107 results about "Brain functioning" patented technology

The present disclosure relates to a multi-modal brain network computation method associated with structural function, apparatus, device, and medium. The method is applied to train a brain disease prediction model, and the brain disease prediction model includes an association perception dual-channel generation module, a disease feature regression module, a topological structure discriminator, and a time-space joint discriminator. In a model training process, by performing a multi-level interactive fusion learning on a high-order topological feature of brain functional magnetic resonance data and magnetic resonance diffusion tensor imaging data, a multi-modal time series activity signal of each brain region is obtained.

The invention provides a brain age estimation method based on a dynamic fuzzy learnable brain network, and belongs to the technical field of medical image processing and artificial intelligence. According to the technical scheme, the method comprises the following steps that S1, brain nuclear magnetic resonance imaging of a subject is collected, and preprocessing and data division are carried out; s2, constructing graph structure data, and performing feature extraction and position information embedding on the data; s3, constructing a dynamic fuzzy learnable brain network model comprising a main branch and a local branch, and respectively extracting global and local connection features; s4, introducing a dynamic fuzzy multi-head self-attention module into the main branch to realize effective modeling of global features; s5, a local branch dynamically models a dependency relationship between channels through a convolution filter and a learnable graph attention module; s6, after the features of the main branches and the local branches are fused, brain age prediction is carried out through a multi-layer perceptron. According to the method, the modeling capability of the brain function connection mode is improved, and the brain age prediction task can be more effectively completed.

The invention provides a voice recognition method and device based on a brain-like model, electronic equipment and a storage medium, and the method comprises the steps: obtaining a whole-brain network topological structure according to a brain function network generated by human brain image data, and carrying out the recognition of a whole-brain network through employing a multi-class neuron model as a node and a synaptic plasticity model as an edge, constructing a multi-brain-region pulse neural network as a brain-like model; constructing a speech recognition framework of the brain-like model; electromagnetic intervention is applied to different brain areas of the brain-like model, optimal electromagnetic intervention parameters are determined by analyzing the voice recognition accuracy of the brain-like model before and after electromagnetic intervention, and brain-like model voice recognition is carried out according to the optimal electromagnetic intervention parameters. According to the invention, the speech recognition performance of the brain-like model can be effectively improved, the biological interpretability and neural information processing capability of the brain-like model are further improved, and the development of brain-like intelligence in the application of a mode recognition task is promoted.

The invention discloses a brain function magnetic resonance imaging data analysis method based on a contrast graph neural network, and the method comprises the steps: firstly carrying out the data enhancement of a brain function connection graph, and simulating the heterogeneity of brain function magnetic resonance data, so as to improve the diversity of a data set; secondly, the hidden space embedding features of the brain function connection diagram are efficiently learned by fusing a double-Hough-Laplacian diagram convolutional network and a contraction incentive mechanism; the embedded features are mapped to a group of prototype vectors, and prototype allocation codes corresponding to the embedded features are calculated by adopting a Sinkhorn-Knopp algorithm; performing exchange optimization on prototype codes between different enhanced brain connection diagrams of the same subject through a contrast learning strategy, and compelling codes of homologous subjects to be aligned at the minimum cost in combination with a cross entropy loss function; and finally, applying the pre-training model to a functional magnetic resonance imaging data set of the Alzheimer's disease, and carrying out interpretability analysis on learning features to improve the classification efficiency and pathological analysis of the Alzheimer's disease under a limited tag condition.

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 an infantile autism diagnosis method based on brain function correlation structure modeling and a default mode network, which comprises the following steps of: firstly, preprocessing acquired infantile autism data, and extracting average time sequence data from the acquired infantile autism data to construct a whole brain function connection matrix and a DMN (default mode network) function connection matrix; a fusion weight parameter is initialized and is used for fusing the two constructed matrixes; constructing a cross-dimension dual adaptive attention module to extract features from a time dimension and a space dimension; a product-based embedded graph generation module is defined to generate a graph structure, the nodes correspond to brain regions, and the weights of the edges correspond to the similarity between the nodes; and high-order features are extracted from the generated graph structure by defining a graph convolutional network predictor, and node features are mapped to classification tags for subsequent diagnosis of autism. The method disclosed by the invention has the beneficial effects that the brain function association relationship of the autism patient is disclosed, the interpretability of clinical application is enhanced, and the autism diagnosis accuracy is improved.

The invention discloses a brain network quantitative analysis method based on near-infrared signal time sequence dynamic graph Fourier transform, which comprises the following steps: constructing a brain network based on fNIRS signals, optimizing time sequence alignment by using a dynamic time warping algorithm, and dividing dynamic network time periods by combining adaptive K-Means clustering with an elbow rule; a neighbor topology overlapping coefficient and feature vector centrality analysis are introduced, and a time sequence dynamic graph is constructed to quantify the connection stability between node layers; a time sequence dynamic graph Fourier transform method is provided, brain function signals are mapped to a time-space-frequency three-dimensional joint domain through spectral decomposition of a time-varying graph Laplacian matrix, and a dynamic spectrogram is generated to analyze a multi-scale spatio-temporal evolution mode; a filter is designed to separate low-frequency (global coordination) and high-frequency (local mutation) components, and a function-structure dynamic constraint model is established. According to the method, an efficient and accurate quantification tool is provided for revealing a brain network dynamic recombination mechanism and neural adaptability changes.

The invention discloses a federated learning-based functional magnetic resonance imaging data classification method. The method comprises the following steps of obtaining multi-site resting state functional magnetic resonance imaging data and performing preprocessing; dividing a brain region and extracting a time sequence; constructing a brain function connection network by adopting a Pearson correlation coefficient; a graph sampling aggregation neural network fusing a residual connection structure and a multi-head attention mechanism is trained at each site, and shallow brain region features are reserved; adopting a linear kernel maximum mean value difference loss function to align the brain region node feature distribution of each site, and minimizing the data distribution difference between the sites; and carrying out classification training by adopting cross validation, aggregating parameters of each station through federal weighting, and evaluating classification performance indexes to obtain a final classification prediction result. According to the method, the graph sampling aggregation neural network and the cross-network layer feature alignment method are combined, the heterogeneity problem of multi-site functional magnetic resonance imaging data can be solved, and therefore generalization and classification performance of a global model are improved.

The invention discloses a calculation method for integrating task induction and intrinsic spontaneous brain function activity. The calculation method comprises the following steps: calculating a brain activation mode when an individual executes a corresponding cognitive task based on task state functional magnetic resonance imaging data and a general linear model; identifying individual large-scale nerve avalanche with spatial continuity based on resting state functional magnetic resonance imaging data; the method comprises the following steps: performing principal component analysis on resting state functional magnetic resonance data of an individual to construct a low-dimensional state space; a task-induced brain activation mode and intrinsic spontaneous nerve avalanche are projected to an individual low-dimensional state space; calculating the Euclidean distance between the task-induced brain activity and the intrinsic spontaneous nerve avalanche in the low-dimensional state space; and detecting the prediction effect of the geometric distance on the performance of the tested task through the regression model. The method is verified on a real data set, and experimental results show that the method not only can integrate two basic brain function activities, but also can significantly predict individual cognitive performance differences.

The invention provides a pathological target positioning method and system based on time domain interference stimulation. The method comprises the steps that electroencephalogram data, functional image data and structural image data of a patient are acquired; generating a spatial distribution diagram according to the electroencephalogram data and the structure image data; performing registration processing on the spatial distribution diagram and the functional image data to obtain the spatial distribution diagram and the functional image data after registration processing; the spatial distribution map after registration processing is matched with the spatial position of the functional image data; performing data fusion on the spatial distribution map and the functional image data after registration processing to generate a comprehensive brain function map; phase synchronism analysis is carried out on the comprehensive brain function map, the phase synchronism of different brain regions is evaluated, and the brain region with the phase synchronism larger than a preset threshold value and active in the cognitive task is determined as the pathological target region. According to the embodiment of the invention, the method can achieve the precise recognition and positioning of the AD core pathological target region, and facilitates the precise intervention of the AD core pathological target region.

Systems and methods associated with mind / brain-computer interfaces are disclosed. Certain implementations may include or involve processes of collecting and processing brain activity data, such as those associated with the use of a brain-computer interface that enables, for example, decoding and / or encoding a user's brain functioning, neural activities, and / or activity patterns associated with thoughts, including sensory-based thoughts, determining user attention and / or intentions during interactions within virtual environment and in other applications. Consistent with various aspects of the disclosed technology, systems and methods herein include and / or involve features and functionality enabling hands-free selection of UI elements in virtual environment or on other media.

The invention discloses a neural development disorder co-disease identification system based on a residual image neural network. Belongs to the technical field of neurodevelopment disorder co-disease recognition, and particularly relates to the technical field of co-disease recognition based on a neural network. The system comprises a data acquisition module for acquiring a resting-state fMRI image; the data preprocessing module is used for extracting standardized time sequences of 116 brain regions of the set resting state fMRI image; the multi-band division and feature extraction module is used for performing frequency band filtering on the standardized time sequence of each brain region to obtain a time sequence after each frequency band filtering; in each frequency band, constructing a binary topological matrix based on the PLV matrix and calculating a PLV feature vector based on the PLV matrix; the neurodevelopmental disorder co-disease recognition module is used for analyzing the brain function connection diagram through a residual image neural network model to obtain diagnostic information of neurodevelopmental disorder co-diseases; and the interpretable output module is used for outputting an abnormal brain region by taking the amplitude low-frequency fluctuation as a reference index.

The present invention relates to the field of brain functional data augmentation, and in particular, to a generative artificial intelligence-based brain data augmentation method and device. The method and device comprise: correcting missing channels in low-resolution electroencephalogram data to obtain coarse-grained high-resolution electroencephalogram data; taking the low-resolution electroencephalogram data as input to output corresponding temporal pattern encodings and spatial pattern encodings of the electroencephalogram data; and taking the low-resolution electroencephalogram data and the temporal pattern encodings and the spatial pattern encodings of the electroencephalogram data as input to output fine-grained high-resolution electroencephalogram data, which is then superimposed with the coarse-grained high-resolution electroencephalogram data to obtain reconstructed high-resolution electroencephalogram data. Compared with existing methods, the method of the present invention is capable of preserving and restoring key information and local details in the data, thereby generating more realistic and higher-quality high-spatial-resolution brain data.

The invention provides a brain network construction and analysis method based on multivariable analysis. The method is mainly used for multi-view analysis of a brain structure and a functional network. The technical problem to be solved is that information loss is caused by neglecting necessary multivariate relationships among brain region nodes when univariate analysis is carried out on a brain function network and a brain structure network. According to the method, the canonical correlation analysis method based on data driving is used for constructing the single-mode brain network, the problem that a traditional univariate analysis method neglects necessary multivariate relations is avoided, the complex relation between the brain structure and functions can be reflected more accurately, and a more reliable basis is provided for deep research of a brain working mechanism.

The invention provides a visual motion function test system and method based on full-view surround synchronous visual stimulation, and is applied to the technical field of medical data processing. Visual stimulation parameters and rotating bar motion mode parameters are combined, the rolling direction of a bar and the rotating direction of a rotating bar are synchronously matched, a synchronous presentation scene of visual stimulation and motion tasks is established based on a four-screen linkage control technology and a rotating bar-screen adaptive design, and full-view stimulation constraint conditions are generated; processing is carried out based on visual stimulation parameters, rotating rod reference data, synchronous control logic and dynamic parameter combination constraint conditions, and rod time, rotating speed during falling and motion trail data are collected; processing the collected kinematics data, and combining functional parameters including visual state grouping and brain region neuron activation counting to generate motion balance ability and brain function associated data; and processing by combining normality test and an inter-group statistical method to generate a vision-motion function evaluation result.

The invention discloses a multi-modal interactive brain training system, method, device and medium. The system comprises a multi-modal signal acquisition layer, a brain dynamics modeling layer, a cross-modal cooperative training layer, a three-dimensional evaluation layer and a brain function adaptation scene generation layer. The multi-mode signal acquisition layer synchronously acquires surface physiological, behavior and deep brain image signals; the brain dynamics modeling layer preprocesses the signal, constructs a brain region interaction model and positions a core brain region pair and an interaction strength threshold value; the cross-modal cooperative training layer generates and adjusts a training task according to the training task; the three-dimensional evaluation layer fuses related features to generate a comprehensive rehabilitation score; the brain function adaptation scene generation layer adjusts training scene elements based on brain function bias features. According to the invention, through synchronous acquisition of multi-modal signals and dynamic modeling of brain functions, accurate adaptation of training tasks, scenes and brain region functions is realized; three-dimensional evaluation guarantees effect quantification and mechanism explanation, and rehabilitation pertinence, scientificity and user compliance are improved.

The present invention relates to an electroencephalogram (EEG) emotion recognition method based on brain network and matrix learning, comprising: obtaining an EEG data set of a subject, reconstructing the EEG data by using wavelet packet transform and dividing it into N sub-bands, calculating the phase-locking value between the reconstructed EEG data of different brain regions in each sub-band according to the reconstructed EEG data of different brain regions in each sub-band during emotion fluctuation by using Hilbert transform, and creating a brain functional connectivity matrix corresponding to each sub-band according to the phase-locking value between the reconstructed EEG data of different brain regions in each sub-band; constructing an ensemble learning classification model by using N classifiers and training the ensemble learning classification model; using the trained ensemble learning classification model to identify the corresponding feature sets of the brain functional connectivity matrices corresponding to the N sub-bands, obtaining classification results, and obtaining the EEG emotion classification results. This method can extract rich brain network feature information and further improve the accuracy of EEG emotion recognition.

A multimodal-based interactive chatbot service method for degenerative brain function decline prediction and cognitive training and an apparatus for the same are provided. A method for supporting prediction for a user's degenerative brain function decline may include outputting first reference content, and at least one system conversation triggering at least one user conversation related to the first reference content to the user through a user interface device; receiving at least one user conversation for the first reference content from the user through the user interface device; and based on at least one of text information corresponding to the at least one user conversation for the first reference content, or voice feature information of the at least one user conversation for the first reference content, acquiring a first prediction result for degenerative brain function decline of the user.

A system may comprise hardware and software configured to perform computing functions that mimic at least one computing function of a human brain, wherein the hardware and software comprises: analog circuitry configured to perform signal processing of neural signals obtained from living brain tissue using at least one sensor, a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor digital circuitry and software configured to process the obtained neural signals to generate a representation of a brain function from the obtained neural signals, and to generate parameters for use by analog and digital circuitry to perform computing functions that mimic at least one computing function of a human brain, and the analog and digital circuitry configured to use the generated parameters to perform computing functions that mimic at least one computing function of a human brain.

The application discloses a spatio-temporal graph-based ADHD brain function connection dynamic representation system and method, belongs to the technical field of medical image processing and artificial intelligence, and solves the defects of existing deep learning methods in constructing an ADHD brain function connection dynamic representation. The system designs an end-to-end learnable adaptive graph construction module, automatically discovers individualized topology through node embedding learning and scaled dot-product attention, guarantees physiological rationality by combining Top-K sparsification and prior graph fusion based on an RBF kernel, proposes a double-branch spatio-temporal graph convolution, uses dilated causal convolution in the time branch and uses graph attention in the space branch, realizes spatio-temporal joint modeling through adaptive gating fusion, introduces a multi-view contrast learning framework, designs three types of enhancement (time domain, frequency domain and graph structure) guided by domain knowledge, and fully utilizes unlabeled data by adopting an InfoNCE loss and two-stage training.

The present application discloses a system and interactive method for evaluating the cognitive function of a subject based on fNIRS. The head cap and near-infrared light emitting / receiving probe in the system enable the imaging range to cover the frontal lobe, temporal lobe, and occipital lobe; the cognitive assessment paradigm interactive device sequentially executes the inhibitory control assessment paradigm, the alert attention assessment paradigm, the selective attention assessment paradigm, the core attention network assessment paradigm, the working memory assessment paradigm, and the cognitive control assessment paradigm in a single-block mode in an order of execution from simple to difficult; the signal acquisition device collaboratively collects near-infrared brain function imaging time series data during the sequential execution of each assessment paradigm; the processing device evaluates the cognitive function of the subject based on the near-infrared brain function imaging time series data and the behavioral data generated based on the assessment paradigm. The present application can obtain brain function data of more dimensions associated with cognitive function in a relatively short period of time, which is convenient for clinical evaluation and analysis of the cognitive function of the subject from multiple angles based on fNIRS.

The application discloses a brain function state analysis method and device, which comprises the following steps: collecting a brain image sequence combination of a target object, preprocessing, then performing feature extraction on the brain image to obtain image features, and selecting key image features; performing quantitative analysis on the key image features to generate a feature parameter set; inputting the feature parameter set into a brain function state prediction model to determine the brain function state of the target object; the brain function state prediction model is obtained by integrating and then training a semantic retrieval model and a fine-tuning generation model, the semantic retrieval model is trained based on a brain knowledge document library, and the fine-tuning generation model is trained based on reinforced learning parameters, brain information segments retrieved by the semantic retrieval model, and a labeled data set of a mapping relationship; statistical characteristic values corresponding to each brain function state are determined, and brain function physiological data of the target object is analyzed. The application can quantitatively and standardize overall analysis of the brain function state.

The invention discloses an autistic child electroencephalogram collection cap fusing severe game intervention and a signal processing method.The autistic child electroencephalogram collection cap comprises a structure module, an electroencephalogram collection module, a wireless communication module, a power module, a signal processing module and a game and report module, and the electroencephalogram collection module collects electroencephalogram signals through five electrodes and two ear patch type electrodes; the wireless communication module is used for communication between the electroencephalogram cap and user terminal equipment, the power supply module is used for storing electric energy and supplying power to other modules, and the signal processing module is used for processing and analyzing electroencephalogram signals in the severe game intervention process, including filtering, slicing and normalization processing, and outputting standardized electroencephalogram data. And then the data is analyzed through an MCNN network model, and the condition of the children is diagnosed, so that an effective tool is provided for brain function research and diagnosis while the adaptability of the children with autism is improved.

The invention relates to a multi-point electroencephalogram analysis method and device, a storage medium and electronic equipment, and relates to the technical field of data processing.The method comprises the steps that electroencephalogram data of multiple points of the brain of a target user is obtained; preprocessing the electroencephalogram data to obtain processed target electroencephalogram data; determining a first evaluation result of a first dimension according to the target electroencephalogram data, determining a second evaluation result of a second dimension according to the target electroencephalogram data, determining a third evaluation result of a third dimension according to the target electroencephalogram data, and determining a fourth evaluation result of a fourth dimension; and performing multi-dimensional decision fusion on the first evaluation result, the second evaluation result, the third evaluation result and the fourth evaluation result to obtain a final evaluation result of the brain function state of the target user. The method has the effect of improving the accuracy of brain function state evaluation.

The invention discloses a space factor function principal component method for brain function structure identification. The method comprises the following steps: S1, inputting original data; s2, extracting function data by utilizing a factor process; s3, decomposing the factor load into a smooth function of a space coordinate and an additionally determined piecewise constant matrix; s4, applying the principal component analysis of the function to the potential process to process the correlation on the variables, and obtaining the final form of the function; determining a block structure of the brain; a regression model about the cognitive function is established as a covariable and is used for analyzing the influence of the ROI volume on the cognitive function. According to the method, the internal correlation, the spatial correlation and the segmentation smoothness of the ROI volume curve can be effectively captured, and the low-dimensional scalar features easy to operate are extracted from the ROI volume curve for subsequent regression analysis, so that the prediction precision of cognitive competence is improved.

The present invention discloses a smoothed group information-guided independent component analysis method for brain functional network analysis, which belongs to the technical field of independent component analysis of brain images. The present invention includes performing independent component analysis to obtain components at the group level as reference signals; calculating voxel features to construct a graph regularization term; using voxel features and reference signals as guidance, using multi-objective functions to perform iterative solutions to estimate the independent components of individual subjects; and calculating the time series corresponding to each component in the individual subject based on the extracted components. The present invention overcomes the limitation of the group information-guided independent component analysis method currently widely used in the field of brain functional network extraction that does not optimize the smoothness of the extracted components, and obtains a more accurate brain functional network. Voxel features are introduced as a guide in the process of constructing the objective function, which enhances the spatial smoothness and functional correlation of the results, and can help the new method learn a network that is more in line with the actual working mechanism of the brain.

An artificial intelligence device according to one embodiment of the present disclosure may comprise: a memory for storing a brain-mimicking artificial intelligence model trained through reinforcement learning; a mental health meter for collecting subject data including a numerical value of memory recall confidence, memory recall accuracy, a numerical value of inference confidence, inference accuracy, learning accuracy, and strategic decision-making bias according to the execution of a meta-memory game of a user; and a processor which acquires a plurality of cognitive behavior numerical values from the subject data by using the brain-mimicking artificial intelligence model, acquires a plurality of brain function estimation signals corresponding to the plurality of cognitive behavior numerical values, respectively, and maps each brain function estimation signal to a brain signal corresponding to a specific brain function.

This application relates to a method, apparatus, and device for screening the risk of reading and attention deficit disorder in children. The method includes acquiring multi-channel raw brain blood oxygenation signals under task-induced conditions using a specific layout fNIRS array integrated into a wearable headband, based on a rapid naming cognitive paradigm. Based on the raw brain blood oxygenation signals, a fusion feature vector representing the reading and attention networks is generated by calculating temporal waveform features and frontotemporal functional connectivity strength. The multi-dimensional fusion feature vector is then processed and analyzed using a Transformer classification model to generate classification results indicating the risk level of reading disorders and comorbid ADHD. This application achieves portable and rapid brain function signal acquisition by integrating a targeted fNIRS array with a standardized cognitive paradigm. By fusing temporal dynamics and brain network connectivity features, a multi-dimensional neural representation is constructed. Finally, a lightweight Transformer model is used to output the risk level of reading disorders and comorbid ADHD end-to-end, achieving high-precision automated assisted screening.

This invention relates to a method and system for EEG emotion recognition based on an adaptive multi-view graph neural network, belonging to the field of brain-computer interface and emotion computing technology. The method includes: dividing multi-channel EEG signals into continuous time windows, and using four adjacent time windows as temporal input samples; extracting multi-band differential entropy features of each time window as initial node features; fusing prior knowledge of electrode spatial proximity and brain biological symmetry to construct a basic matrix, and modulating and applying sparse constraints through a learnable attention mechanism to generate an individualized brain functional connectivity topology; designing a parallel bi-branch deep network, where a graph convolutional branch extracts global spatiotemporal features from the graph structure sequences corresponding to the four time windows, and a one-dimensional convolutional branch extracts and fuses local frequency-spatial features; and during training, comprehensively applying node-level domain adversarial and graph structure collaborative regularization to output the emotion category. This invention is beneficial for improving cross-subject recognition performance.

The invention discloses a wearable fNIRS safety helmet integrating self-diagnosis and automatic calibration functions and an implementation method of the wearable fNIRS safety helmet, and relates to the technical field of wearable brain function monitoring devices. Comprising an fNIRS signal acquisition module, an automatic calibration module, a self-diagnosis module, an edge calculation module, a wireless communication module and a man-machine interaction module which are integrated in a safety helmet shell, wherein the fNIRS signal acquisition module, the automatic calibration module, the self-diagnosis module, the wireless communication module and the man-machine interaction module are respectively in communication connection with the edge calculation module. According to the method, signal stability is enhanced, signal drifting caused by factors such as wearing displacement and sweat interference is remarkably reduced, and the effectiveness of fNIRS data in cognitive load, emotion change and consciousness definition evaluation is improved.