (TMS) coil applied to a wearable helmet as described in any one of claims 5 to 6.

11. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, it implements the design method of a wearable helmet as described in any one of claims 1 to 4, or implements the control method of a transcranial magnetic stimulation (TMS) coil applied to a wearable helmet as described in any one of claims 5 to 6.

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