Human brain transcranial stimulation circuit and human brain transcranial stimulation method

Abstract

The invention relates to the technical field of transcranial electrical stimulation, and discloses a human brain transcranial stimulation circuit and a human brain transcranial stimulation method, and the method comprises the steps: obtaining the brain state data of a to-be-stimulated user; according to the brain state data, analyzing a connection relationship between brain regions, 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 atlas and the corresponding preset value, analyzing an abnormal region in the brain network atlas in combination with a preset anomaly detection model to obtain a brain target region; generating a stimulation strategy according to the brain target area; optimizing the stimulation strategy in combination with the brain state data to realize closed-loop control of the stimulation strategy; by combining the real-time state of the user, dynamically generating the stimulation signal, accurately positioning the brain target area and optimizing the stimulation strategy, the brain target area can be stimulated more effectively, the treatment process is more scientific and reasonable, and the treatment effect and efficiency are improved.

Description

Technical Field

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

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

[0003] For example, the Chinese patent with the authorization announcement number CN116983546B discloses a transcranial electrical stimulation device applied to the motor cortex and its control method, including: a main body, stimulation electrodes, detection electrodes, a controller, and a switching module. The main body is a head-mounted type and includes a strap; the stimulation electrodes are arranged on the inner side of the main body, and the positions of the stimulation electrodes on the main body correspond to the positions of the brain motor areas. The stimulation electrodes are used to stimulate the brain motor areas with weak current. The brain motor areas include the left central area and the right central area of the brain; the detection electrodes are arranged on the inner side of the main body, and the detection electrodes are used to detect the current electroencephalogram signal; 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 working states of the stimulation electrodes and the detection electrodes.

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

[0005] Aiming at the deficiencies of the prior art, the main purpose of the present invention is to provide a human brain transcranial stimulation circuit and a human brain transcranial stimulation method, which can effectively solve the problems in the background art. The specific technical solutions of the present invention are as follows:

[0006] A human brain transcranial stimulation method, implemented by a processing device and executing a specific software method, includes:

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

[0008] According to the brain state data, analyzing the connection relationship between brain regions and constructing a brain network map, wherein each node in the brain network map includes a node degree value and a betweenness centrality value;

[0009] Based on the relationships between the node degree value and the betweenness centrality value of each node in the brain network atlas and their corresponding preset values, and in combination with a preset anomaly detection model, analyze the abnormal regions in the brain network atlas to obtain the target brain regions;

[0010] Generate a stimulation strategy according to the target brain regions;

[0011] Combine the brain state data to optimize the stimulation strategy and achieve closed-loop control of the stimulation strategy.

[0012] Specifically, according to the brain state data, analyze the connection relationships between brain regions and construct a brain network atlas, including:

[0013] Calculate the phase values of the signals of each channel based on the resting-state EEG data in the brain state data, and construct a phase synchronization matrix in combination with the phase synchronization degree between every two channel signals;

[0014] Calculate the functional connection strength between different brain regions according to the brain state data to obtain a functional connection matrix;

[0015] Dynamically fuse the phase synchronization matrix and the functional connection matrix to obtain a connection relationship matrix;

[0016] Construct a brain network atlas based on the connection relationship matrix, where each brain region in the brain network atlas is used as each node, and each node is connected through the corresponding value in the connection relationship matrix as the weight of the edge.

[0017] Specifically, the step of analyzing the abnormal regions in the brain network atlas based on the relationships between the node degree value and the betweenness centrality value of each node in the brain network atlas and their corresponding preset values, and in combination with a preset anomaly detection model to obtain the target brain regions includes:

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

[0019] Regard the brain regions corresponding to the nodes with the node degree value greater than the preset node degree value and the betweenness centrality value greater than the preset betweenness centrality value as the core brain regions;

[0020] Screen the abnormal regions in the core brain regions through a preset anomaly detection model to obtain abnormal core brain regions;

[0021] Screen out the brain regions with the connection relationship value greater than the preset first anomaly threshold or the connection relationship value less than the preset second anomaly threshold among the brain regions connected to the abnormal core brain regions as the anomaly-related brain regions, where the first anomaly threshold is greater than the second anomaly threshold, and the connection relationship value is the corresponding value in the connection relationship matrix;

[0022] Combine the abnormal core brain region and the abnormally related brain regions as the brain target region.

[0023] Specifically, according to the brain target region, a stimulation strategy is generated, including:

[0024] According to the connection relationship between each brain region in the brain target region, analyze the brain region stimulation order to obtain a multi-channel stimulation path;

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

[0026] Specifically, the step of analyzing the brain region stimulation order according to the connection relationship between each brain region in the brain target region to obtain a multi-channel stimulation path includes:

[0027] Construct a brain region causal model according to the connection relationship between each brain region in the brain target region;

[0028] According to the brain region causal model, calculate the influence degree value of each brain region in the brain target region on the overall brain target region when it is used as a stimulation target point;

[0029] According to the influence degree value, determine the target point priority of each brain region in the brain target region;

[0030] Take the brain region with the highest target point priority as the main stimulation target point, and determine the brain region stimulation order according to the target point priority to obtain a multi-channel stimulation path.

[0031] Specifically, according to the multi-channel stimulation path, calculating the stimulation current parameters of each channel through a preset stimulation parameter calculation model to generate a stimulation strategy includes:

[0032] According to the multi-channel stimulation path combined with the user's real-time state, through a preset stimulation parameter calculation model, obtain the stimulation current parameters of each channel, where the stimulation current parameters include the magnitude, frequency, and duration of the stimulation current;

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

[0034] Specifically, combining the brain state data to optimize the stimulation strategy to achieve closed-loop control of the stimulation strategy includes:

[0035] Construct a state vector according to the user's real-time state;

[0036] Construct an action vector according to the stimulation strategy;

[0037] Update the state vector according to the user state after stimulation in accordance with a preset time period to obtain an updated state vector;

[0038] Calculate the reward value of the current stimulation process according to the difference between the state vector and the updated state vector;

[0039] Optimize the stimulation strategy through a preset policy optimization model according to the reward value to obtain an updated action vector;

[0040] Perform stimulation on the user in the next time period based on the updated action vector until the overall stimulation process is completed.

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

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

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

[0044] 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;

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

[0046] A transcranial brain stimulation circuit for the human brain, includes:

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

[0048] A sending module, which sends 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 generation module, which receives the generation parameters of the stimulation signal and generates a stimulation signal according to the generation parameters;

[0050] Wherein, the processing device generates generation parameters of a stimulation signal according to the brain state data, including: analyzing the connection relationship between brain regions according to the brain state data, constructing a brain network map; based on the relationship between the degree value and the betweenness centrality value of each node in the brain network map and their corresponding preset values, combining a preset anomaly detection model, analyzing the abnormal regions in the brain network map to obtain the brain target regions; generating a stimulation strategy according to the brain target regions; combining the brain state data to optimize the stimulation strategy to achieve closed-loop control of the stimulation strategy.

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

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

[0053] Figure 1 It is a flowchart of a method for transcranial brain stimulation of the human brain in Embodiment 1 of the present invention;

[0054] Figure 2 It is a schematic diagram of brain target area recognition in Embodiment 1 of the present invention;

[0055] Figure 3 It is a schematic structural diagram of a transcranial brain stimulation circuit for the human brain in Embodiment 3 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following detailed description of the specific embodiments of the present invention will be given in conjunction with the accompanying drawings of the specification.

[0057] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0058] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments.

[0059] Embodiment 1

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

[0061] S101. Obtain the brain state data of the user to be stimulated;

[0062] S102. Analyze the connection relationship between brain regions according to 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 betweenness centrality value;

[0063] S103. Based on the relationship between the node degree value and the betweenness centrality value of each node in the brain network map and their corresponding preset values, and combined with a preset anomaly detection model, analyze the abnormal regions in the brain network map to obtain the brain target regions;

[0064] S104. Generate a stimulation strategy according to the brain target regions;

[0065] S105. Combine the brain state data to optimize the stimulation strategy and realize the closed-loop control of the stimulation strategy.

[0066] In this embodiment, a brain network map is constructed based on the real-time brain state of the user to accurately locate the abnormal regions, and the stimulation strategy is optimized in real time by combining closed-loop control, making the stimulation more targeted and improving the treatment effect. The stimulation strategy is optimized according to the real-time reaction of the brain to adapt to the dynamic changes of the brain, enhancing the treatment safety and effectiveness. It solves the problems of single stimulation method and poor treatment effect caused by insufficient consideration of individual brain network differences in the prior art, and avoids the problem that the existing technology lacks closed-loop control and the stimulation current cannot be adjusted in real time according to the user's own state.

[0067] In this embodiment, the electroencephalogram (EEG) signals of the user are collected through the electrodes in the circuit device that are in contact with the human brain, and the physical state data of the user are collected in real time through a temperature and humidity sensor. The real-time EEG signals, temperature data, humidity data, and resistance data of the user are obtained. The collected real-time state data of the user can dynamically reflect the current activity state of the brain, capture the changes in the brain state over time, and help 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, improve the data quality, and use the preprocessed state data to analyze the correlation, synchrony, and other characteristics between the signals in different brain regions, determine the functional and structural connection relationships between brain regions, and construct a brain network map. In this embodiment, the brain is divided into regions based on the resting-state functions and cellular structures of different parts. There are many current methods for brain region division. In this embodiment, the Brodmann area division is adopted, and each hemisphere of the human brain is divided into 52 regions to obtain different brain regions. The connection relationships between brain regions are analyzed based on the correlation between different brain regions, and the correlation coefficients between brain regions are calculated by combining the functional and structural correlations between brain regions. The correlation coefficients are used as the weights of the connection edges, and the brain regions are connected to construct a brain network map. Through the brain network map, the mutual relationships between the various regions of the brain can be intuitively displayed, which helps to understand the functional organization and information transmission mode of the brain, and thus analyze the abnormal brain regions of the user.

[0068] Specifically, the constructed brain network map is analyzed, and the abnormal regions 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 the connection situation between a certain brain region and other brain regions shows abnormal changes, it indicates that there is an abnormal situation in this brain region. This brain region and the abnormal regions connected to it are determined as the stimulation target regions. By accurately determining the target regions in the brain, a clear target is provided for targeted transcranial stimulation, improving the accuracy and effectiveness of treatment or intervention.

[0069] Furthermore, after determining the brain stimulation target regions, according to factors such as the location, functional characteristics, and severity of the abnormal situation of the determined brain target regions, combined with the principles and technical parameters of transcranial stimulation, the magnitude, frequency, and stimulation time of the stimulation current are set to formulate corresponding stimulation plans. Through targeted stimulation strategies, it can directly act on the abnormal regions of the brain and regulate abnormal neural activities, hopefully improving brain function and relieving related symptoms.

[0070] Meanwhile, during the implementation of the stimulation strategy, real-time status data of the user is continuously collected and compared with the data before stimulation and the expected brain state changes. Based on 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 subsequent stimulation processes, continuously monitored and adjusted until the expected brain state or therapeutic effect is achieved; through closed-loop control, the stimulation strategy can be dynamically optimized according to the real-time response of the brain, improving the accuracy and effectiveness of the stimulation, avoiding over-stimulation or under-stimulation, and at the same time better adapting to individual differences, improving the therapeutic effect and safety.

[0071] Further, based on the brain state data, analyze the connection relationships between brain regions and construct a brain network map, including:

[0072] S201: Calculate the phase values of the signals of each channel according to the resting-state EEG data in the brain state data, and construct a phase synchronization matrix by combining the phase synchronization degree between the signals of every two channels;

[0073] S202: Calculate the functional connection strength between different brain regions according to the brain state data to obtain a functional connection matrix;

[0074] S203: Dynamically fuse the phase synchronization matrix and the functional connection matrix to obtain a connection relationship matrix;

[0075] S204: Based on the connection relationship matrix, construct a brain network map, where each brain region in the brain network map is used as each node, and the corresponding values in the connection relationship matrix are used as the weights of the edges to connect between each node.

[0076] This embodiment combines the collected resting-state EEG data of the user, analyzes the phase synchronization information of the data and the functional connection relationships between different brain regions, dynamically fuses the phase synchronization matrix and the functional connection matrix, so that the obtained connection relationship matrix can more accurately reflect the actual connection situation between brain regions, and then constructs a more accurate brain network map; by constructing the brain network map, the actual situation of the user's brain can be accurately reflected, improving the accuracy of the stimulation position and avoiding the problem of poor therapeutic effect caused by inaccurate stimulation areas of the human brain in the prior art.

[0077] In this embodiment, the preprocessed EEG signals are analyzed. The collected EEG signals are multi-channel signals. First, the phase values of the signals of each channel are calculated, and the degree of phase synchronization is calculated based on the phase values, which reflects the consistency of the phase changes of the signals of two channels. When there is a functional connection in the neural activities of two brain regions, the phase changes of their corresponding EEG channel signals are synchronous. By calculating the phase values of the signals of each channel and the phase synchronization index between every two channels, the functional coupling relationship between 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] In the formula, 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 value of the phase synchronization index is closer to 1, it indicates that the phase synchronization degree of the signals of the two channels is higher. The phase synchronization matrix can reveal the functional connection relationship between brain regions from the phase level, capture the cooperative activity information between brain regions, and provide relevant relationship information for brain network connection.

[0080] Specifically, according to the EEG data in the real-time state data, the functional connection strength between different brain regions is analyzed. The functional connection 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 every two channel signals is calculated, and a functional connection matrix is constructed. The elements in the functional connection matrix are the Pearson correlation coefficients between the signals of the corresponding brain regions, which reflect the functional connection strength between the corresponding brain regions. The functional connection matrix intuitively shows the tightness of the functional connections between different brain regions. By analyzing the functional connection matrix, the cooperative working mode between brain regions in different states of the brain can be found, and it can assist in analyzing the functional abnormalities in the disease state.

[0081] Furthermore, the phase synchronization matrix and the functional connection matrix are dynamically fused. Different weights can be set according to the analysis focus and calculation requirements. In this embodiment, the average weighted summation is adopted. Through dynamic fusion, the information of these two matrices is integrated, comprehensively considering the features of both phase synchronization and functional connection strength, and a connection relationship matrix that is more comprehensive and can better reflect the actual connection relationship between brain regions is obtained. The combined phase synchronization matrix and functional connection matrix reflect the connection relationship information between brain regions from different angles, avoiding the problem that a single matrix cannot comprehensively and accurately describe the brain network connection.

[0082] Specifically, a brain network atlas is constructed based on the connection relationship matrix. The brain regions are regarded as nodes, the connection relationships between the brain regions are regarded as edges, and the weights of the edges are determined by the values of the corresponding elements in the connection relationship matrix. The constructed brain network atlas can intuitively display the connection patterns between various brain regions of the brain. The weights of the edges reflect the tightness or strength of the connections between the brain regions. Through the atlas, the overall architecture of the brain functional network and the mutual relationships between various brain regions can be clearly observed, so as to identify abnormal regions according to the states of the brain regions and formulate corresponding treatment plans.

[0083] Further, based on the relationships between the node degree values and the betweenness centrality values of each node in the brain network atlas and their corresponding preset values, and in combination with a preset anomaly detection model, the abnormal regions in the brain network atlas are analyzed to obtain the target brain regions, including:

[0084] S301. Calculate the node degree values and the betweenness centrality values of each node in the brain network atlas;

[0085] S302. Take the brain regions corresponding to the nodes whose node degree values are greater than the preset node degree value and whose betweenness centrality values are greater than the preset betweenness centrality value as the core brain regions;

[0086] S303. Through the preset anomaly detection model, screen the abnormal regions in the core brain regions to obtain abnormal core brain regions;

[0087] S304. Screen out the brain regions whose connection relationship values are greater than the preset first anomaly threshold or less than the preset second anomaly threshold among the brain regions connected to the abnormal core brain regions as the abnormally related brain regions, where the first anomaly threshold is greater than the second anomaly threshold, and the connection relationship value is the corresponding value in the connection relationship matrix;

[0088] S305. Combine the abnormal core brain regions and the abnormally related brain regions as the target brain regions.

[0089] As Figure 2 shown, in this embodiment, the core brain regions are identified according to the brain network atlas, the abnormal brain regions are screened out in the core brain regions, and other abnormal brain regions are determined through the brain regions with abnormal connection relationships with the abnormal brain regions. Through multi-step quantitative analysis and model screening, the core regions (abnormal core brain regions) related to functional abnormalities in the brain and the related regions (abnormally related brain regions) affected by them can be accurately located, improving the accuracy of determining the target brain regions, avoiding the problem of unclear positioning of the stimulation regions in the prior art, and the clear target brain regions provide clear targets for transcranial stimulation, enabling the treatment to act more specifically on the abnormal brain regions, improving the treatment effect, while reducing unnecessary stimulation of normal brain regions and reducing the treatment risk.

[0090] In this embodiment, according to the brain network atlas, the node degree value and the betweenness centrality value of each node in the brain network atlas are calculated. The node degree value can measure the number of other nodes directly connected to a node, reflecting the local importance of the node in the network. The higher the node degree value, the more other nodes the node is directly connected to, and it plays a key role in the information transmission and functional integration of the local network. The betweenness 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 overall network information transmission. The higher the betweenness centrality value of a node, the greater the impact on the overall connectivity and information propagation efficiency of the network. By calculating these two values, the importance and status of each node (corresponding brain region) in the brain network atlas can be evaluated from different perspectives, which helps to identify the brain regions that play a key role in local connection and overall information transmission from the complex brain network.

[0091] Specifically, the core brain regions are screened according to the calculated node degree value and betweenness centrality value. The brain regions corresponding to the nodes with a node degree value greater than the preset node degree value and a betweenness centrality value greater than the preset betweenness centrality value are used as the core brain regions. The preset node degree value and betweenness centrality value can be empirical thresholds obtained through statistical analysis of a large amount of normal brain network atlas data. When the node degree value and betweenness centrality value corresponding to a brain region are both higher than these thresholds, it indicates that the brain region has a high degree of connectivity and information control ability in the network and is in a core position in the functional integration and information transmission of the brain. Screening out these core brain regions can specifically analyze the regions that have a greater impact on brain function, narrow the scope of abnormal analysis, improve the analysis efficiency, and these core brain regions will change significantly when the brain function is abnormal, providing a key entry point for discovering abnormal regions.

[0092] Furthermore, the abnormal brain regions are identified among the screened core brain regions. Through a preset anomaly detection model, the abnormal core brain regions are identified. The anomaly detection model is specifically a random forest model. The anomaly detection model is trained based on a large amount of known normal and abnormal brain network data to obtain a pre-trained anomaly detection model. The preset anomaly detection model learns the characteristic patterns of the core brain regions in the normal brain network and the characteristic changes in abnormal situations, and classifies and judges the input core brain region data. When the data characteristics of the core brain region deviate from the normal pattern characteristics learned by the model, the model identifies it as abnormal. The abnormal core brain regions are the root or key parts of brain function abnormalities. Accurately identifying them helps to deeply understand the pathogenesis of brain diseases and formulate targeted treatment strategies.

[0093] Specifically, after identifying the abnormal core brain region, further screening is performed on the related brain regions with abnormal connections to the abnormal core brain region to obtain the abnormally related brain regions. When the core brain region is abnormal, it will affect other connected brain regions through the connection relationship. The connection relationship value reflects the tightness or strength of the connection between brain regions. 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 region connected to the abnormal core brain region is greater than the first abnormal threshold, it indicates 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 regions with abnormal connection relationships to the abnormal core brain region are all affected by corresponding functional abnormalities due to the user's actual brain disease. Comprehensively identifying the brain regions with abnormal connection relationships to the abnormal core brain region expands the analysis scope of the brain function abnormal regions, helps to understand the spread and influence mechanism of brain diseases from the network level, and provides a basis for formulating a more comprehensive treatment plan.

[0094] Combining the abnormal core brain region and the abnormally related brain regions as the brain target region, the abnormal core brain region is the key part of the abnormal brain function, and the abnormally related brain regions are the regions with abnormal connection relationships affected by the abnormal core brain region. Combining these two types of brain regions can more comprehensively cover the regions related to abnormal brain function in the brain, jointly constituting a network related to brain diseases or dysfunctions. Applying corresponding stimulation to the brain target region can more effectively improve brain function and treat related diseases.

[0095] Furthermore, according to the brain target region, a stimulation strategy is generated, including:

[0096] S401. Analyze the brain region stimulation order based on the connection relationship between each brain region in the brain target region to obtain a multi-channel stimulation path;

[0097] S402. According to the multi-channel stimulation path, calculate the stimulation current parameters of each channel through a preset stimulation parameter calculation model to generate a stimulation strategy.

[0098] In this embodiment, by constructing a brain region causal model and quantifying the influence degree value, the main stimulation target and the multi-channel stimulation path are accurately determined, and the stimulation current parameters are generated in combination with the individual's real-time state, enabling the stimulation strategy to be accurately designed according to the characteristics of the brain target region and individual differences, improving the accuracy of treatment; considering the causal relationship and priority between brain regions and performing multi-channel stimulation in an orderly manner can give full play to the synergistic effect of stimulation, maximize the improvement of abnormal brain function, and improve the treatment effect.

[0099] Further, analyzing the brain region stimulation sequence according to the connection relationship between each brain region in the brain target region to obtain a multi-channel stimulation path includes:

[0100] S501. Construct a brain region causal model according to the connection relationship between each brain region in the brain target region;

[0101] S502. According to the brain region causal model, calculate the influence degree value of each brain region in the brain target region on the overall brain target region when it is used as a stimulation target;

[0102] S503. According to the influence degree value, determine the target priority of each brain region in the brain target region;

[0103] S504. Take the brain region with the highest target priority as the main stimulation target, and determine the order of brain region stimulation according to the target priority to obtain a multi-channel stimulation path.

[0104] In this embodiment, based on the connection relationship between brain regions in the brain target region, a causal inference algorithm is used to construct a brain region causal model. Specifically, the Granger causality analysis method is used. By comparing the difference in the prediction accuracy of the time series of another brain region when including and not including the time series data of a certain brain region, the causal relationship is judged. 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; by traversing and calculating the causal relationship parameters between every two brain regions in the brain target region, a brain region causal model is constructed; which helps to deeply understand the interaction mechanism between brain regions when the brain function is abnormal, so as to formulate the corresponding stimulation path.

[0105] Specifically, based on the constructed brain region causal model, when a certain brain region is set as a stimulation target, a series of chain reactions caused by simulating the stimulation of this brain region are calculated according to the causal relationship path and intensity described in the model, and the influence degree value on the overall brain target region is calculated; by calculating the influence degree value, the effects of the stimulation target on other brain regions through direct and indirect causal relationships are comprehensively considered, reflecting the influence of this brain region in the entire brain target region network; the formula for calculating the influence degree value is as follows:

[0106] ;

[0107] In the formula, is the influence degree value of brain region i as a stimulation target, is the causal relationship parameter between brain region i and brain region j, $\Delta S_i$ is the state change amount of brain region $i$, and $n$ is the number of brain regions in the brain target area. The influence degree value reflects the influence of each brain region as a stimulation target on the overall brain target area. According to the calculated influence degree value, the brain regions are sorted. The brain regions with greater influence are given higher target priorities, and the brain regions with smaller influence are given lower priorities. The determination of the target priority provides clear guidance for formulating the stimulation strategy. Within limited treatment resources and time, it is possible to preferentially stimulate the brain regions that have a greater impact on the overall brain target area, improving the pertinence and effectiveness of the treatment.

[0108] Furthermore, select the brain region with the highest target priority as the main stimulation target. Then, according to the target priorities of other brain regions, determine the order of the stimulated brain regions in sequence to form a multi-channel stimulation path. By selecting the main stimulation target, it is determined to preferentially stimulate the brain regions that have a greater impact on the overall brain target area. The design of the multi-channel stimulation path enables the stimulation to act on the brain target area in a planned and sequential manner, avoiding blind stimulation, improving the synergy and effectiveness of the stimulation, and helping to more comprehensively and deeply regulate the brain functional network and improve the abnormal brain function conditions.

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

[0110] S601: According to the multi-channel stimulation path combined with the user's real-time state, through the preset stimulation parameter calculation model, obtain the stimulation current parameters of each channel, where 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, according to the stimulation path combined with the user's real-time state, use the preset stimulation parameter calculation model to calculate the stimulation current parameters of each channel. 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 according to the input brain region information, multi-channel stimulation path, and user real-time state. Input the brain region information corresponding to each channel in the multi-channel stimulation path and the user's real-time state data into the trained stimulation parameter calculation model, and the model outputs the parameter values of the magnitude, frequency, and duration of the stimulation current for each channel. Generate personalized stimulation current parameters according to individual differences and real-time state, which can improve the accuracy and effectiveness of the stimulation, avoid over-stimulation or under-stimulation caused by unified standard stimulation current parameters, and at the same time improve the safety of the treatment and reduce the occurrence of adverse reactions.

[0113] Specifically, by combining the multi-channel stimulation path and the stimulation current parameters of each channel, a specific stimulation strategy is obtained. The multi-channel stimulation path determines the sequence and collaborative relationship of the stimulation acting on the target brain area, while the stimulation current parameters of each channel clarify the specific stimulation method and intensity at each stimulation point. Combining the two forms a complete stimulation strategy, which can systematically and specifically stimulate and intervene in the target brain area to achieve the purpose of improving abnormal brain function and make the stimulation strategy more scientific and reasonable.

[0114] Further, by combining the brain state data, the stimulation strategy is optimized to achieve closed-loop control of the stimulation strategy, including:

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

[0116] S702. Construct an action vector according to the stimulation strategy;

[0117] S703. Update the state vector according to the user's state after stimulation at a preset time period to obtain an updated state vector;

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

[0119] S705. Optimize the stimulation strategy through a preset policy optimization model according to the reward value to obtain an updated action vector;

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

[0121] In this embodiment, the user's state is monitored in real time to construct a state vector, and the stimulation strategy is dynamically adjusted according to the stimulation effect, which can accurately treat according to the individual differences and real-time changes of each user, improving the pertinence and accuracy of treatment; through continuous closed-loop control and strategy optimization, it helps to guide the user's state to develop towards the expected treatment goal, maximize the therapeutic effect of transcranial stimulation, improve the user's brain function and physical condition, and increase the treatment success rate.

[0122] In this embodiment, according to the collected user status data, the data is normalized to construct a status vector, which reflects the real-time characteristics of the user in different aspects and can comprehensively describe the status change of the user during the stimulation process; according to the previously generated stimulation strategy, including parameters such as the brain area to be stimulated, the magnitude of the stimulation current, the frequency, and the duration, different value combinations of these parameters are regarded as different actions to construct an action vector; during the stimulation process, the status of the user 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 collected again, and the previously constructed status vector is updated according to the updated data. The updated status vector reflects the status change of the user during the stimulation process. According to the difference between the status vector and the updated status vector, the reward value of the current stimulation process is calculated to evaluate the stimulation effect.

[0123] Specifically, by comparing the differences in the status vector before and after the stimulation (i.e., before and after the update), the reward value is calculated. If the user's status changes in the expected direction after the stimulation, such as the improvement of brain function, the normalization of physiological indicators, etc., then the reward value is positive; on the contrary, if the status deteriorates or there is no obvious improvement, the reward value is negative; the calculation of the reward value provides a feedback signal for optimizing the stimulation strategy, guiding how to adjust the stimulation strategy to obtain better treatment effects. The calculation formula of the reward value is as follows:

[0124] ;

[0125] In the formula, is the reward value, m is the number of elements in the status vector, is the p-th element in the original status vector, is the p-th element in the updated status 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, which is trained using a large amount of historical data to obtain a pre-trained strategy optimization model. The model learns which stimulation strategy can make the user's status develop in the expected direction according to the level of the reward value, so as to adjust the selection probability of different actions in the action space or directly generate a new and better action 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 status changes of users, improve the pertinence and effectiveness of the stimulation strategy, enable transcranial stimulation treatment to better meet the needs of each user, and improve the treatment effect.

[0126] Specifically, apply the newly determined stimulation strategy in the updated action vector to the user and start transcranial stimulation for the next time period. During this new stimulation process, monitor the user's state, update the state space, calculate the reward value, and optimize the strategy again according to the previous steps (S701 - S706). Repeat this process until the entire preset stimulation treatment process is completed. By continuously iteratively optimizing the stimulation strategy, gradually guide the user's brain and body state towards the expected treatment goal, ensure that the stimulation can always adapt to the user's changes, maximize the effect of transcranial stimulation treatment, provide a more accurate and effective treatment plan for the user, and at the same time improve the intelligent level of the treatment process.

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

[0128] In this embodiment, perform data cleaning on the user's brain state data to remove noise and outliers during the acquisition process, avoid noise and outliers interfering with data processing and analysis and affecting the accuracy of the results, and make the data more pure and reliable; perform standardization processing on the data to convert the data into a form with the same scale and distribution, avoiding differences in dimension and scale due to factors such as individual differences and acquisition devices; perform artifact removal processing on the brain state data, use the characteristics and distribution laws of artifacts to separate and remove them from the original data, and extract the true brain state signal to avoid interference from eye movement artifacts, electromyogram artifacts, electrocardiogram artifacts, etc. on the true electroencephalogram signal.

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

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

[0131] 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;

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

[0133] In this embodiment, the electrode part in the stimulation circuit that contacts the human brain is stimulated to collect the brain state data of the user to be stimulated in real time. The collected data is sent to the processing device through the network unit. The processing device analyzes the brain state data of the user and constructs a brain network map, analyzes the abnormal brain area part in combination with the actual disease situation of the user, formulates a corresponding transcranial stimulation strategy, and obtains the generation parameters of the stimulation signal, including the magnitude, frequency, stimulation time, etc. of the stimulation current. The stimulation circuit generates a corresponding stimulation current according to the generation parameters of the stimulation signal and performs transcranial stimulation on the user through the electrode in contact with the user.

[0134] Embodiment 2

[0135] This embodiment provides a human brain transcranial stimulation circuit, and the human brain transcranial stimulation circuit includes:

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

[0137] A sending module that sends the brain state data to the processing device so that the processing device generates the generation parameters of the stimulation signal according to the brain state data;

[0138] A stimulation signal generation module that receives the generation parameters of the stimulation signal and generates a stimulation signal according to the generation parameters;

[0139] Among them, the processing device generates the generation parameters of the stimulation signal according to the brain state data, including: analyzing the connection relationship between brain regions according to the brain state data and constructing a brain network map; based on the relationship between the node degree value and the betweenness centrality value of each node in the brain network map and their corresponding preset values, combining a preset abnormal detection model to analyze the abnormal regions in the brain network map to obtain the brain target regions; generating a stimulation strategy according to the brain target regions; combining the brain state data to optimize the stimulation strategy to achieve closed-loop control of the stimulation strategy.

[0140] Embodiment 3

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

[0142] A power supply module that is responsible for providing stable power supply;

[0143] A human brain contact module that is responsible for generating a dynamic stimulation signal according to the real-time state of the user and acting on the brain target region of the user to achieve dynamic stimulation of the user's brain;

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

[0145] In this embodiment, a continuous and stable power supply is provided according to the power supply module, solving the problems of too fast battery power consumption and insufficient battery life in the existing stimulation circuit; the human brain contact module generates dynamic stimulation signals based on the user's real-time state, which is conducive to realizing personalized treatment, can accurately adjust the stimulation according to the real-time state of each user's brain, improves the treatment effect, and solves the problem that in the existing technology, fixed stimulation current parameters are mostly used and it is impossible to flexibly adapt to individual differences and changes in the user's state; through the isolation module, all-round electrical isolation between the power supply module and the human brain contact module is achieved, ensuring the safety of the user during the stimulation process.

[0146] Specifically, the power supply module can obtain electrical energy by connecting to an external power supply or a charging device through an external power interface, and can continuously provide a stable power supply. It can not only rely on the external power supply to ensure long-term stable operation, but also use battery power supply in mobile scenarios, enhancing the portability and flexibility of the device; the human brain contact module real-time monitors information such as the user's state, stimulation output, and human body impedance, and accurately generates dynamic stimulation signals according to the user's real-time state, realizing personalized stimulation treatment, which helps to timely detect abnormal situations and ensure the safety and effectiveness of the stimulation.

[0147] Specifically, the isolation module electrically isolates the power supply module and the human brain contact module, which can improve the stability and safety of the circuit, prevent the influence of power supply fluctuations, electromagnetic interference, etc. on 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 the quality of the signal and the accuracy of acquisition, and ensure the reliable operation of the entire transcranial stimulation circuit.

[0148] The circuit design of this application can use a large-capacity and high-voltage battery for power supply, providing a long battery life, and ensuring stable operation. It realizes 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 state, dynamically generate stimulation signals, accurately locate the brain target area and optimize the stimulation strategy, which can more effectively stimulate the brain target area, improve brain function, and improve the effect of treating brain-related diseases. According to the user's real-time state, the stimulation strategy is optimized in real time, and the stimulation current parameters are adjusted in a timely manner, making the treatment process more scientific and reasonable, and improving the treatment effect and efficiency.

[0149] Further, the power supply module includes:

[0150] An external power interface for connecting to an external power supply or a charging device;

[0151] A wireless network unit responsible for remote communication and transmitting control instructions and user physiological data;

[0152] Battery unit and battery charging unit, which provide a portable power supply and support isolated control of the charging state.

[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 is connected to the battery charging unit to enable charging. The external power interface can introduce external electrical energy into the circuit system to achieve power input, providing the required power for the entire transcranial stimulation circuit. Through the external power interface, it is convenient for the circuit to obtain power from the outside. It can be connected to the mains power and converted to a suitable voltage for device power supply through a power adapter, or it can be connected to a mobile power supply such as a power bank to meet the power supply requirements of the device in different scenarios, improving the flexibility and adaptability of the device's use.

[0154] Specifically, the wireless network unit is a module for remote communication, connected to the isolation digital unit for mutual communication, receiving digital signals from the isolation digital unit (the signal source comes from the programmable processing unit), to achieve remote communication between the device and the outside. Doctors or researchers can send control instructions through terminal devices such as mobile phones and computers far away from the device to adjust stimulation current parameters, etc., 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 for analysis and storage in real time, facilitating doctors to timely understand the patient's treatment situation and improving the convenience and intelligent level of treatment.

[0155] Specifically, the battery charging unit is a battery charging circuit, connected to the battery unit and the isolation digital unit to charge the battery unit. The isolation digital unit can control whether the battery is charging; the battery unit is a rechargeable lithium battery, providing power for the entire circuit, convenient for carrying, connected to the isolation analog unit and the isolation power unit. The isolation analog unit collects the battery voltage, and the isolation power unit isolates the battery voltage and outputs it to the circuit in the area of human brain contact; the battery unit and the battery charging unit provide a portable power supply, enabling the transcranial brain stimulation circuit to work independently without an external power supply, facilitating patients to use in mobile or outdoor scenarios, etc., improving the portability and applicability of the device. The battery charging unit supports isolated control of the charging state, which can ensure the stability and safety of the circuit system during the charging process, prevent interference during charging from affecting other parts of the circuit, and at the same time protect the service life of the battery and extend the overall service time of the device.

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

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

[0158] A DAC control unit that converts digital signals into analog stimulation waveforms;

[0159] The voltage, current and temperature acquisition unit monitors the user's status, stimulation output and body impedance in real time;

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

[0161] The stimulation generation circuit unit generates a dynamic stimulation signal based on the operational amplifier and the 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, an external human brain electrode interface unit ⑪, and a stimulation generation circuit unit ⑫. The programmable processing unit is a programmed MCU or FPGA chip, which is connected to the isolation digital unit, the isolation analog unit, the voltage, current and temperature acquisition unit, the DAC (digital-to-analog) control unit, and the stimulation generation circuit unit, and uses software acquisition to control the stimulation signal that needs to be output for both analog signals and digital signals simultaneously; the programmable processing unit can analyze and process the user's real-time status data, judge the user's current brain status, and dynamically adjust the stimulation current parameters (such as the magnitude, frequency, duration, etc. of the stimulation current) of the stimulation signal according to the judgment result, so as to achieve personalized and precise brain stimulation; by responding to the changes in the user's brain status in real time and adjusting the stimulation current parameters in a timely manner, the stimulation can be made more precise and effective.

[0163] Specifically, the DAC (digital-to-analog) control unit is a circuit composed of a DAC chip and an instrumentation amplifier, which is connected to the programmable processing unit and the stimulation generation circuit unit. The circuit output voltage of the DAC control unit is set through a program to the stimulation generation circuit unit to achieve real-time control of the output waveform, thereby converting the digital signal output by the programmable processing unit into an analog stimulation waveform to ensure the accuracy and stability of the stimulation signal. The voltage, current and temperature acquisition unit is a circuit such as resistor voltage division and capacitor filtering. The current, voltage and temperature data of the external human body electrode interface are converted into an analog quantity with an appropriate ratio by this unit and then detected by the programmable processing unit. The user's status, stimulation output and body impedance are monitored in real time through sensors and measurement circuits; by monitoring the voltage, current and temperature in real time, abnormal situations such as excessive current and overheating can be detected in a timely manner to avoid harm to the user. By monitoring the body impedance, the contact situation between the human body and the electrode and the physiological state of human tissues can be understood, providing a basis for adjusting the stimulation current parameters and improving the accuracy of the stimulation.

[0164] Specifically, the external electrode interface unit of the human brain is an external electrode connector interface, which is connected to the voltage, current, and temperature acquisition unit and the stimulation generation circuit unit. Through these two circuits, the stimulation of the human brain and real-time data sampling are realized. It is responsible for connecting the multi-channel electrode array and configuring multi-sensors to collect the user's brain state information in real time, realizing multi-channel data acquisition, and comprehensively obtaining the physiological information of the user's brain. The stimulation generation circuit unit is a circuit composed of operational amplifiers, instrumentation amplifiers, and resistors and capacitors. It is connected to the programmable processing unit, the DAC control unit, and the external electrode interface unit of the human body. It is the core waveform generation circuit for various waveforms required by the human body. According to the stimulation current parameters provided by the programmable processing unit, it generates dynamic stimulation signals based on operational amplifiers (operational amplifiers) and instrumentation amplifiers; the stimulation generation circuit unit can generate precise stimulation signals according to different stimulation current parameters, realizing precise stimulation of the target area of the user's brain. The operational amplifier and the instrumentation amplifier can ensure the quality of the stimulation signal.

[0165] Further, the isolation module includes:

[0166] The isolation digital unit is responsible for the electrical isolation of digital signals;

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

[0168] The isolation 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 isolation digital unit ⑤, an isolation analog unit ⑥, and an isolation power supply unit ⑦. The isolation digital unit includes an isolation digital chip, which is connected to the programmable processing unit and receives and sends communication signals from the wireless network unit (the signal source comes from the wireless network unit), facilitating the remote operation of the system; during the signal transmission process, it electrically isolates between the two circuit domains, avoiding electrical interference caused by direct connection, effectively isolating the electrical interference between different circuit modules, ensuring the stable transmission of digital signals, reducing signal distortion and bit error rate, and improving the reliability of the system; avoiding circuit damage and safety hazards caused by problems such as potential difference or short circuit, and protecting the safety of the equipment and users.

[0170] Specifically, the isolation analog unit is an isolation analog chip. In the power supply part area, it is connected to the battery unit to detect the battery voltage and obtain the battery capacity information. In the area in contact with the human brain, it is connected to the programmable processing unit to obtain the battery voltage data. Through devices such as isolation amplifiers, the analog signal is isolated and transmitted from one circuit domain to another circuit domain, achieving electrical isolation during the transmission process, effectively isolating the interference of the power supply to the analog circuit, reducing the distortion and noise interference of the analog signal, and improving the accuracy and stability of the analog signal. The isolation power supply unit is an isolation DC / DC power module, which is connected to the battery unit to supply power to the entire circuit in the area in contact with the human brain, completely electrically isolating 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 failures or interference, protecting the safety of users and equipment; reducing the mutual interference between modules, ensuring the independent and stable operation of each module, and improving the performance and reliability of the entire transcranial stimulation circuit system.

[0171] Further, the isolation power supply unit includes at least two isolation DC / DC power sub-units, which respectively provide ±15V stimulation operational amplifier power supply and 5V logic power supply, and integrate an overvoltage or overcurrent protection circuit, which cuts off the output through the OD pin in case of abnormality.

[0172] In this embodiment, the isolation 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 components such as a switching circuit, an inductor, and a capacitor to provide a suitable working power supply for the stimulation operational amplifier; the 5V logic power supply converts the input DC voltage into a 5V DC voltage; through different isolation power sub-units, specific voltages that meet the requirements of different circuit components are provided, ensuring that both the stimulation operational amplifier and the logic circuit can work at their respective appropriate voltages, improving the stability and reliability of the entire circuit system; the isolated DC / DC conversion can effectively isolate the electrical interference between the power supply side and the load side, reduce the influence of power supply fluctuations, noise, etc. on the load circuit, and at the same time prevent the failure of the load circuit from being fed back to the power supply side, improving the anti-interference ability and safety of the system.

[0173] Specifically, an overvoltage or overcurrent protection circuit is integrated. When the output voltage exceeds the set overvoltage threshold, circuits such as a comparator will detect this abnormal situation and trigger a protection mechanism to make the protection circuit act and cut off the output to prevent subsequent circuit components from being damaged by excessive voltage; when the detected current exceeds the set overcurrent threshold, the protection circuit will respond quickly and cut off the circuit by controlling switching diodes, etc., to avoid excessive current passing through the circuit components and prevent the components from being damaged due to overheating, etc.; through the overvoltage or overcurrent protection circuit, the safety of the circuit is greatly improved, and it can effectively protect each component in the circuit from being damaged by excessive voltage or current, extend the service life of the circuit components, and reduce the risk of circuit failures.

[0174] Specifically, the OD pin, i.e., the 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 will send a specific level signal to the OD pin. By controlling the level state of the OD pin, the switching device connected thereto is controlled, thereby realizing the function of cutting off the output. The on / off control of the entire output circuit can be achieved through the control of a single pin, which can quickly respond to abnormal situations and cut off the output in a timely manner, playing a role in protecting the circuit and equipment.

[0175] Exemplarily, as Figure 3 shown, the power supply part area and the human brain contact part area are isolated through the isolated power supply unit. Even if a large fault occurs during the charging of the external power supply interface with an external power supply (such as a voltage burning out the circuit in the power supply part area), it will not cause a large voltage to enter the circuit in the human brain contact part area and will not cause harm to the human body. Transcranial stimulation requires a portable rechargeable battery, and the isolated power supply unit has the ability to supply power with a wide range of large voltages. Therefore, a large-capacity and large-voltage battery can be used to supply power, providing a longer battery life and ensuring stable operation. The wireless network unit realizes communication by isolating the digital unit and the programmable processing unit, and the battery unit can detect the battery voltage for the programmable processing unit through the isolated analog unit and can give a charging prompt.

[0176] Specifically, the voltage, current, and temperature sampling unit is connected to the external electrode interface of the human brain, and the voltage and current waveforms of the human brain stimulation are monitored in real time to realize the 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, and after amplification, it is output to the + pole of the stimulation operational amplifier; the stimulation generation circuit is connected to the human brain electrode interface through a sampling resistor, and the voltage or current signal of the human brain is connected to the - pole of the stimulation operational amplifier after passing through the instrumentation amplifier chip. The stimulation operational amplifier adjusts in real time according to the voltage comparison between the + and - poles, and the stimulation waveform output is adjusted in a closed loop.

[0177] Specifically, the stimulation generation circuit has a function of protecting with an OD switch. If an abnormality is detected in the human body stimulation voltage or current, the OD pin is pulled low, and the stimulation operational amplifier shuts down the output; there are 2 paths for the isolated power supply circuit. One path provides ±15V power supply for the stimulation operational amplifier, and the other path 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 providing ±15V power supply can be pulled low, and the ±15V power supply is shut down to protect the safety of the human brain. The Ctrl pin is also pulled low when the stimulation is not started, and the ±15V power supply is shut down to save battery power.

[0178] Specifically, there are two chips in the isolation digital unit and one chip in the isolation analog unit. For the programmable processing unit to communicate digitally with the wireless network unit, as well as for power switch control and battery voltage detection, isolation input and output need to be carried out through isolation chips.

[0179] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for transcranial stimulation of the human brain, implemented by a processing device and executing a specific software method, characterized in that Including: Obtaining the brain state data of the user to be stimulated; According to the brain state data, analyzing the connection relationship between brain regions, and constructing a brain network map, wherein each node in the brain network map includes a node degree value and a betweenness centrality value; Based on the relationship between the node degree value and the betweenness centrality value of each node in the brain network map and their corresponding preset values, and combining a preset anomaly detection model, analyzing the abnormal regions in the brain network map to obtain the brain target regions; Generating a stimulation strategy according to the brain target regions; Combining the brain state data to optimize the stimulation strategy to achieve closed-loop control of the stimulation strategy.

2. The transcranial brain stimulation method according to claim 1, characterized in that According to the brain state data, analyzing the connection relationship between brain regions, and constructing a brain network map, including: Calculating the phase value of each channel signal according to the resting-state EEG data in the brain state data, and combining the phase synchronization degree between every two channel signals to construct a phase synchronization matrix; Calculating the functional connection strength between different brain regions according to the brain state data to obtain a functional connection matrix; Dynamically fusing the phase synchronization matrix and the functional connection matrix to obtain a connection relationship matrix; Based on the connection relationship matrix, constructing a brain network map, wherein each brain region in the brain network map is used as each node, and each node is connected by the corresponding value in the connection relationship matrix as the weight of the edge.

3. The method for transcranial stimulation of the human brain according to claim 1, wherein The analyzing the abnormal regions in the brain network map based on the relationship between the node degree value and the betweenness centrality value of each node in the brain network map and their corresponding preset values, and combining a preset anomaly detection model to obtain the brain target regions includes: Calculating the node degree value and the betweenness centrality value of each node in the brain network map; Regarding the brain regions corresponding to the nodes with the node degree value greater than the preset node degree value and the betweenness centrality value greater than the preset betweenness centrality value as the core brain regions; Screening the abnormal regions in the core brain regions through a preset anomaly detection model to obtain abnormal core brain regions; Screening out the brain regions with the connection relationship value greater than the preset first anomaly threshold or the connection relationship value less than the preset second anomaly threshold among the brain regions connected to the abnormal core brain regions as the abnormal related brain regions, wherein the first anomaly threshold is greater than the second anomaly threshold, and the connection relationship value is the corresponding value in the connection relationship matrix; Combining the abnormal core brain regions and the abnormal related brain regions as the brain target regions.

4. A transcranial brain stimulation method according to claim 1, characterized in that Generating a stimulation strategy according to the brain target regions, including: Analyzing the brain region stimulation order according to the connection relationship between each brain region in the brain target regions to obtain a multi-channel stimulation path; According to the multi-channel stimulation path, calculating the stimulation current parameters of each channel through a preset stimulation parameter calculation model to generate a stimulation strategy.

5. A transcranial brain stimulation method according to claim 4, characterized in that, The analyzing the brain region stimulation order according to the connection relationship between each brain region in the brain target regions to obtain a multi-channel stimulation path includes: Constructing a brain region causal model according to the connection relationship between each brain region in the brain target regions; According to the brain region causal model, calculating the influence degree value of each brain region in the brain target regions as a stimulation target on the overall brain target regions; Determine the target priority of each brain region in the brain target area according to the influence degree value; Take the brain region with the highest target priority as the main stimulation target, and determine the order of brain region stimulation according to the target priority to obtain a multi-channel stimulation path.

6. A transcranial brain stimulation method according to claim 4, characterized in that, According to the multi-channel stimulation path, calculate the stimulation current parameters of each channel through a preset stimulation parameter calculation model, and generate a stimulation strategy, including: According to the multi-channel stimulation path and the real-time state of the user, obtain the stimulation current parameters of each channel through a preset stimulation parameter calculation model, where the stimulation current parameters include the magnitude, frequency, and duration of the stimulation current; Combine the multi-channel stimulation path and the stimulation current parameters of each channel to obtain a stimulation strategy.

7. A transcranial brain stimulation method according to claim 1, characterized in that Combine the brain state data to optimize the stimulation strategy and achieve closed-loop control of the stimulation strategy, including: Construct a state vector according to the real-time state of the user; Construct an action vector according to the stimulation strategy; Update the state vector according to the user state after stimulation at a preset time period to obtain an updated state vector; Calculate the reward value of the current stimulation process according to the difference between the state vector and the updated state vector; Optimize the stimulation strategy through a preset policy optimization model according to the reward value to obtain an updated action vector; Based on the updated action vector, perform stimulation on the user for the next time period until the overall stimulation process is completed.

8. A transcranial brain stimulation method according to claim 1, characterized in that, The acquisition of the brain state data of the user to be stimulated further includes data cleaning, normalization processing, and artifact removal processing on the acquired brain state data of the user to be stimulated.

9. A method for transcranial stimulation of the human brain, implemented by a stimulation circuit, characterized in that, Including: Collect the brain state data of the user to be stimulated; 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; Receive the generation parameters of the stimulation signal and generate a stimulation signal according to the generation parameters.

10. A transcranial stimulation circuit for the human brain, characterized in that, Including: A brain data acquisition module that collects the brain state data of the user to be stimulated; A sending module that sends 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 generation module that receives the generation parameters of the stimulation signal and generates a stimulation signal according to the generation parameters; Among them, the processing device generates the generation parameters of the stimulation signal according to the brain state data, including: analyzing the connection relationship between brain regions according to the brain state data, constructing a brain network map; based on the relationship between the node degree value and the media centrality value of each node in the brain network map and their corresponding preset values, combined with a preset anomaly detection model, analyzing the abnormal regions in the brain network map to obtain the brain target area; generating a stimulation strategy according to the brain target area; combining the brain state data to optimize the stimulation strategy and achieve closed-loop control of the stimulation strategy.

Images