22 results about "BrainMaps" patented technology

The invention discloses a multi-mode brain dysfunction auxiliary diagnosis method based on a dynamic function connection network. A two-stage collaborative learning framework from an individual brain graph to a group relation graph is constructed. Firstly, an individual multi-modal fusion brain map is constructed, node features of the individual multi-modal fusion brain map are obtained through node regularization regression analysis of an rs-fMRI time sequence, an adjacent matrix is obtained through calculation of the brain interval grey matter volume difference of a T1 image, and individual enhancement characterization is obtained through map convolutional network fusion. And then constructing a group relationship enhancement graph, taking individual representation as node features, constructing a dual-channel adjacency relationship for distinguishing homologous / heterologous connection according to age and gender, obtaining final discriminative representation through dual-channel graph attention network aggregation, and performing classification diagnosis according to the final discriminative representation. According to the method, deep fusion of multi-modal information and explicit modeling of key biological variables are realized, and an effective tool is provided for accurate and explainable auxiliary diagnosis of brain diseases.

The application discloses a brain map drawing electronic pen and a handwriting knowledge generation and database construction device and method, and relates to the technical field of knowledge generation and database construction, and specifically relates to a brain map drawing electronic pen and a handwriting knowledge generation and database construction device and method.

The invention discloses a visual modeling-script programming-brain graph type test case design system and method oriented to an embedded system, and belongs to the technical field of embedded systems. The method comprises the steps of receiving a user instruction, selecting and entering a design mode, and performing test case design in the selected design mode: if the design mode is a visual modeling mode, completing case design by dragging components, configuring parameters and establishing logic connection lines; if the mode is a script programming mode, script codes are written by calling a special function library, and case design is debugged and completed; if the mode is the brain map mode, using case design is completed by creating hierarchical nodes and carding test scenes and steps; and the data management module is used for storing the designed test case, and supporting exporting as a standard document or switching to other modes for secondary editing and data intercommunication. According to the method, three design modes are fused, the test case design efficiency and logic definition are remarkably improved, and team cooperation convenience is greatly enhanced.

The invention relates to a multi-span collaborative study and judgment method based on a thinking brain map. The method comprises the following steps: constructing the thinking brain map by taking a case event as a central point and taking a case element as a node; in response to a collaborative editing operation of any user on the thinking brain map in multi-span collaboration, identifying an operation type of the collaborative editing operation; if the operation type is a modified node type, identifying a first target node corresponding to the collaborative editing operation in the thinking brain map, and obtaining editing content of the collaborative editing operation; standardizing the edited content through a preset data format to obtain standardized edited content, and updating a first target node in the thinking brain map according to the standardized edited content; and encrypting the updated thinking brain map, and pushing the encrypted thinking brain map to each user of the intranet from the extranet by adopting a secure data transmission technology, so that each user carries out case research and judgment through the updated thinking brain map. According to the method, cross-network and cross-department multi-person collaborative research and judgment can be realized, and the research and judgment efficiency and the data security are improved.

The invention relates to the technical field of medical image diagnosis, and particularly discloses a brain network analysis method based on time-space two dimensions and multiple scales, which comprises the following steps: obtaining a functional magnetic resonance image and extracting a time sequence of each brain region; on the basis of Pearson's correlation, functional homogeneity weighting is combined, and a functional brain map is constructed on fine, medium and coarse scales; multi-scale spatial features are extracted through a mixed structure of a U-Net encoder and a multi-scale graph isomorphic network; for a brain region BOLD signal, an internal and external dual-branch selective state space model architecture is adopted to respectively capture short-term instantaneous fluctuation and long-term trend dependence, and a time sequence fragment related to a pathological time sequence attention mechanism enhanced disease is extracted through a multi-scale context to obtain time features; and performing residual gating fusion on the spatio-temporal characteristics and then inputting the spatio-temporal characteristics into a classifier to obtain a classification result. According to the method, the limitation of single scale and single time sequence modeling is broken through, the refinement and functional fusion of the brain spatial-temporal characteristics are realized, and the accuracy and interpretability of brain disease auxiliary diagnosis are improved.

The application discloses a kind of visual modeling-embedded system-oriented script programming-brain map test case design system and design method, belong to embedded system technical field.The method includes receiving user instruction, selects and enters a kind of design mode, in the selected design mode, test case design is carried out: if visual modeling mode, through drag component, configuration parameter and establish logic connection line complete case design;If script programming mode, through calling special function library script code is written and debugged to complete case design;If brain map mode, through creating hierarchical node, combing test scene and step complete case design;Through data management module, the test case of design completion is saved, and export to standard document or switch to other mode is supported Secondary editing and data intercommunication.The application fuses three kinds of design modes, significantly improves test case design efficiency and logic clarity, greatly enhances team collaboration convenience.

The invention of intelligent system for detection and classification of individual capabilities through brain mapping and advanced EEG signal analysis using deep learning relates to a method capable of identifying and mapping an individual's cognitive strengths and weaknesses based on the impact of each brain region on the individual's performance In this invention, a combination of data and information from cognitive assessment databases, along with rules extracted from previous research and studies, and results obtained from individuals' brain signals in electroencephalography are aggregated to create an enhanced collective trained model for generating the individual's brain map and identifying the individual's cognitive strengths and weaknesses based on the obtained results. Rapid and accurate data processing, coupled with the use of modern deep learning and statistical techniques, has transformed this system into a powerful tool for better understanding brain function and its clinical and research applications.

This invention relates to the field of medical image processing technology, specifically a method for constructing a brain functional network based on a large language model. First, the functional magnetic resonance imaging (fMRI) data of the subject is preprocessed, brain regions are divided based on a pre-defined atlas, and average time series data are extracted. Then, a large language model is used, referencing a neuroscience knowledge base, to generate prior knowledge text descriptions for each brain region. Simultaneously, instance-level text descriptions are generated based on the activation state and correlation of the time series data. The prior knowledge text and instance-level text are fused and converted into node feature vectors using a professional text encoder. Furthermore, this method constructs a brain map containing node features and edge connections, which is then input into a graph neural network for feature learning and classification training. By deeply integrating prior textual knowledge from the medical field with image data, a brain functional network rich in semantic information is constructed, significantly improving the accuracy of brain disease identification and the model's generalization ability.

The invention provides an information visualization processing method and system based on a brain graph of a net structure, and relates to the technical field of information interaction processing, and the method is characterized by comprising the following steps: 1, rendering a plurality of information nodes in a canvas area, and storing the data structure of the information nodes based on a graph structure, any node is allowed to have a plurality of father nodes and a plurality of child nodes; 2, detecting a cursor position or a node selection state of a user in real time, rendering a suspension operation component at a corresponding area position after entering an activated state, and performing option guide; step 3, according to the operation component in the step 2, guiding and selecting to create a new node or establish a connection relationship; and step 4, according to the connection relationship and the physical attribute between the nodes in the step 3, calculating the coordinate position of each node in real time by using a force steering algorithm, and updating canvas display through a rendering engine. The method has the advantages that a complex knowledge network is constructed more directly and clearly and displayed, the overall thinking efficiency is improved, and use and operation are convenient.

1. The name of the design product: problem solving aid card (brainstorming card). 2. The use of the design product: the design product is used to assist students in solving problems. 3. The design points of the design product: the combination of shape and pattern. 4. The picture or photo that best indicates the design points: front view. 5. The design product is a flat product, and the left view, right view, top view and bottom view are omitted.

The invention belongs to the technical field of video quality evaluation, and particularly relates to a neural typing method based on a multi-modal brain map and semi-supervised deep clustering. Comprising the following steps: constructing a multi-modal brain network according to multi-modal neural image data; the brain network of each mode comprises a normal brain network and an abnormal brain network; performing pre-training on the MBVAE model by adopting a multi-modal brain network to obtain a pre-trained MBVAE model; performing fine tuning training on the pre-trained MBVAE model in combination with a clustering module to obtain a trained deep embedded clustering model; obtaining multi-modal neural image data of a user, constructing a multi-modal brain network, and inputting the multi-modal brain network into the trained deep embedded clustering model for processing to obtain a neural typing result; according to the method, the accuracy and reliability of ASD neural typing results are improved, the problems of gender imbalance and multi-site heterogeneity existing in ASD data are effectively solved, and the robustness and generalization ability of the model are enhanced.

The application relates to the technical field of image recognition, and discloses an unmanned parking lot AI auxiliary guarding method based on a cloud brain map large model, which comprises the following steps: taking images when vehicles enter and exit the parking lot and performing fuzzy processing, and automatically identifying and matching the vehicles by using the similarity between the images. The application can identify the vehicles entering and exiting the parking lot and make release or prohibition decisions under different illumination, angles and environmental changes. The application can realize automatic management under high privacy protection, improve the efficiency, accuracy and safety of parking lot management, and simultaneously reduce the misjudgment rate and operation cost of a traditional identity recognition system.

The application relates to a language area positioning method and device based on nuclear magnetic resonance imaging and a lateralization coefficient. The application sets a structural nuclear magnetic image and a brain function nuclear magnetic image of a detected person, registers the nuclear magnetic image and the brain function nuclear magnetic image, and outputs a visual three-dimensional brain map, combines a seed point Pearson correlation coefficient, extracts voxel counts of left and right brains in a threshold range, and combines a lateralization coefficient to distinguish whether the detected person is left brain dominant, right brain dominant or bilateral brain dominant.

This invention belongs to the field of deep learning clustering, specifically relating to an ASD neural typing method based on synthetic brain map data augmentation. The method includes: acquiring brain map data for training and inputting it into a pre-trained dual-decoder graph autoencoder to obtain latent embeddings; inputting the latent embeddings into a pre-trained latent space conditional diffusion model to obtain an augmented brain map; training a deep embedding clustering model based on MBVAE or a deep graph clustering model based on HGDAE using the augmented brain map to obtain a trained deep embedding clustering model based on MBVAE or a deep graph clustering model based on HGDAE; acquiring brain map data to be detected and inputting it into the trained deep embedding clustering model based on MBVAE or a deep graph clustering model based on HGDAE to obtain the ASD neural typing result. This invention effectively solves the gender imbalance and multi-site heterogeneity problems existing in ASD data, enhancing the robustness and generalization ability of the model.

The invention relates to the technical field of signal intelligent processing, in particular to a subject neural feedback effectiveness prediction method and system based on a graph neural network, and the method comprises the steps: carrying out the resting state functional magnetic resonance imaging scanning of a subject through a magnetic resonance scanner, and extracting a resting state brain signal time sequence of the subject; obtaining a Pearson's correlation coefficient between interested brain regions of the subject and a corresponding brain region position index based on the resting state brain signal time sequence, and extracting time sequence statistics by performing time domain transformation on the brain signal time sequence; taking the time sequence statistic as a node feature, obtaining an edge feature according to a Pearson's correlation coefficient and a brain region position index, and constructing a brain map based on the resting state fMRI of the subject; and identifying the neural feedback effectiveness of the subject by using the neural feedback effectiveness prediction model. According to the method, the brain image feature extraction process can be simplified, the degree of dependence of previous machine learning prediction model construction on features is reduced, and the universality of a neural feedback effectiveness prediction model is improved.

1. Name of the product in this design: Graphical User Interface for Mind Map Marker Registration in Electronic Devices. 2. Purpose of this design: An electronic device. 3. The key design feature of this product is its graphical user interface. 4. The image or photo that best illustrates the design points: Interface change state diagram 2. 5. Purpose of the graphical user interface: This design is used as a human-computer interaction interface to display the registration process of markers corresponding to target points on a mind map model. The main view displays the information interface of the brain map model marked with target points. The left side shows the brain map model marked with target points, the center shows the target point information, and the right side shows the stimulation parameters based on the target point. Clicking the "Confirm" button in the lower right corner of the main view leads to interface state diagram 1. Interface state diagram 1 mainly displays the markers added to the brain map model corresponding to the target points. The left side shows the brain map model with added markers, and the right side shows the marker cards. Clicking the "Next" button in the lower right corner of interface state diagram 1 leads to interface state diagram 2. Interface state diagram 2 mainly displays the interface for registering markers. The left side shows the brain map model with markers and highlights the markers currently to be registered, while the center shows the camera... The system displays the camera's recognition range and the real-time position of the tracked object within that range. Marker cards are displayed on the right, with currently registered marker cards highlighted. Clicking the "Register" button in the lower right corner of interface state diagram 2 registers the current marker. After registration, interface state diagram 3 appears, with the "Next" button in the lower right corner lit up. Clicking the "Next" button again leads to interface state diagram 4. Interface state diagram 4 primarily displays the registration verification results of the markers after registration. Its left side displays a mind map model with markers, the middle shows the camera's recognition range and the real-time position of the tracked object within that range, and the right side displays the registration verification results.

The invention discloses a template-based near-infrared channel brain region automatic mapping and display method, system and device and a storage medium, and the method comprises the steps: taking each detector as a collection channel, correspondingly collecting near-infrared signals, and carrying out the interference removal of each near-infrared signal, so as to calculate the blood oxygen concentration change value of each collection channel; the method comprises the following steps: establishing a corresponding relation between signal intensity of near-infrared signals and activation degrees of brain regions, calculating the activation degree of each brain region, drawing a boundary of a functional region on a preset three-dimensional brain map, marking activation hot spots in the functional region to form an activation map, and marking significant points of blood oxygen concentration change on a preset blood oxygen response curve. The blood oxygen distribution curve graph is formed, automatic positioning of the position of the collection channel is achieved, the manual comparison workload during data analysis is greatly reduced, the analysis efficiency is improved, subjective errors are eliminated through an automatic mapping method based on the template, and the corresponding judgment accuracy of the channel and the anatomical or functional brain area is improved.

The application can detect the whole brain blood flow change of the patient with spinocerebellar degeneration and optimize the cerebellum visualization. The application carries out CBF reconstruction on ASL data, carries out nonlinear co-registration on the whole brain CBF image to the PET template, and spatially standardizes to the MNI space, divides the CBF value of each voxel by the whole brain average value to obtain the normalized CBF and smooth; construct a general linear model in SPM8, and carry out whole brain statistical analysis at the voxel level; extract the t value image of the significant cluster and its mask, and use the mask to extract the cluster average CBF value for regional analysis. The significant cluster t value image of the cerebellum is overlaid to the coronal SUIT template, the t value image is mapped to the SUIT plane graph in the SUIT toolbox, and the outline of the t value plane graph is overlaid to the cerebellar anatomical and functional orientation plane graph respectively, so that the anatomical and functional detail visualization of the cerebellar region is realized.

This invention discloses a severe depression assessment model based on cloud-edge collaboration and knowledge-guided cross-comparative learning, belonging to the field of intelligent medical technology. The model employs a cloud-edge collaborative architecture, acquiring fMRI data and demographic information at the edge, combining this with AAL and Harvard brain atlases to extract ROI time series, modeling using Pearson correlation coefficients, conducting inter-group t-tests for significance analysis, and constructing a functional connectivity graph through feature fusion before uploading it to the cloud. On the cloud, based on multi-view brain map hierarchical analysis, it integrates graph attention mechanisms, default mode network medical prior knowledge, and cross-comparative learning to complete the training and optimization of graph attention, subgraph generation, and the prediction network. Then, feature extraction, knowledge-guided pruning, and cross-view fusion are performed on the functional connectivity graph to be tested, and the depression assessment result is obtained through inference by the prediction network. This invention shortens transmission time, reduces bandwidth pressure, avoids privacy leakage risks, and improves assessment accuracy.

The invention provides a brain map-based vehicle network multi-communication protocol management method and system and electronic equipment, and the method comprises the steps: receiving a new protocol request, and obtaining the input basic information of a new protocol and the selected template information; analyzing the template, and displaying the template in the form of a brain map logic structure diagram; the brain graph logic structure diagram comprises a main node for representing vehicle real-time information reporting and a main node for representing reissuing information reporting; receiving an editing request on the brain map logic structure chart according to the expansion requirement; the editing request comprises a child node editing request under a main node or a data body editing request under a child node; and receiving a new protocol storage request, and storing the new protocol. According to the method, the existing basic protocol template can be reused to carry out brain map management on the newly added protocol, so that repeated work is reduced, the protocol development efficiency is improved, and then extension and customization are carried out on the basis of the basic protocol according to actual requirements.

Systems and methods for electroencephalography (EEG) mapping based on naturalistic behaviors are provided. The EEG mapping does not rely on trial-averaging and thus does not require repetitive tasks. Instead, it enables mapping of various types of naturalistic behaviors, such as conversation, which are more familiar for a patient, more ecologically valid, and useful for pediatric patients or patients with cognitive delays.

The invention discloses a data analysis method and system based on dynamic drilling and a medium, and is suitable for multi-dimensional analysis of insurance business data. The method comprises the following steps: a data preparation step: receiving and preprocessing structured data containing insurance business fields; a drilling path configuration and dynamic drilling analysis step: allowing a user to define a chained drilling path comprising a plurality of nodes through an interactive interface, each node being configured with a filtering condition, a statistical dimension, a statistical index and a result filtering rule for the insurance business field; according to the chained drilling path, dynamically generating an SQL query statement in an incremental calculation mode, and carrying out hierarchical statistics and filtering on source data; the result visualization step is used for displaying the drilling path and the analysis result in the form of a brain map linkage chart. According to the method, the problems that an existing data analysis technology is single in display mode, rigid in drilling and low in efficiency are solved, and flexible, efficient and deep exploration of the insurance business data is achieved.