56 results about "BrainMaps" patented technology

The invention discloses a method for identifying an autism brain image through multi-mode brain image information fusion, is applied to the field of image identification, and aims to solve the problems that multi-mode nuclear magnetic resonance image data are not fully utilized and the model interpretability is insufficient in the prior art. The method comprises the following steps: respectively extracting brain region features from three modal data of a structural magnetic resonance image, a functional magnetic resonance image and a phenotype, then carrying out multi-modal fusion, and constructing a brain region map structure; designing a drawing convolution module and a loss function to learn how to extract brain map depth features; on the basis of brain map depth features, a multi-layer perceptron classifier is trained through ABIDE data set label data, and better performance indexes such as accuracy, sensitivity and heterogeneity can be obtained; the trained multi-mode brain image fusion identification autism model can be used for assisting autism diagnosis and identification.

The invention provides a network graph generation method based on brain image data, and relates to the technical field of brain connection.The method comprises the steps that brain image data (including fMRI data and sMRI data) of a target object are obtained, the brain image data are preprocessed and registered, and registered brain graph data are obtained; dividing the brain map data into a plurality of brain regions based on a set brain map, and determining time sequence data of each brain region; based on the time sequence data of each brain region, a function connection matrix is determined, and non-diagonal elements in the function connection matrix reveal the connection strength between the brain regions; and based on the brain region and the functional connection matrix of the brain map data, generating a brain network map (which can be visualized) of the target object. According to the scheme, the sMRI data (the precision is millimeter level) is introduced as a registration basis, so that the registration precision can be improved, and a brain network diagram with higher precision can be generated subsequently.

The invention provides a depression prediction model training method and device based on knowledge-guided comparative learning, and belongs to the technical field of intelligent medical treatment, and the method comprises the steps: calculating a functional connection matrix between time sequence data of each region of interest based on different brain maps, and training a network model; and performing feature fusion on the global semantic relationship features and the domain knowledge sub-graphs of the to-be-tested patient under the at least two brain maps to obtain a high-order embedding representation of the to-be-tested patient, and inputting the high-order embedding representation of the to-be-tested patient into the trained prediction network to obtain a prediction result. According to the method, a plurality of maps are used for constructing complementary brain map representation, a default mode network closely related to depression is introduced for enhancement of different sub-maps, cross-contrast learning is designed on the basis of multi-view and multi-mode to learn robust representation, the problem of data sparseness is relieved, and the method has the advantages of being high in robustness and high in robustness. And the generalization ability and the prediction accuracy of the model are improved.

The invention provides an abnormal region recognition method for brain map data, which comprises the following steps: acquiring the brain map data of a target object, the brain map data comprising time sequence data and a connection strength matrix of M brain regions in the brain of the target object; performing sliding window analysis on time sequence data of M brain regions in the brain map data, and determining K groups of time-varying connection matrixes; performing statistical feature extraction and dynamic feature extraction based on the K groups of time-varying connection matrixes, and determining statistical features and dynamic fluctuation features of each brain region; based on the statistical characteristics and the dynamic fluctuation characteristics of each brain region, determining input data of each brain region; and inputting the input data of each brain region into the abnormal region recognition model to obtain an abnormal region recognition result output by the abnormal region recognition model. According to the scheme, whether the abnormal brain region exists or not can be detected by analyzing the connection function of each brain region in the brain map data of the target object.

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 relates to the technical field of brain network construction, in particular to a group prior embedded double-flow space-time brain network analysis method, which comprises the following steps of: S1, preprocessing a brain function image; s2, dividing the brain into a plurality of brain regions, and extracting an average time sequence; s3, calculating edges of a connection weight construction brain map, and outputting a symmetric correlation matrix # imgabs0 #; s4, defining a graph isomorphic network under spatial features, taking the correlation matrix R as the input of the graph isomorphic network, and outputting to obtain a tag Z1 related to the spatial features; S5, collecting BOLD signals of brain function images, and inputting the BOLD signals into a # imgabs1 # model to a # imgabs2 # model to obtain a tag Z2 related to the time features; s6, performing dimensionality reduction and aggregation on the spatial feature tag Z1 and the time feature tag Z2 to obtain a same-dimensional tag Z; and S7, establishing a group-based attraction graph # imgabs3 # by using the same-dimensional label Z to realize classification and identification. According to the method, the spatial-temporal feature tags are utilized to construct the group graph Gp, and node feature updating is matched to obtain new tags embedded into group priori, so that the classification and recognition accuracy of the brain function image can be effectively improved.

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 invention discloses a processing method, device and equipment for resting-state brain function magnetic resonance imaging, and relates to the technical field of computers. According to the method, the time sequence features of each brain region of the user are obtained based on historical resting state brain function magnetic resonance imaging of the user, and then brain map construction is performed according to the time sequence features of each brain region through two different brain map construction modes to obtain brain maps constructed at different angles; when learning is carried out on the basis, the mastery degree of features in resting-state brain function magnetic resonance imaging is improved; then, further diffusing the brain maps constructed at two different angles through a diffusion model, respectively performing feature extraction through two different encoders to obtain brain map features at different angles, and further applying the brain map features to map contrast learning represented by the brain maps, so that the feeling visual field of the brain map contrast learning method is expanded, and the brain map contrast learning efficiency is improved. And the extraction learning ability of the brain map comparison learning method on the intricate interaction between the brain areas is improved.

The invention provides an industrial neural network graph mode query method and device based on an SIPyOC-RC model. The method comprises the following steps: acquiring a pre-constructed industrial neural network graph based on the SIPyOC-RC model; wherein the industrial neural network graph comprises a brain graph and an instance graph, the brain graph comprises brain graph nodes and a connection relation, the instance graph comprises instance graph nodes and a connection relation, and the instance graph nodes are instantiation results of the brain graph nodes; obtaining a user query request, and analyzing the user query request into an intention format based on the SIPyOC-RC model; and performing mode query on the industrial neural network diagram according to the intention format to obtain a mode query result. According to the industrial neural network graph mode query method and device based on the SIPyOC-RC model provided by the invention, mode query of the industrial neural network graph based on the SIPyOC-RC model is realized, rapid, efficient and accurate general query of one graph can be realized, and real-time accurate query of enterprise information is assisted.

The invention discloses an autism classification algorithm based on combination of dynamic and static brain networks. A static brain network encoder effectively extracts global brain network features and generates functional connections with richer semantic information; a dynamic brain network encoder independently encodes each subsequence in the dynamic connection and fuses all codes according to an attention mechanism; and the graph classifier recombines the results of the first two modules into a high-order function connection brain graph and performs classification based on a GCN algorithm. According to the method, deep features in a static function connection matrix are effectively learned by introducing the static brain network encoder, and the dynamic brain network encoder module is introduced, so that the method has the capabilities of learning time dynamics of a brain network and integrating dynamic function connection, and the brain fitting capability of the model is greatly improved; and finally, through representation learning of a graph classifier, the model can deeply learn classification features of the brain to diseases from two aspects of a dynamic brain network and a static brain network, so that the classification performance of the model can be improved.

The invention discloses an intracranial electroencephalogram epileptic seizure early warning method based on multipath map brain network modeling, and belongs to the technical field of electroencephalogram analysis. The method comprises the following steps: providing a multi-path map brain network modeling framework for epileptic seizure prediction; the multi-channel brain map is constructed based on three cross-frequency coupling (CFC) relationships in neuroscience, a frequency band in each channel of the electroencephalogram signal forms nodes of the map, and the three CFC relationships form edges of the map; and designing a multi-path brain map learning network with a joint fusion module to learn modes in the multi-path brain map to form a final representation. According to the method, the iEEG signals can be analyzed, the modes in the iEEG signals can be mined by utilizing the designed multi-channel brain map learning network, uniform characterization is formed, and the accuracy of epileptic seizure prediction is improved.

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.

Embodiments of the present disclosure disclose a brain disease diagnosis method, a storage medium, and an electronic device. The brain disease diagnosis method comprises: acquiring medical imaging data of a brain; preprocessing the medical imaging data, to obtain a brain tissue image and a brain map; separately inputting the brain tissue image and the brain map into a pre-trained brain disease diagnosis model, the brain disease model comprising at least one convolution module, at least one map convolution module, at least one fusion module, and an output module; extracting a geometric texture feature of the brain tissue image by means of the convolution module, and, by means of the map convolution module, extracting from the brain map a topological relationship feature between brain regions; fusing the geometric texture feature and the topological relationship feature to a node feature by means of the fusion module, to obtain a new node feature; and, by means of the output module and on the basis of the new node feature, determining a brain disease corresponding to the medical imaging data, and outputting the brain disease.

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.

A system and a method of producing a curative game by at least one processor may include obtaining a brain map data structure, associating specific brain regions with respective, audiovisual stimuli; receiving an expert diagnosis of a neurological impairment of a subject; based on the expert diagnosis, prompting the subject to engage in at least one evaluation activity that corresponds to the neurological impairment, via a user interface (UI); assessing a functionality of one or more brain regions of the subject based on the evaluation activity; selecting one or more brain regions based on the assessed functionality; and creating at least one audiovisual, curative game designed to stimulate the selected brain region based on the brain map data structure.

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 discloses an electroencephalogram decoding method based on an electroencephalogram filter and a multi-scale adaptive convolutional network, which comprises the following steps of: firstly, carrying out preprocessing such as data standardization on an original electroencephalogram signal, establishing a function connection relation graph between different electroencephalogram electrodes according to a phase-locked value (PLV), and constructing the electroencephalogram filter based on the function connection relation graph; according to the method, mixed information in electroencephalogram signals can be effectively filtered out, the resolution of the electroencephalogram signals is enhanced, then a multi-scale adaptive convolutional network based on an attention mechanism is designed, one-dimensional convolution kernels of different lengths are adopted to extract relative global and local time features in the electroencephalogram signals, and meanwhile, due to physiological factors, the time features of the electroencephalogram signals are extracted. According to the significant difference of electroencephalogram signals among different subjects, an attention mechanism is adopted to weight features extracted from different convolution kernel lengths so as to adaptively select a feature extraction scheme adaptive to the electroencephalogram signals of different subjects, the generalization ability of a model in a cross-subject scene is improved, then the electroencephalogram signals are subjected to convolution in a channel dimension, and the convolution performance of the electroencephalogram signals is improved. And finally, inputting the extracted features into a full connection layer to obtain a classification result of the electroencephalogram signals.

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.

A device, used in conjunction with a brainwave detector, includes a display unit configured to display at least one object initial information of an application program, a brainwave signal receiving module for receiving at least one brainwave signal from the brainwave detector, an analysis and judgment unit analyzing and judging the at least one brainwave signal to generate mental activity classification information, and a recover control unit generating alteration information based on the mental activity classification information, recovering the object initial information into object expectation information according to the alteration information, and displaying the object expectation information on the display unit.

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.