40 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 invention discloses a personalized communication training content generation method based on big data. The method comprises the following steps: S1, constructing a multi-modal feature sequence; s2, constructing a user expression brain map; s3, performing cognitive causal alignment on the time axis of the user expression brain map; s4, identifying ambiguous expressions in user input, expanding a plurality of semantic interpretation paths, forming a semantic trajectory tree, and generating a primary semantic path and a secondary semantic path; s5, generating conventional communication content based on the primary semantic path, constructing an evolution expression path based on the secondary semantic path, generating challenging expression content, and integrating and outputting a double-track fusion training corpus; s6, identifying a negative expression fragment in the double-track fusion training corpus, and generating an evolution training sequence based on an annealing control function; and S7, performing content reconstruction on the double-track fusion training corpus based on the evolution training sequence, and generating personalized communication training content. According to the method, multi-modal modeling and a semantic evolution control method are fused, and personalized communication training content generation is realized.

The embodiment of the invention discloses an autism spectrum disorder subtype division method and device, a medium and a program product. The method comprises the following steps: constructing a connection brain map based on neuroimaging data and a functional brain region division template of an ASD individual; constructing a brain age regression model, predicting the social brain age of the ASD individual based on the connection brain map and the brain age regression model, and obtaining the brain age difference of the ASD individual in combination with the actual brain age of the ASD individual; obtaining an ADOS social score of the ASD individual, and carrying out clustering analysis on the ADOS social score and the brain age difference to obtain a clustering subtype; and carrying out behavioral verification and neural dimension verification on the clustering subtypes, and constructing a combined portrait among the subtypes, the behavior features and the neural features based on a verification result. According to the method, the social brain age can be predicted by constructing the connection brain map, the clustering subtypes are divided, the combined portrait is constructed, a doctor can be accurately assisted to detect ASD subtype neural development differences, and discovery and application of subtype specific biomarkers are assisted.

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 present invention discloses a method for early warning of intracranial electroencephalographic epileptic seizures based on multi-path graph brain network modeling, which belongs to the technical field of electroencephalogram analysis. The method includes proposing a multi-path graph brain network modeling framework for epileptic seizure prediction; constructing a multi-path brain map based on three cross-frequency coupling (CFC) relationships in neuroscience, wherein the frequency bands in each channel of the electroencephalogram signal form the nodes of the map, and the three CFC relationships form the edges of the map; designing a multi-path brain map learning network with a joint fusion module to learn the patterns in the multi-path brain map and form a final representation. The present invention can analyze iEEG signals, use the designed multi-path brain map learning network to mine the patterns therein and form a unified representation, thereby improving the accuracy of epileptic seizure prediction.

The embodiment of the invention discloses a brain disease diagnosis method, a storage medium and electronic equipment. The brain disease diagnosis method comprises the following steps: acquiring medical image data of a brain; preprocessing the medical image data to obtain a brain tissue image and a brain map; respectively inputting the brain tissue image and the brain image into a pre-trained brain disease diagnosis model, wherein the brain disease diagnosis model comprises at least one convolution module, at least one image convolution module, at least one fusion module and an output module; extracting geometric texture features of the brain tissue image through the convolution module, and extracting topological relation features between brain regions from the brain image through the image convolution module; fusing the geometric texture features and the topological relation features to the node features through the fusion module to obtain new node features; and determining a brain disease corresponding to the medical image data through the output module according to the new node features, and outputting the brain disease.

The invention relates to a language area positioning method and device based on nuclear magnetic resonance imaging and a bias coefficient. According to the method, the structure nuclear magnetic image and the brain function nuclear magnetic image are obtained by scanning the detected person, the nuclear magnetic image and the brain function nuclear magnetic image are registered, the visual three-dimensional brain map is output, and the Pearson's correlation coefficient of the seed point is combined; voxel counts of the left brain and the right brain within a threshold range are extracted, and whether the detected person has a left brain advantage or a right brain advantage or a bilateral brain advantage can be distinguished by combining with lateral coefficient calculation.

The invention provides an electronic medical record feature extraction method based on natural language processing, and the method comprises the steps: obtaining an electronic medical record of a brain injury patient, the electronic medical record comprising preoperative brain map data, medical record text records, and postoperative brain map data; extracting medical record character records by using a natural language processing model, and determining text features; on the basis of the preoperative brain image data and the postoperative brain image data, brain region features are determined; based on data time nodes in the electronic medical record of the brain injury patient, performing feature fusion on the text features by using the preoperative brain map data and the postoperative brain map data; and based on the brain region features and the text features, determining electronic medical record features of the brain injury patient. According to the scheme, an electronic medical record is processed by using a natural language through a multi-modal data processing scheme, and the electronic medical record is further combined with the brain map features of the neural image data, so that organically combined multi-modal data features capable of being applied by an artificial intelligence technology are formed, and a foundation is laid for subsequent application.

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.

A method of predicting brain age for a subject having major depressive disorder (MDD) comprises obtaining at least one medical image of a brain of a subject; producing a brain map based on the at least one medical image; segmenting the brain map into more than one brain regions; and calculating a brain age prediction of the subject based on a predetermined set of key features for each of the brain regions; wherein the subject has MDD.

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 provides an aphasia classification method based on artificial intelligence, a storage medium and a server, and the method comprises the steps: obtaining brain map data and tested data of a target object, the brain map data comprising connection strength matrixes and time sequence data of M brain regions in the brain of the target object, and imaging indexes of the M brain regions, the tested data comprises scale data; performing feature extraction on the brain map data and the tested data, and determining brain map features and scale features; determining an input feature based on the brain map feature and the scale feature; and inputting the input features into an aphasia classification model to determine an aphasia classification result. According to the scheme, feature extraction is performed by introducing the brain map data and combining the scale data, the artificial intelligence classification model is constructed, and classification and recognition of aphasia are realized.

The invention relates to a language area positioning method and device of a nuclear magnetic resonance imaging technology. According to the method, the structure nuclear magnetic image and the brain function nuclear magnetic image are obtained by scanning the detected person, the structure nuclear magnetic image and the brain function nuclear magnetic image are registered, the visual three-dimensional brain map is output, and the Pearson's correlation coefficient of the seed point is combined; the position of the core language area is judged by setting a proper selection threshold and combining the size of the selected area, and the classification of the core language area can be accurately judged.

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.

The invention provides a nervous system disease evaluation method and system based on a brain network graph, and the method comprises the steps: obtaining brain graph data of a target object, the brain graph data comprising time sequence data and a time-varying connection matrix of M brain regions in the brain of the target object; based on the brain map data of the target object, sliding window analysis is carried out, and a brain region feature sequence is determined; and inputting the brain region feature sequence into a nervous system disease evaluation model, and determining an evaluation index of the target object in a specified nervous system disease, the specified nervous system disease including one of schizophrenia, depression and Alzheimer's disease. On the basis of brain map data, sliding window analysis is carried out to realize multi-scale dynamic feature extraction, on the basis of model design and on the basis of taking bidirectional LSTM as a core, a full connection layer is introduced to carry out cross-brain region interactive modeling, and on the basis of loss function design, a sample imbalance factor is fully considered to improve the assessment accuracy of a specified nervous system disease.

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 application discloses a method for fusing and recognizing autism brain maps based on multi-modal brain map information, and is applied to the field of image recognition, and aims at the problem that the prior art fails to fully utilize multi-modal nuclear magnetic resonance image data and is insufficient in model interpretability; the application extracts brain region features from structural magnetic resonance images, functional magnetic resonance images and three modal data such as phenotypes, then performs multi-modal fusion, and constructs a brain region map structure; a graph convolution module and a loss function are designed to learn how to extract brain map deep features; based on the brain map deep features, a multilayer perceptron classifier is trained through ABIDE data set label data, and better performance indexes such as accuracy, sensitivity and heterogeneity can be obtained; and the trained multi-modal brain map fusion autism recognition model can be used for assisting autism diagnosis and recognition.

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 present invention relates to the technical field of brain network construction, and in particular to a dual-stream spatiotemporal brain network analysis method embedded with group priors, comprising: S1, preprocessing brain functional images; S2, dividing the brain into several brain regions and extracting average time series; S3, calculating connection weights to construct edges of the brain map and outputting a symmetrical correlation matrix; S4, defining a graph isomorphism network under spatial features, using the correlation matrix R as the graph isomorphism network input, and outputting a spatial feature label Z1; S5, collecting BOLD signals of brain functional images and inputting them into a model to obtain a temporal feature label Z2; S6, respectively converting the spatial feature label Z1 and the temporal feature label Z2 into the spatial feature label Z1 and the temporal feature label Z2. 2 Dimensionality reduction and aggregation are performed to obtain the same-dimensional label Z; S7, using the same-dimensional label Z to establish a group-based attraction graph to achieve classification and recognition. The present invention uses spatiotemporal feature labels to construct a group graph G p The new labels embedded with group priors are obtained in conjunction with node feature updates, which can effectively improve the classification and recognition accuracy of brain functional images.

A method of predicting brain age for a subject having a mental health condition. The method comprises obtaining at least one medical image of a brain of a subject; preprocessing the medical image to produce a brain map; segmenting the brain map into more than one brain regions; and calculating a brain age of the subject based on a predetermined set of key features for each of the brain regions.

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 invention belongs to the technical field of graph representation learning, and discloses a neurodegenerative disease auxiliary diagnosis method based on a space-time brain graph Transform. In order to solve the problems that time and space are often separated, the integrity of space-time dynamic in brain communication is neglected, the space-time dependency relationship between brain regions cannot be fully captured, and heterogeneity exists during multi-modal data fusion in an existing brain map representation method, the invention provides a multi-modal data fusion method. Functional magnetic resonance imaging data are processed through a time-space brain map construction strategy based on a Wasserstein distance to obtain a functional brain map, diffusion magnetic resonance imaging data are processed in combination with a Pearson's correlation coefficient and a threshold to obtain a structural brain map, and modal heterogeneity is relieved through a dual alignment module composed of local comparison pooling and a global prompt strategy. And finally, averaging classification results of the two types of brain maps and outputting a neurodegenerative disease diagnosis result. The invention has excellent performance in diagnosis of various neurodegenerative diseases, and provides an effective solution for clinical diagnosis.

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.