A human brain transcranial stimulation circuit

Abstract

The present application relates to the technical field of transcranial electrical stimulation, and discloses a human brain transcranial stimulation circuit, comprising: obtaining brain status data of a user to be stimulated; analyzing the connection relationship between brain regions based on the brain status data, and constructing a brain network map; based on the relationship between the node degree value and the medium centrality value of each node in the brain network map and the corresponding preset value, combined with a preset abnormality detection model, analyzing the abnormal area in the brain network map to obtain the brain target area; generating a stimulation strategy based on the brain target area; optimizing the stimulation strategy in combination with the brain status data to achieve closed-loop control of the stimulation strategy; the present application combines the user's real-time status to dynamically generate stimulation signals, accurately locate the brain target area and optimize the stimulation strategy, which can more effectively stimulate the brain target area, make the treatment process more scientific and reasonable, and improve the effect and efficiency of the treatment.

Description

Technical Field

[0001] The present invention relates to the technical field of transcranial electrical stimulation, and in particular to a human brain transcranial stimulation circuit. Background Art

[0002] The voltage of the existing transcranial stimulation circuit lithium battery is too low and its capacity is limited, resulting in a relatively short stimulation time and a limited boost voltage multiple. The charger cannot be directly plugged into the stimulation circuit and can only be removed for charging. It is impossible to formulate personalized stimulation strategies based on the user's brain status data, and it is impossible to optimize the stimulation strategy in real time according to the user's real-time status. It is impossible to adjust the stimulation current parameters in time to adapt to changes in brain status, which affects the treatment effect and efficiency.

[0003] For example, Chinese patent application number CN116983546B discloses a transcranial electrical stimulation device for the motor cortex and its control method, comprising a body, stimulation electrodes, detection electrodes, a controller, and a switching module. The body is head-mounted and includes a strap. The stimulation electrodes are disposed on the inner side of the body, corresponding to the location of the motor regions of the brain. The stimulation electrodes are used to stimulate the motor regions of the brain using a weak current. The motor regions of the brain include the left and right central regions of the brain. The detection electrodes are disposed on the inner side of the body and are used to detect current EEG signals. The controller is connected to the stimulation electrodes and the detection electrodes. The switching module is connected to the controller and is used to switch the operating states of the stimulation and detection electrodes.

[0004] The above existing technologies have the problems raised in this background technology. Based on the above problems existing in the existing technologies, in order to solve at least one of the above problems, the present application proposes a human brain transcranial stimulation circuit. Summary of the Invention

[0005] In view of the shortcomings of the prior art, the main purpose of the present invention is to provide a human brain transcranial stimulation circuit that can effectively solve the problems in the background technology. The specific technical solutions of the present invention are as follows:

[0006] A method for transcranial stimulation of the human brain is implemented by a processing device and executes a specific software method, including:

[0007] Acquiring brain state data of the user to be stimulated;

[0008] Analyzing the connectivity between brain regions based on the brain state data to construct a brain network map, wherein each node in the brain network map includes a node degree value and a medium centrality value;

[0009] Based on the relationship between the node degree value and the medium centrality value of each node in the brain network map and the corresponding preset values, combined with a preset anomaly detection model, the abnormal area in the brain network map is analyzed to obtain the brain target area;

[0010] generating a stimulation strategy according to the target brain area;

[0011] In combination with the brain state data, the stimulation strategy is optimized to achieve closed-loop control of the stimulation strategy.

[0012] Specifically, based on the brain state data, the connection relationship between brain regions is analyzed to construct a brain network map, including:

[0013] According to the resting-state EEG data in the brain state data, the phase value of each channel signal is calculated, and the phase synchronization matrix is ​​constructed by combining the phase synchronization degree between every two channel signals;

[0014] Based on the brain state data, the functional connection strength between different brain regions is calculated to obtain the functional connection matrix;

[0015] Dynamically fusing the phase synchronization matrix and the functional connectivity matrix to obtain a connectivity relationship matrix;

[0016] Based on the connection relationship matrix, a brain network map is constructed, wherein each brain region in the brain network map serves as each node, and each node is connected by using the corresponding value in the connection relationship matrix as the weight of the edge.

[0017] Specifically, based on the relationship between the node degree value and the medium centrality value of each node in the brain network map and the corresponding preset value, combined with a preset anomaly detection model, the abnormal area in the brain network map is analyzed to obtain the brain target area, including:

[0018] Calculate the node degree and medium centrality value of each node in the brain network map;

[0019] The brain region corresponding to the node whose node degree value is greater than the preset node degree value and whose medium centrality value is greater than the preset medium centrality value is taken as the core brain region;

[0020] Screening abnormal areas in the core brain region using a preset abnormality detection model to obtain abnormal core brain regions;

[0021] Screening out brain regions connected to the abnormal core brain region whose connection relationship values ​​are greater than a preset first abnormal threshold or whose connection relationship values ​​are less than a preset second abnormal threshold as abnormal-related brain regions, wherein the first abnormal threshold is greater than the second abnormal threshold, and the connection relationship values ​​are corresponding values ​​in the connection relationship matrix;

[0022] The abnormal core brain region and the abnormal related brain region are combined as the target brain region.

[0023] Specifically, generating a stimulation strategy based on the target brain area includes:

[0024] According to the connection relationship between each brain area in the target brain region, the stimulation sequence of the brain areas is analyzed to obtain the multi-channel stimulation path;

[0025] According to the multi-channel stimulation path, the stimulation current parameters of each channel are calculated by a preset stimulation parameter calculation model to generate a stimulation strategy.

[0026] Specifically, the method of analyzing the brain region stimulation sequence based on the connection relationship between each brain region in the target brain region to obtain the multi-channel stimulation path includes:

[0027] Construct a brain region causal model based on the connection relationship between each brain region in the target brain area;

[0028] Calculating the influence of each brain region in the target brain region on the overall target brain region when it is used as a stimulation target based on the brain region causal model;

[0029] determining a target priority for each brain region in the target brain region according to the impact degree value;

[0030] The brain region with the highest target priority is used as the main stimulation target, and the order of brain region stimulation is determined according to the target priority to obtain a multi-channel stimulation path.

[0031] Specifically, according to the multi-channel stimulation path, the stimulation current parameters of each channel are calculated by a preset stimulation parameter calculation model to generate a stimulation strategy, including:

[0032] According to the multi-channel stimulation path and the user's real-time status, the stimulation current parameters of each channel are obtained through a preset stimulation parameter calculation model, wherein the stimulation current parameters include the magnitude, frequency and duration of the stimulation current;

[0033] The multi-channel stimulation path and the stimulation current parameters of each channel are combined to obtain a stimulation strategy.

[0034] Specifically, the stimulation strategy is optimized in combination with the brain state data to achieve closed-loop control of the stimulation strategy, including:

[0035] Construct a state vector based on the user's real-time status;

[0036] Based on the stimulation strategy, an action vector is constructed;

[0037] According to a preset time period, the state vector is updated according to the user state after stimulation to obtain an updated state vector;

[0038] Calculating a reward value for the current stimulation process according to the difference between the state vector and the updated state vector;

[0039] According to the reward value, the stimulation strategy is optimized through a preset strategy optimization model to obtain an updated action vector;

[0040] Based on the updated motion vector, the user is stimulated for the next time period until the entire stimulation process is completed.

[0041] Specifically, the step of obtaining the brain state data of the user to be stimulated further includes performing data cleaning, standardization, and artifact removal on the obtained brain state data of the user to be stimulated.

[0042] A method for transcranial stimulation of the human brain, implemented by a stimulation circuit, comprising:

[0043] Collecting brain state data of the user to be stimulated;

[0044] sending the brain state data to a processing device so that the processing device generates generation parameters of a stimulation signal according to the brain state data;

[0045] Receive generation parameters of the stimulation signal, and generate the stimulation signal according to the generation parameters.

[0046] A human brain transcranial stimulation circuit, comprising:

[0047] A brain data acquisition module collects brain state data of the user to be stimulated;

[0048] a sending module, configured to send the brain state data to a processing device, so that the processing device generates generation parameters of a stimulation signal according to the brain state data;

[0049] a stimulation signal generating module, receiving generation parameters of the stimulation signal and generating the stimulation signal according to the generation parameters;

[0050] Among them, the processing device generates generation parameters of the stimulation signal according to the brain state data, including: analyzing the connection relationship between brain areas according to the brain state data to construct a brain network map; based on the relationship between the node degree value and the medium centrality value of each node in the brain network map and the corresponding preset value, combined with a preset anomaly detection model, analyzing the abnormal area in the brain network map to obtain the brain target area; generating a stimulation strategy according to the brain target area; optimizing the stimulation strategy in combination with the brain state data to achieve closed-loop control of the stimulation strategy.

[0051] Compared with the prior art, this application has the following beneficial effects:

[0052] The circuit design of the present application can be powered by a large-capacity, high-voltage battery, providing a long battery life and ensuring stable operation, realizing comprehensive electrical isolation measures and multi-parameter real-time monitoring functions, effectively preventing electrical interference and potential safety hazards, and ensuring the safety of the user during the stimulation process; combined with the user's real-time brain status, dynamically generating stimulation signals, accurately locating the target area of ​​the brain and optimizing the stimulation strategy, it can more effectively stimulate the target area of ​​the brain, improve brain function, and improve the effect of treating brain-related diseases. The stimulation strategy is optimized in real time according to the user's real-time status, and the stimulation current parameters are adjusted in time to make the treatment process more scientific and reasonable, and improve the effect and efficiency of the treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a workflow diagram of a method for transcranial stimulation of the human brain in Example 1 of the present invention;

[0054] Figure 2 Schematic diagram of brain target area identification in Example 1 of the present invention;

[0055] Figure 3 This is a schematic structural diagram of a human brain transcranial stimulation circuit in Example 3 of the present invention. DETAILED DESCRIPTION

[0056] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0057] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0058] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0059] Example 1

[0060] This embodiment provides a method for transcranial stimulation of the human brain, such as Figure 1 As shown, the method for transcranial stimulation of the human brain comprises:

[0061] S101, obtaining brain state data of the user to be stimulated;

[0062] S102. Analyze the connectivity between brain regions based on the brain state data and construct a brain network map, wherein each node in the brain network map includes a node degree value and a medium centrality value;

[0063] S103, based on the relationship between the node degree value and the medium centrality value of each node in the brain network map and the corresponding preset value, combined with a preset anomaly detection model, analyzing the abnormal area in the brain network map to obtain the brain target area;

[0064] S104, generating a stimulation strategy according to the target brain area;

[0065] S105. Optimize the stimulation strategy based on the brain state data to achieve closed-loop control of the stimulation strategy.

[0066] This embodiment constructs a brain network map based on the user's real-time brain state to accurately locate abnormal areas, and combines closed-loop control to optimize the stimulation strategy in real time, making the stimulation more targeted and improving the treatment effect. It optimizes the stimulation strategy according to the real-time response of the brain, adapts to dynamic changes in the brain, and improves the safety and effectiveness of treatment. It solves the problem of poor treatment effect caused by the single stimulation method in the existing technology and the failure to fully consider the differences in individual brain networks, and avoids the problem of lack of closed-loop control in the existing technology, which makes it impossible to adjust the stimulation current in real time according to the user's own state.

[0067] In this embodiment, the user's EEG signals are collected through electrodes in contact with the human brain in the circuit device, and the user's physical state data is collected in real time through the temperature and humidity sensor to obtain the user's real-time EEG signals (EEG), temperature data, humidity data, and resistance data. The collected real-time state data of the user can dynamically reflect the current activity state of the brain and capture the changes in the brain state over time, which helps to understand the real-time situation of the brain more accurately; the collected raw data is preprocessed to remove interference signals such as noise and artifacts to improve data quality. The preprocessed state data is used to analyze the correlation, synchronization and other characteristics between signals in different brain regions, determine the functional connection and structural connection relationship between brain regions, and construct A brain network map is produced. In this embodiment, the brain is partitioned based on the resting-state functions and cell structures of different parts. There are many methods for brain partitioning. In this embodiment, Brodmann partitioning is adopted to divide each hemisphere of the human brain into 52 regions to obtain different brain regions. The connection relationship between brain regions is analyzed according to the correlation between different brain regions. The correlation coefficient between brain regions is calculated based on the functional correlation and structural correlation between brain regions. The correlation coefficient is used as the weight of the connection edge, and the brain regions are connected to construct a brain network map. The brain network map can intuitively display the relationship between various brain regions, which helps to understand the functional organization and information transmission mode of the brain, thereby analyzing the user's abnormal brain regions.

[0068] Specifically, the constructed brain network map is analyzed, and abnormal areas are found by analyzing the connection strength between brain regions. When the connection strength between certain brain regions is significantly higher or lower than the normal range, or when there is an abnormal change in the connection between a brain region and other brain regions, it indicates that there is an abnormality in the brain region. The brain region and the abnormal areas connected to it are identified as the target areas for stimulation. By accurately determining the target brain area, a clear target is provided for targeted transcranial stimulation, thereby improving the accuracy and effectiveness of treatment or intervention.

[0069] Furthermore, after determining the target area for brain stimulation, the size, frequency, and stimulation time of the stimulation current are set according to factors such as the location, functional characteristics, and severity of the abnormality of the target area, combined with the principles and technical parameters of transcranial stimulation, to formulate a corresponding stimulation plan; through targeted stimulation strategies, it is possible to directly act on the abnormal areas of the brain and regulate abnormal neural activity, which is expected to improve brain function and alleviate related symptoms.

[0070] At the same time, during the implementation of the stimulation strategy, the user's real-time status data is continuously collected, and compared and analyzed with the data before stimulation and the expected changes in brain state. According to the analysis results, the parameters of the stimulation strategy are adjusted in real time to achieve closed-loop control, so that the stimulation can always be optimized according to the actual response of the brain; the adjusted stimulation strategy is applied to the subsequent stimulation process, and continuous monitoring and adjustment are carried out until the expected brain state or treatment effect is achieved; through closed-loop control, the stimulation strategy can be dynamically optimized according to the real-time response of the brain, the accuracy and effectiveness of the stimulation can be improved, and over-stimulation or under-stimulation can be avoided. At the same time, it can better adapt to individual differences and improve treatment effect and safety.

[0071] Furthermore, based on the brain state data, the connection relationship between brain regions is analyzed to construct a brain network map, including:

[0072] S201. Calculate the phase value of each channel signal based on the resting-state EEG data in the brain state data, and construct a phase synchronization matrix based on the phase synchronization degree between every two channel signals;

[0073] S202. Calculating the functional connectivity strength between different brain regions based on the brain state data to obtain a functional connectivity matrix;

[0074] S203, dynamically fusing the phase synchronization matrix and the functional connectivity matrix to obtain a connectivity relationship matrix;

[0075] S204. Construct a brain network map based on the connection relationship matrix, wherein each brain region in the brain network map serves as a node, and each node is connected by using the corresponding value in the connection relationship matrix as the weight of the edge.

[0076] This embodiment combines the collected resting-state EEG data of the user, analyzes the phase synchronization information of the data and the functional connectivity relationship between different brain regions, and dynamically fuses the phase synchronization matrix and the functional connectivity matrix, so that the obtained connectivity matrix more accurately reflects the actual connectivity between brain regions, thereby constructing a more accurate brain network map; by constructing a brain network map, it can accurately reflect the actual situation of the user's brain, improve the accuracy of the stimulation position, and avoid the problem of poor therapeutic effect caused by the existing technology of not being precise enough in stimulating the human brain area.

[0077] In this embodiment, the preprocessed EEG signal is analyzed. The collected EEG signal is a multi-channel signal. First, the phase value of each channel signal is calculated. The phase synchronization degree is calculated based on the phase value to reflect the consistency of the phase changes of the two channel signals. When there is a functional connection between the neural activities of two brain regions, the phase changes of their corresponding EEG channel signals are synchronized. By calculating the phase value of each channel signal and the phase synchronization index between each channel, the functional coupling relationship between the brain regions is reflected, and a phase synchronization matrix is ​​constructed. The elements in the matrix represent the phase synchronization index between the corresponding channels (brain regions). The calculation formula of the phase synchronization index is as follows:

[0078] ;

[0079] Where, is the phase synchronization index, is the phase time series of channel 1, is the phase time series of channel 2, is the standard deviation of the phase difference between channel 1 and channel 2. When the phase synchronization index is closer to 1, the degree of phase synchronization between the two channel signals is higher. The phase synchronization matrix can reveal the functional connectivity between brain regions at the phase level, capture information about the coordinated activity between brain regions, and provide relevant relationship information for brain network connectivity.

[0080] Specifically, the functional connectivity strength between different brain regions is analyzed based on the EEG data in the real-time state data. The functional connectivity strength is calculated by analyzing features such as the time series correlation between the EEG signals of different brain regions. In this embodiment, the Pearson correlation coefficient between each two channel signals is calculated to construct a functional connectivity matrix. The elements in the functional connectivity matrix are the Pearson correlation coefficients between the signals of the corresponding brain regions, reflecting the functional connectivity strength between the corresponding brain regions. The functional connectivity matrix intuitively shows the closeness of the functional connection between different brain regions. By analyzing the functional connectivity matrix, the collaborative working mode between brain regions in different states can be discovered, which can assist in analyzing functional abnormalities in disease states.

[0081] Furthermore, the phase synchronization matrix and the functional connectivity matrix are dynamically fused, and different weights can be set according to the analysis focus and calculation requirements. In this embodiment, the average weighted summation is adopted to integrate the information of the two matrices through dynamic fusion, and the characteristics of both phase synchronization and functional connectivity strength are comprehensively considered to obtain a connection relationship matrix that is more comprehensive and can better reflect the actual connection relationship between brain regions. The combination of the phase synchronization matrix and the functional connectivity matrix reflects the connection relationship information between brain regions from different angles, avoiding the problem that a single matrix cannot fully and accurately describe the brain network connection.

[0082] Specifically, a brain network map is constructed based on the connection relationship matrix, with brain regions regarded as nodes and the connection relationships between brain regions as edges. The weights of the edges are determined by the values ​​of the corresponding elements in the connection relationship matrix. The constructed brain network map can intuitively display the connection patterns between brain regions. The weights of the edges reflect the tightness or strength of the connections between brain regions. The map can clearly observe the overall structure of the brain functional network and the relationships between brain regions, thereby identifying abnormal areas and formulating corresponding treatment plans based on the status of the brain regions.

[0083] Furthermore, based on the relationship between the node degree value and the medium centrality value of each node in the brain network map and the corresponding preset value, combined with a preset anomaly detection model, the abnormal area in the brain network map is analyzed to obtain the brain target area, including:

[0084] S301, calculating the node degree value and medium centrality value of each node in the brain network map;

[0085] S302: The brain region corresponding to the node whose node degree value is greater than a preset node degree value and whose medium centrality value is greater than a preset medium centrality value is determined as a core brain region;

[0086] S303, screening abnormal areas in the core brain region using a preset abnormality detection model to obtain abnormal core brain regions;

[0087] S304. Screening out brain regions connected to the abnormal core brain region whose connection relationship values ​​are greater than a preset first abnormal threshold or whose connection relationship values ​​are less than a preset second abnormal threshold as abnormal-related brain regions, wherein the first abnormal threshold is greater than the second abnormal threshold, and the connection relationship values ​​are corresponding values ​​in the connection relationship matrix;

[0088] S305: combining the abnormal core brain region and the abnormal related brain region as a target brain region.

[0089] like Figure 2 As shown, this embodiment identifies core brain areas based on brain network maps, screens out abnormal brain areas in the core brain areas, and determines other abnormal brain areas through brain areas with abnormal connection relationships with the abnormal brain areas. Through multi-step quantitative analysis and model screening, it is possible to accurately locate the core areas in the brain related to functional abnormalities (abnormal core brain areas) and the related areas affected therefrom (abnormal related brain areas), thereby improving the accuracy of determining the target brain areas and avoiding the problem of unclear positioning of the stimulation area in the prior art. The clear brain target areas provide clear targets for transcranial stimulation, enabling treatment to act more specifically on abnormal brain areas, improving treatment effects, while reducing unnecessary stimulation of normal brain areas and lowering treatment risks.

[0090] In this embodiment, the node degree value and medium centrality value of each node in the brain network map are calculated based on the brain network map. The node degree value can measure the number of direct connections between a node and other nodes, reflecting the local importance of the node in the network. The higher the node degree value, the more direct connections the node has with other nodes, which plays a key role in the information transmission and functional integration of the local network; the medium centrality value measures the frequency of a node appearing on the shortest path between other nodes in the network, reflecting the control ability of the node in the information transmission of the entire network. The higher the medium centrality value of a node, the greater the impact on the overall connectivity and information dissemination efficiency of the network; by calculating these two values, the importance and status of each node (corresponding brain area) in the brain network map can be evaluated from different angles, which helps to identify brain areas that play a key role in local connections and overall information transmission from complex brain networks.

[0091] Specifically, core brain regions are screened out based on the calculated node degree values ​​and medium centrality values, and brain regions corresponding to nodes whose node degree values ​​are greater than the preset node degree values ​​and whose medium centrality values ​​are greater than the preset medium centrality values ​​are taken as core brain regions. The preset node degree values ​​and medium centrality values ​​can be empirical thresholds obtained based on statistical analysis of a large amount of normal brain network map data. When the node degree value and medium centrality value corresponding to a brain region are higher than these thresholds, it indicates that the brain region has a high degree of connectivity and information control ability in the network, and occupies a core position in the functional integration and information transmission of the brain. Screening out these core brain regions can target the analysis of areas that have a greater impact on brain function, narrow the scope of abnormal analysis, and improve analysis efficiency. In addition, these core brain regions will undergo significant changes when brain function is abnormal, providing a key entry point for discovering abnormal areas.

[0092] Furthermore, abnormal brain areas are identified in the screened core brain areas, and abnormal core brain areas are identified through a preset abnormality detection model. The abnormality detection model is specifically a random forest model. The abnormality detection model is trained based on a large amount of known normal and abnormal brain network data to obtain a pre-trained abnormality detection model. The preset abnormality detection model learns the characteristic patterns of the core brain areas in the normal brain network and the characteristic changes under abnormal conditions, and classifies and judges the input core brain area data. When the data characteristics of the core brain area deviate from the normal pattern characteristics learned by the model, the model identifies it as abnormal. The abnormal core brain area is the root or key part of abnormal brain function. Their accurate identification helps to deeply understand the pathogenesis of brain diseases and formulate targeted treatment strategies.

[0093] Specifically, after identifying the abnormal core brain area, the related brain areas with abnormal connections to the abnormal core brain area are further screened to obtain abnormal related brain areas; when an abnormality occurs in the core brain area, it will affect other brain areas connected to it through the connection relationship. The connection relationship value reflects the closeness or strength of the connection between the brain areas. The preset first abnormal threshold and second abnormal threshold can be determined based on a large amount of normal and abnormal brain network data, and the first abnormal threshold is greater than the second abnormal threshold; when the connection relationship value of the brain area connected to the abnormal core brain area is greater than the first abnormal threshold, it means that the connection is too tight and there is excessive functional coupling; when the connection relationship value is less than the second abnormal threshold, it indicates that the connection is too loose and there is impaired functional connection; these brain areas with abnormal connection relationships with the abnormal core brain area are all affected by corresponding functional abnormalities due to the user's actual brain disease. The brain areas with abnormal connection relationships with the abnormal core brain area are comprehensively identified, which expands the analysis scope of abnormal brain function areas, helps to understand the propagation and impact mechanism of brain diseases from the network level, and provides a basis for formulating more comprehensive treatment plans.

[0094] The abnormal core brain area and the abnormal related brain area are combined as the target brain area. The abnormal core brain area is the key part of abnormal brain function, while the abnormal related brain area is the area with abnormal connection relationship affected by the abnormal core brain area. Combining these two types of brain areas can more comprehensively cover the areas in the brain related to functional abnormalities, and together constitute a network related to brain diseases or functional disorders. Corresponding stimulation of the target brain area can more effectively improve brain function and treat related diseases.

[0095] Furthermore, generating a stimulation strategy based on the target brain area includes:

[0096] S401, analyzing the brain region stimulation sequence based on the connectivity between each brain region in the target brain region to obtain a multi-channel stimulation path;

[0097] S402 : Calculate stimulation current parameters of each channel according to the multi-channel stimulation path using a preset stimulation parameter calculation model to generate a stimulation strategy.

[0098] This embodiment constructs a causal model of brain regions and quantifies the degree of influence, accurately determines the main stimulation target and multi-channel stimulation path, and generates stimulation current parameters based on the individual's real-time status, so that the stimulation strategy can be designed precisely according to the characteristics of the target brain area and individual differences, thereby improving the accuracy of treatment; considering the causal relationship and priority between brain regions, multi-channel stimulation is carried out in a sequential manner, which can give full play to the synergistic effect of stimulation, maximize the improvement of abnormal brain function, and improve the treatment effect.

[0099] Furthermore, the brain region stimulation sequence is analyzed based on the connection relationship between each brain region in the target brain region to obtain a multi-channel stimulation path, including:

[0100] S501, constructing a brain region causal model based on the connection relationship between each brain region in the target brain region;

[0101] S502, calculating the influence of each brain region in the target brain region on the entire target brain region when it is used as a stimulation target, based on the brain region causal model;

[0102] S503, determining the target priority of each brain region in the target brain region according to the impact degree value;

[0103] S504: The brain region with the highest target priority is selected as the main stimulation target, and the order of brain region stimulation is determined according to the target priority to obtain a multi-channel stimulation path.

[0104] In this embodiment, based on the connection relationship between the brain regions in the target brain region, a brain region causal model is constructed using a causal inference algorithm. Specifically, the Granger causal analysis method is used to judge the causal relationship by comparing the difference in the prediction accuracy of the time series data of a certain brain region when the time series data of another brain region is included and not included. If adding the time series data of brain region A can significantly improve the prediction accuracy of the time series of brain region B, it is considered that brain region A has a Granger causal influence on brain region B. The brain region causal model is constructed by traversing and calculating the causal relationship parameters between every two brain regions in the target brain region. This helps to deeply understand the interaction mechanism between brain regions when brain function is abnormal, so as to formulate corresponding stimulation paths.

[0105] Specifically, based on the established brain region causal model, when a certain brain region is set as a stimulation target, the chain reaction triggered by stimulating the brain region is simulated according to the causal relationship path and intensity described in the model, and the impact degree value on the entire brain target region is calculated. The impact degree value is calculated by comprehensively considering the effects of the stimulation target on other brain regions through direct and indirect causal relationships, reflecting the influence of the brain region on the entire brain target region network. The calculation formula for the impact degree value is as follows:

[0106] ;

[0107] Where, is the influence value of brain region i as the stimulation target, The causal relationship parameter between brain region i and brain region j, is the change in state of brain region i, and n is the number of brain regions in the target brain region. The influence value reflects the influence of each brain region on the overall target brain region when it is a stimulation target. Brain regions are ranked based on the calculated influence value, with regions with greater influence being given higher target priority and those with less influence being given lower priority. Target priority provides clear guidance for developing stimulation strategies. Within limited treatment resources and time, it is possible to prioritize stimulation of brain regions with greater influence on the overall target brain region, thereby improving the targeted nature and effectiveness of treatment.

[0108] Furthermore, the brain area with the highest target priority is selected as the main stimulation target. Then, according to the target priority of other brain areas, the order of the brain areas to be stimulated is determined in turn to form a multi-channel stimulation path. By selecting the main stimulation target, the brain area with a greater impact on the overall brain target area is determined to be given priority stimulation. The design of the multi-channel stimulation path enables the stimulation to act on the target area of ​​the brain in a planned and sequential manner, avoiding blind stimulation, improving the synergy and effectiveness of the stimulation, and helping to regulate the brain functional network more comprehensively and deeply, and improve abnormal brain function.

[0109] Furthermore, according to the multi-channel stimulation path, the stimulation current parameters of each channel are calculated by a preset stimulation parameter calculation model to generate a stimulation strategy, including:

[0110] S601, obtaining stimulation current parameters for each channel based on the multi-channel stimulation path and the user's real-time status using a preset stimulation parameter calculation model, wherein the stimulation current parameters include the magnitude, frequency, and duration of the stimulation current;

[0111] S602: Combine the multi-channel stimulation path and the stimulation current parameters of each channel to obtain a stimulation strategy.

[0112] In this embodiment, the stimulation current parameters of each channel are calculated according to the stimulation path in combination with the real-time status of the user using a preset stimulation parameter calculation model. The stimulation parameter calculation model is specifically a linear regression model. The model is trained based on a large amount of historical data, and the parameters of the model are adjusted so that the model can accurately predict the optimal stimulation current parameters based on the input brain area information, the multi-channel stimulation path and the real-time status of the user; the brain area information corresponding to each channel in the multi-channel stimulation path and the real-time status data of the user are input into the trained stimulation parameter calculation model, and the model outputs the stimulation current size, frequency and duration parameter values ​​of each channel; personalized stimulation current parameters are generated according to individual differences and real-time status, which can improve the accuracy and effectiveness of the stimulation, avoid overstimulation or understimulation caused by unified standard stimulation current parameters, and at the same time improve the safety of treatment and reduce the occurrence of adverse reactions.

[0113] Specifically, the multi-channel stimulation path and the stimulation current parameters of each channel are combined to obtain a specific stimulation strategy. The multi-channel stimulation path determines the order and synergistic relationship of stimulation on the target area of ​​the brain, while the stimulation current parameters of each channel clarify the specific stimulation method and intensity at each stimulation point. Combining the two to form a complete stimulation strategy can systematically and targetedly stimulate the target area of ​​the brain to achieve the purpose of improving abnormal brain function, making the stimulation strategy more scientific and reasonable.

[0114] Furthermore, the stimulation strategy is optimized in combination with the brain state data to achieve closed-loop control of the stimulation strategy, including:

[0115] S701. Construct a state vector based on the user's real-time state;

[0116] S702, constructing an action vector according to the stimulation strategy;

[0117] S703, updating the state vector according to the user state after stimulation in a preset time period to obtain an updated state vector;

[0118] S704, calculating the reward value of the current stimulation process according to the difference between the state vector and the updated state vector;

[0119] S705: Optimizing the stimulation strategy using a preset strategy optimization model according to the reward value to obtain an updated action vector;

[0120] S706 : Based on the updated action vector, stimulate the user for the next time period until the entire stimulation process is completed.

[0121] This embodiment monitors the user status in real time to construct a state vector, and dynamically adjusts the stimulation strategy according to the stimulation effect. It can accurately target the individual differences and real-time changes of each user for treatment, thereby improving the targetedness and accuracy of treatment. Through continuous closed-loop control and strategy optimization, it helps to guide the user status toward the expected treatment goal, maximize the therapeutic effect of transcranial stimulation, improve the user's brain function and physical condition, and increase the success rate of treatment.

[0122] In this embodiment, based on the collected user status data, the data is normalized to construct a state vector, which reflects the real-time characteristics of the user in different aspects and can comprehensively describe the changes in the user's state during the stimulation process; based on the previously generated stimulation strategy, including parameters such as the stimulated brain area, stimulation current size, frequency, and duration, different value combinations of these parameters are regarded as different actions to construct an action vector; during the stimulation process, the user's state will change accordingly. According to a preset time period (such as every 5 minutes), the real-time status data of the user after receiving the stimulation is re-collected, and the previously constructed state vector is updated according to the updated data. The updated state vector reflects the changes in the user's state during the stimulation process. According to the difference between the state vector and the updated state vector, the reward value of the current stimulation process is calculated to evaluate the stimulation effect.

[0123] Specifically, the reward value is calculated by comparing the difference in the state vector before and after stimulation (i.e., before and after updating). If the user's state changes in the expected direction after stimulation, such as improved brain function and normal physiological indicators, the reward value is positive; conversely, if the state deteriorates or there is no significant improvement, the reward value is negative. The calculation of the reward value provides feedback signals for optimizing the stimulation strategy, guiding how to adjust the stimulation strategy to achieve better treatment effects. The reward value calculation formula is as follows:

[0124] ;

[0125] Where, is the reward value, m is the number of elements in the state vector, is the pth element in the original state vector, To update the pth element in the state vector, after calculating the reward value, the stimulation strategy is optimized according to the reward value. The current stimulation strategy is optimized through a preset strategy optimization model. The strategy optimization model is specifically a Q-learning model. A pre-trained strategy optimization model is obtained by training with a large amount of historical data. The model learns which stimulation strategy can better make the user state develop in the expected direction according to the level of the reward value, thereby adjusting the selection probability of different actions in the action space or directly generating new and better actions to obtain an updated action vector; the strategy optimization model can continuously optimize the stimulation strategy according to the reward value, automatically adapt to the individual differences and state changes of the user, improve the pertinence and effectiveness of the stimulation strategy, so that transcranial stimulation therapy can better meet the needs of each user and improve the treatment effect.

[0126] Specifically, the new stimulation strategy determined in the updated action vector is applied to the user, and the next time period of transcranial stimulation is started. In this new stimulation process, the user state is monitored, the state space is updated, the reward value is calculated, and the strategy is optimized again according to the previous steps (S701 to S706), and this cycle is repeated until the entire preset stimulation treatment process is completed; by continuously iteratively optimizing the stimulation strategy, the user's brain and body state are gradually guided towards the expected treatment goal, ensuring that the stimulation can always adapt to the changes of the user, maximizing the effect of transcranial stimulation treatment, providing users with more accurate and effective treatment plans, and also improving the intelligence level of the treatment process.

[0127] Furthermore, the step of obtaining the brain state data of the user to be stimulated further includes performing data cleaning, standardization, and artifact removal on the obtained brain state data of the user to be stimulated.

[0128] In this embodiment, the user's brain state data is cleaned to remove noise and outliers in the acquisition process, so as to avoid noise and outliers interfering with data processing and analysis and affecting the accuracy of the results, making the data purer and more reliable; the data is standardized to convert the data into a form with the same scale and distribution to avoid differences in dimension and scale due to individual differences, acquisition equipment and other factors; the brain state data is processed for artifact removal, and the characteristics and distribution patterns of the artifacts are used to separate and remove them from the original data to extract the real brain state signal, avoiding interference of eye movement artifacts, electromyographic artifacts, electrocardiographic artifacts, etc. on the real EEG signal.

[0129] A method for transcranial stimulation of the human brain, implemented by a stimulation circuit, comprising:

[0130] Collecting brain state data of the user to be stimulated;

[0131] sending the brain state data to a processing device so that the processing device generates generation parameters of a stimulation signal according to the brain state data;

[0132] Receive generation parameters of the stimulation signal, and generate the stimulation signal according to the generation parameters.

[0133] This embodiment collects brain status data of the user to be stimulated in real time through the electrode part in contact with the human brain in the stimulation circuit, and sends the collected data to the processing device through the network unit. The processing device analyzes the user's brain status data and constructs a brain network map. In combination with the user's actual illness, it analyzes the abnormal brain area and formulates a corresponding transcranial stimulation strategy to obtain the generation parameters of the stimulation signal, including the magnitude, frequency, and stimulation time of the stimulation current. The stimulation circuit generates the corresponding stimulation current according to the generation parameters of the stimulation signal, and performs transcranial stimulation on the user through the electrodes in contact with the user.

[0134] Example 2

[0135] This embodiment provides a human brain transcranial stimulation circuit, comprising:

[0136] A brain data acquisition module collects brain state data of the user to be stimulated;

[0137] a sending module, configured to send the brain state data to a processing device, so that the processing device generates generation parameters of a stimulation signal according to the brain state data;

[0138] a stimulation signal generating module, receiving generation parameters of the stimulation signal and generating the stimulation signal according to the generation parameters;

[0139] Among them, the processing device generates generation parameters of the stimulation signal according to the brain state data, including: analyzing the connection relationship between brain areas according to the brain state data to construct a brain network map; based on the relationship between the node degree value and the medium centrality value of each node in the brain network map and the corresponding preset value, combined with a preset anomaly detection model, analyzing the abnormal area in the brain network map to obtain the brain target area; generating a stimulation strategy according to the brain target area; optimizing the stimulation strategy in combination with the brain state data to achieve closed-loop control of the stimulation strategy.

[0140] Example 3

[0141] This embodiment provides a specific implementation of a human brain transcranial stimulation circuit, such as Figure 3 , the human brain transcranial stimulation circuit includes:

[0142] Power supply module, responsible for providing stable power supply;

[0143] The human brain contact module is responsible for generating dynamic stimulation signals based on the user's real-time status and applying them to the target area of ​​the user's brain to achieve dynamic stimulation of the user's brain;

[0144] The isolation module is used to achieve electrical isolation between the power supply module and the human brain contact module.

[0145] In this embodiment, the power supply module provides a continuous and stable power supply to solve the problems of fast battery consumption and insufficient battery life in the existing stimulation circuit; the human brain contact module generates a dynamic stimulation signal according to the real-time status of the user, which is conducive to personalized treatment. The stimulation can be accurately adjusted according to the real-time status of each user's brain to improve the treatment effect, and solves the problem that the existing technology mostly uses fixed stimulation current parameters and cannot flexibly adapt to individual differences and changes in user status; the isolation module realizes all-round electrical isolation between the power supply module and the human brain contact module to ensure the safety of the user during the stimulation process.

[0146] Specifically, the power supply module can obtain electricity by connecting to an external power supply or charging device through an external power supply interface, and can continuously provide a stable power supply. It can rely on an external power supply to ensure long-term stable operation, and can also be powered by a battery in mobile scenarios, enhancing the portability and flexibility of the device; the human brain contact module monitors user status, stimulation output, human body impedance and other information in real time, and accurately generates dynamic stimulation signals according to the user's real-time status to achieve personalized stimulation therapy, which helps to detect abnormal situations in a timely manner and ensure the safety and effectiveness of stimulation.

[0147] Specifically, the isolation module electrically isolates the power supply module from the human brain contact module, which can improve the stability and safety of the circuit, prevent power fluctuations, electromagnetic interference, etc. from affecting the human brain contact module, and avoid accidental electrical stimulation or damage to the user's brain; at the same time, the isolation module can effectively suppress common-mode interference, improve signal quality and acquisition accuracy, and ensure the reliable operation of the entire transcranial stimulation circuit.

[0148] The circuit design of the present application can be powered by a large-capacity, high-voltage battery, providing a long battery life and ensuring stable operation, realizing comprehensive electrical isolation measures and multi-parameter real-time monitoring functions, effectively preventing electrical interference and potential safety hazards, and ensuring the safety of users during the stimulation process; combined with the user's real-time status, dynamically generating stimulation signals, accurately locating the target area of ​​the brain and optimizing the stimulation strategy, it can more effectively stimulate the target area of ​​the brain, improve brain function, and improve the effect of treating brain-related diseases. The stimulation strategy is optimized in real time according to the user's real-time status, and the stimulation current parameters are adjusted in time to make the treatment process more scientific and reasonable, and improve the effect and efficiency of the treatment.

[0149] Furthermore, the power supply module includes:

[0150] External power interface, used to connect to an external power source or charging device;

[0151] Wireless network unit, responsible for remote communication and transmission of control instructions and user physiological data;

[0152] Battery units and battery charging units provide portable power and support isolated control of charging status.

[0153] In this embodiment, the power supply module includes an external power interface ①, a wireless network unit ②, a battery charging unit ③, and a battery unit ④. The external power interface connects to the battery charging unit to enable charging. The external power interface can introduce external electrical energy into the circuit system, enabling power input and providing the required power for the entire transcranial stimulation circuit. The external power interface facilitates the circuit to obtain external power. It can be connected to the mains power and converted to an appropriate voltage via a power adapter to power the device, or it can be connected to a mobile power source such as a power bank, meeting the power supply needs of the device in different scenarios and improving the device's flexibility and adaptability.

[0154] Specifically, the wireless network unit is a remote communication module, which is connected to the isolated digital unit for mutual communication and receives digital signals from the isolated digital unit (the signal originates from the programmable processing unit), thereby realizing remote communication between the device and the outside world. Doctors or researchers can send control instructions and adjust stimulation current parameters through mobile phones, computers and other terminal devices away from the device, thereby facilitating remote treatment and monitoring of patients. At the same time, the user's physiological data can be transmitted to the cloud or other data processing centers in real time for analysis and storage, so that doctors can understand the patient's treatment status in a timely manner, thereby improving the convenience and intelligence of treatment.

[0155] Specifically, the battery charging unit is a battery charging circuit, which is connected to the battery unit and the isolated digital unit to charge the battery unit. The isolated digital unit can control whether the battery is charged. The battery unit is a rechargeable lithium battery that provides power to the entire circuit and is easy to carry. It is connected to the isolated analog unit and the isolated power supply unit. The isolated analog unit collects the battery voltage, and the isolated power supply unit isolates the battery voltage and outputs it to the circuit in the area that contacts the human brain. The battery unit and the battery charging unit provide a portable power supply, so that the human brain transcranial stimulation circuit can work independently without an external power supply, which is convenient for patients to use in mobile or outdoor scenarios, and improves the portability and applicability of the device. The battery charging unit supports charging status isolation control, which can ensure the stability and safety of the circuit system during the charging process, prevent interference during the charging process from affecting other parts of the circuit, and also protect the battery life and extend the overall use time of the device.

[0156] Furthermore, the human brain contact module includes:

[0157] A programmable processing unit dynamically adjusts the stimulation current parameters of the stimulation signal according to the user's real-time status;

[0158] DAC control unit, converting digital signals into analog stimulation waveforms;

[0159] Voltage, current and temperature acquisition unit to monitor user status, stimulation output and human body impedance in real time;

[0160] The human brain external electrode interface unit is responsible for connecting to the multi-channel electrode array and configuring multiple sensors to collect real-time information about the user's brain status;

[0161] The stimulation generating circuit unit generates a dynamic stimulation signal based on an operational amplifier and an instrumentation amplifier according to the stimulation current parameters.

[0162] In this embodiment, the human brain contact module includes a programmable processing unit ⑧, a DAC control unit ⑨, a voltage, current, and temperature acquisition unit ⑩, a human brain external electrode interface unit ⑪, and a stimulation generation circuit unit ⑫. The programmable processing unit is a programmed MCU or FPGA chip connected to the isolated digital unit, isolated analog unit, voltage, current, and temperature acquisition unit, DAC (digital-to-analog) control unit, and stimulation generation circuit unit. It uses software to collect analog and digital signals and simultaneously control the desired stimulation signal output. The programmable processing unit can analyze and process the user's real-time status data to determine the user's current brain state. Based on this determination, it dynamically adjusts the stimulation current parameters of the stimulation signal (such as the magnitude, frequency, and duration of the stimulation current), thereby achieving personalized and precise brain stimulation. By responding to changes in the user's brain state in real time and adjusting the stimulation current parameters in a timely manner, the stimulation is more precise and effective.

[0163] Specifically, the DAC (digital-to-analog) control unit is a circuit consisting of a DAC chip and an instrumentation amplifier. It is connected to the programmable processing unit and the stimulation generation circuit unit. The DAC control unit's circuit output voltage is set by a program to the stimulation generation circuit unit, achieving real-time control of the output waveform. This converts the digital signal output by the programmable processing unit into an analog stimulation waveform, ensuring the accuracy and stability of the stimulation signal. The voltage, current, and temperature acquisition unit, consisting of a resistor divider, capacitor filter, and other circuits, converts the current, voltage, and temperature data from the external electrode interface of the human body into analog quantities of appropriate proportions before being sent to the programmable processing unit for detection. Sensors and measurement circuits monitor the user's status, stimulation output, and body impedance in real time. Real-time monitoring of voltage, current, and temperature allows for timely detection of abnormalities such as excessive current or high temperature to prevent harm to the user. Monitoring body impedance provides information on the contact between the human body and the electrodes, as well as the physiological state of human tissue, providing a basis for adjusting stimulation current parameters and improving stimulation accuracy.

[0164] Specifically, the human brain external electrode interface unit is an external electrode connector interface, connected to the voltage, current, and temperature acquisition unit and the stimulation generation circuit unit. These two circuits are used to stimulate the human brain and sample real-time data. It is responsible for connecting the multi-channel electrode array and configuring multiple sensors to collect real-time information about the user's brain status, achieving multi-channel data acquisition and comprehensively obtaining physiological information from the user's brain. The stimulation generation circuit unit is a circuit composed of an op amp, an instrumentation amplifier, and resistors and capacitors. It is connected to the programmable processing unit, the DAC control unit, and the human body external electrode interface unit. It is the core waveform generation circuit for various waveforms required by the human body. Based on the stimulation current parameters provided by the programmable processing unit, it generates dynamic stimulation signals based on the op amp (operational amplifier) ​​and instrumentation amplifier. The stimulation generation circuit unit can generate precise stimulation signals based on different stimulation current parameters to achieve precise stimulation of the target area of ​​the user's brain. The op amp and instrumentation amplifier can ensure the quality of the stimulation signal.

[0165] Furthermore, the isolation module includes:

[0166] Isolation digital unit, responsible for the electrical isolation of digital signals;

[0167] The isolation analog unit is responsible for processing analog signals and isolating the interference of power supply on analog circuits;

[0168] The isolated power supply unit realizes complete electrical isolation between the power supply module and the human brain contact module.

[0169] In this embodiment, the isolation module includes an isolated digital unit ⑤, an isolated analog unit ⑥, and an isolated power supply unit ⑦. The isolated digital unit includes an isolated digital chip, connected to the programmable processing unit, and receives and transmits communication signals from the wireless network unit (the signals originally originate from the wireless network unit), facilitating remote operation of the system. During signal transmission, it electrically isolates the two circuit domains, preventing electrical interference caused by direct connection and effectively isolating electrical interference between different circuit modules. This ensures stable transmission of digital signals, reduces signal distortion and bit error rates, and improves system reliability. It also prevents circuit damage and safety hazards caused by potential differences or short circuits, protecting the safety of equipment and users.

[0170] Specifically, the isolated analog unit is an isolated analog chip. In the power supply area, it is connected to the battery unit to detect the battery voltage and obtain battery capacity information. In the human brain contact area, it is connected to the programmable processing unit to obtain battery voltage data. Through devices such as isolation amplifiers, the analog signal is isolated and transmitted from one circuit domain to another circuit domain. Electrical isolation is achieved during the transmission process, effectively isolating the interference of the power supply on the analog circuit, reducing the distortion and noise interference of the analog signal, and improving the accuracy and stability of the analog signal. The isolated power supply unit is an isolated DC / DC power supply module that is connected to the battery unit and supplies power to the entire circuit in the human brain contact area. It completely electrically isolates the power supply module and the human brain contact module, ensuring the independent operation and safety of the two modules, preventing safety accidents caused by power failure or interference, and protecting the safety of users and equipment. It also reduces mutual interference between modules, ensures the independent and stable operation of each module, and improves the performance and reliability of the entire transcranial stimulation circuit system.

[0171] Furthermore, the isolated power supply unit includes at least two isolated DC / DC power supply sub-units, which respectively provide ±15V stimulation op amp power supply and 5V logic power supply, and integrate overvoltage or overcurrent protection circuits to cut off the output through the OD pin in an abnormality.

[0172] In this embodiment, the isolated power supply unit uses DC / DC conversion technology to convert the input DC voltage into a DC voltage with an amplitude of ±15V through a conversion circuit composed of switching circuits, inductors, capacitors and other components, providing a suitable working power supply for the stimulation op amp; the 5V logic power supply converts the input DC voltage into a 5V DC voltage; through different isolated power supply sub-units, specific voltages are provided to meet the requirements of different circuit components, ensuring that the stimulation op amp and logic circuit can operate at their respective appropriate voltages, thereby improving the stability and reliability of the entire circuit system; the isolated DC / DC conversion can effectively isolate the electrical interference on the power supply side and the load side, reduce the impact of power supply fluctuations, noise, etc. on the load circuit, and also prevent the load circuit fault from being fed back to the power supply side, thereby improving the system's anti-interference ability and safety.

[0173] Specifically, the integrated overvoltage or overcurrent protection circuit, when the output voltage exceeds the set overvoltage threshold, the comparator and other circuits will detect this abnormal situation and trigger the protection mechanism, so that the protection circuit will be activated and the output will be cut off to prevent the excessive voltage from damaging subsequent circuit components; when the detected current exceeds the set overcurrent threshold, the protection circuit will respond quickly and cut off the circuit by controlling the switching diode, etc., to avoid excessive current passing through the circuit components and prevent the components from being damaged due to overheating and other reasons; the overvoltage or overcurrent protection circuit greatly improves the safety of the circuit, and can effectively protect the various components in the circuit from damage by excessive voltage or current, extend the service life of the circuit components, and reduce the risk of circuit failure.

[0174] Specifically, the OD pin, or open drain pin, can receive control signals from the overvoltage or overcurrent protection circuit. When the protection circuit detects an abnormal situation and is triggered, it sends a specific level signal to the OD pin. By controlling the level state of the OD pin, the switching device connected to it is controlled to achieve the function of cutting off the output. By controlling a single pin, the on-off control of the entire output circuit can be achieved, which can quickly respond to abnormal situations, cut off the output in time, and play a role in protecting circuits and equipment.

[0175] For example, Figure 3 As shown, the power supply area and the area in contact with the human brain are isolated by an isolated power supply unit. Even if a major fault occurs while the external power interface is connected to an external power source for charging (for example, the voltage burns out the circuit in the power supply area), the high voltage will not enter the circuit in the area in contact with the human brain, and will not cause harm to the human body. Transcranial stimulation requires a portable rechargeable battery. The isolated power supply unit has a wide range of high voltage power supply capabilities, so it can be powered by a large-capacity, high-voltage battery, providing a long battery life and ensuring stable operation. The wireless network unit communicates with the programmable processing unit via the isolated digital unit. The battery unit can detect the battery voltage for the programmable processing unit through the isolated analog unit, and can provide a charging prompt.

[0176] Specifically, the voltage, current and temperature sampling units are connected to the external electrode interface of the human brain to monitor the voltage and current waveforms of human brain stimulation in real time, thereby realizing real-time protection and control of the transcranial stimulation waveform; the DAC control unit outputs a small voltage to the instrumentation amplifier chip through the DAC chip, which is amplified and output to the + pole of the stimulation op amp; the stimulation generating circuit is connected to the human brain electrode interface through the sampling resistor, and the voltage or current signal of the human brain is connected to the - pole of the stimulation op amp after passing through the instrumentation amplifier chip. The stimulation op amp makes real-time adjustments based on the voltage comparison between the + and - poles, and closes the loop to adjust the stimulation waveform output.

[0177] Specifically, the stimulation generation circuit adopts a function with OD switch protection. If an abnormality is detected in the human body stimulation voltage or current, the OD pin will be pulled low and the stimulation op amp will turn off the output; the isolation power supply circuit has two channels, one provides ±15V power supply for the stimulation op amp, and the other provides 5V power supply for the programmable processing unit. When an abnormality is detected in the human body stimulation voltage or current, the Ctrl pin of the isolated power supply with ±15V power supply can be pulled low, and the ±15V power supply is turned off to protect the safety of the human brain. When the stimulation is not started, the Ctrl pin will also be pulled low to turn off the ±15V power supply to save battery power.

[0178] Specifically, the isolated digital unit has two chips, the isolated analog unit has one chip, and the programmable processing unit performs digital communication with the wireless network unit, power switch control, and battery voltage detection, all of which require isolated input and output through the isolation chip.

[0179] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A human brain transcranial stimulation circuit, characterized in that: include: A brain data acquisition module collects brain state data of the user to be stimulated; a sending module, configured to send the brain state data to a processing device, so that the processing device generates generation parameters of a stimulation signal according to the brain state data; a stimulation signal generating module, receiving generation parameters of the stimulation signal and generating the stimulation signal according to the generation parameters; The processing device generates generation parameters of the stimulation signal based on the brain state data, including: analyzing the connection relationship between brain regions based on the brain state data to construct a brain network map; analyzing abnormal areas in the brain network map based on the relationship between the node degree value and the medium centrality value of each node in the brain network map and the corresponding preset values, combined with a preset abnormality detection model, to obtain brain target areas; generating a stimulation strategy based on the brain target area; and optimizing the stimulation strategy based on the brain state data to achieve closed-loop control of the stimulation strategy; Based on the relationship between the node degree value and the medium centrality value of each node in the brain network map and the corresponding preset value, combined with a preset anomaly detection model, the abnormal area in the brain network map is analyzed to obtain the brain target area, including: Calculate the node degree and medium centrality value of each node in the brain network map; The brain region corresponding to the node whose node degree value is greater than the preset node degree value and whose medium centrality value is greater than the preset medium centrality value is taken as the core brain region; Screening abnormal areas in the core brain region using a preset abnormality detection model to obtain abnormal core brain regions; Screening out brain regions connected to the abnormal core brain region whose connection relationship values ​​are greater than a preset first abnormal threshold or whose connection relationship values ​​are less than a preset second abnormal threshold as abnormal-related brain regions, wherein the first abnormal threshold is greater than the second abnormal threshold, and the connection relationship values ​​are corresponding values ​​in the connection relationship matrix; The abnormal core brain region and the abnormal related brain region are combined as the target brain region.

2. A human brain transcranial stimulation circuit according to claim 1, characterized in that: Analyze the connectivity between brain regions based on the brain state data and construct a brain network map, including: According to the resting-state EEG data in the brain state data, the phase value of each channel signal is calculated, and the phase synchronization matrix is ​​constructed by combining the phase synchronization degree between every two channel signals; Based on the brain state data, the functional connection strength between different brain regions is calculated to obtain the functional connection matrix; Dynamically fusing the phase synchronization matrix and the functional connectivity matrix to obtain a connectivity relationship matrix; Based on the connection relationship matrix, a brain network map is constructed, wherein each brain region in the brain network map serves as each node, and each node is connected by using the corresponding value in the connection relationship matrix as the weight of the edge.

3. A human brain transcranial stimulation circuit according to claim 1, characterized in that: Generate a stimulation strategy based on the target brain area, including: According to the connection relationship between each brain area in the target brain region, the stimulation sequence of the brain areas is analyzed to obtain the multi-channel stimulation path; According to the multi-channel stimulation path, the stimulation current parameters of each channel are calculated by a preset stimulation parameter calculation model to generate a stimulation strategy.

4. A human brain transcranial stimulation circuit according to claim 3, characterized in that: The method of analyzing the brain region stimulation sequence based on the connection relationship between each brain region in the target brain region to obtain a multi-channel stimulation path includes: Construct a brain region causal model based on the connection relationship between each brain region in the target brain area; Calculating the influence of each brain region in the target brain region on the overall target brain region when it is used as a stimulation target based on the brain region causal model; determining a target priority for each brain region in the target brain region according to the impact degree value; The brain region with the highest target priority is used as the main stimulation target, and the order of brain region stimulation is determined according to the target priority to obtain a multi-channel stimulation path.

5. A human brain transcranial stimulation circuit according to claim 3, characterized in that: According to the multi-channel stimulation path, the stimulation current parameters of each channel are calculated by a preset stimulation parameter calculation model to generate a stimulation strategy, including: According to the multi-channel stimulation path and the user's real-time status, the stimulation current parameters of each channel are obtained through a preset stimulation parameter calculation model, wherein the stimulation current parameters include the magnitude, frequency and duration of the stimulation current; The multi-channel stimulation path and the stimulation current parameters of each channel are combined to obtain a stimulation strategy.

6. The human brain transcranial stimulation circuit according to claim 1, characterized in that: Optimizing the stimulation strategy based on the brain state data to achieve closed-loop control of the stimulation strategy includes: Construct a state vector based on the user's real-time status; Based on the stimulation strategy, an action vector is constructed; According to a preset time period, the state vector is updated according to the user state after stimulation to obtain an updated state vector; Calculating a reward value for the current stimulation process according to the difference between the state vector and the updated state vector; According to the reward value, the stimulation strategy is optimized through a preset strategy optimization model to obtain an updated action vector; Based on the updated motion vector, the user is stimulated for the next time period until the entire stimulation process is completed.

7. The human brain transcranial stimulation circuit according to claim 1, characterized in that: The collecting of the brain state data of the user to be stimulated further includes performing data cleaning, standardization and artifact removal processing on the collected brain state data of the user to be stimulated.

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