18 results about "Brain functioning" patented technology

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.

This application relates to an auxiliary analysis system based on neural activity synchronization, comprising: a cooperative task output module that outputs a multi-person cooperative task; a task execution module that adjusts the synchronous participation of each target object in the cooperative task within the same time window based on the multi-person cooperative task; a data acquisition module that collects brain function signals and behavioral data of each target object during the execution of the multi-person cooperative task; an interference control module that collects interference information; a group neural activity synchronization calculation module that, based on the collected brain function signals, behavioral data, and interference information of each target object, quantifies the neural activity synchronization of any two target objects during the cooperative task to obtain a neural activity synchronization index; a group synchronization index and a group synchronization network structure are obtained based on the neural activity synchronization index; and a risk monitoring module that obtains auxiliary analysis results. This system can achieve objective auxiliary identification, dynamic assessment, and closed-loop optimization of ASD risk status.

The application discloses a brain function state and multi-modal image data association method and device, the method comprises the following steps: extracting fusion features from multi-modal brain image data samples, and determining corresponding brain function state labels; according to the membership function, the membership matrix of the fusion feature and the brain function state is obtained, the parameters of the membership function are determined by inputting the fusion feature into the membership function parameter prediction model; according to the membership matrix and the five-state Geng prior matrix, a dynamic Geng constraint matrix is generated; according to the fusion feature and the corresponding brain function state label, the membership matrix and the dynamic Geng constraint matrix, the association relationship prediction model is trained; after obtaining the multi-modal brain image data of the subject, the association relationship between the brain function state and the fusion feature is obtained through the membership function and the association relationship prediction model, and the association relationship among the brain function state, the brain region and the fusion feature is formed. The application can effectively establish the complex nonlinear association between the brain function state and the multi-modal image data.

This application relates to the fields of medical image processing and AI-assisted diagnosis, and specifically to a collaborative perception method based on multi-scale brain network features. The method includes: acquiring resting-state functional magnetic resonance imaging (fMRI) data of a subject and constructing a brain functional connectivity matrix after preprocessing; collaboratively extracting multi-scale brain network features from the brain functional connectivity matrix through parallel global and local perception flows; fusing the global and local feature representations across scales to generate a collaborative feature representation; and outputting classification results based on the collaborative feature representation using a classifier. This method can address the technical problems of existing single-scale models, such as high risk of missed diagnoses due to perceptual blind spots and insufficient discriminative power for complex pathological patterns, thereby improving the overall classification performance of the model.

This application relates to the field of data processing technology, and discloses an orthogonal fusion model of emotion, fatigue, and subjective will, as well as a method for assessing mental state. For the problem of emotion recognition, this application proposes a spatiotemporal entropy feature extraction method, which has good adaptability to small sample EEG data; for the problem of low recognition rate in fatigue state recognition, it proposes a power spectrum normalization feature extraction method for brain functional connectivity, which shows higher accuracy in fatigue state recognition; for the problem of feature fusion of emotion, fatigue, and work will, it proposes a mental state assessment model based on the Taguchi orthogonal method that fuses subjective and objective indicators. Experimental results show that the spatiotemporal entropy feature emotion recognition, the power spectrum recognition fatigue state recognition of brain functional connectivity, and the orthogonal fusion mental state assessment model of subjective and objective indicators proposed in this invention can complete the task of recognizing emotional state, fatigue state, and mental state.

The application provides a method for cognitive evaluation and intervention of rescue personnel under brain signal and physiological load regulation, and belongs to the field of cognitive evaluation. The method comprises the following steps: collecting brain function signals and peripheral physiological signals of rescue personnel during a task and extracting corresponding features; constructing multiple cognitive state indexes based on the brain function features and constructing a physiological load index based on the peripheral physiological features; taking the physiological load index as a regulation factor, mapping it into a regulation factor, combining a preset regulation sensitivity coefficient of each cognitive dimension, correcting the cognitive state indexes, and obtaining corrected cognitive state indexes; comparing the corrected cognitive state indexes with a preset threshold to determine whether each cognitive dimension is in an abnormal state; and generating an intervention strategy according to the determination result and executing the intervention strategy by dynamically adjusting the output mode of task information. In this way, the non-specific interference of physiological signals on cognitive evaluation is effectively isolated, and appropriate intervention is performed according to the evaluation result.

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.

Provided are a brain functional area location method, a wearable edge computing device, and a storage medium. The includes: obtaining first video data and second video data which are a multi-angle head video recorded when a user's head is not wearing a wearable apparatus and a multi-angle head video recorded when the user's head is wearing the wearable apparatus, respectively; obtaining a first three-dimensional model and a second three-dimensional model of the user's head based on the first video data and the second video data, respectively; obtaining relative position information between a detection device in the wearable apparatus and the user's head based on the first three-dimensional model and the second three-dimensional model; and registering the first three-dimensional model with a predetermined standard spatial template, and obtaining a brain area position of the user corresponding to the detection device based on a registration result and the relative position information.

The present application relates to the technical field of electroencephalogram signal recognition processing, in particular to a learning interest recognition method and system based on a topological constraint brain network model, comprising the following steps: S1, collecting multi-channel electroencephalogram signals of a user under a preset task stimulus; S2, preprocessing the electroencephalogram signals; S3, performing sliding window segmentation on the preprocessed electroencephalogram signals to obtain multiple time window signal segments; S4, for each time window signal segment, constructing a corresponding brain function network matrix, constraining the connection between channels based on the spatial topological relationship of the electroencephalogram channels to form a topological constraint matrix, and determining a functional connection matrix of the connection weight based on the functional connection significance of the signals between the channels, obtaining an adjacency matrix that fuses the topological constraint and functional connection information as the brain function network matrix; and S5, inputting the brain function network matrix into a pre-trained classification model, and outputting a learning state classification result of the corresponding user in the time window from the classification model, wherein the learning state includes an interesting state and an uninteresting state.

Provided are a brain functional area location method, a wearable edge computing device, and a storage medium. The includes: obtaining first video data and second video data which are a multi-angle head video recorded when a user's head is not wearing a wearable apparatus and a multi-angle head video recorded when the user's head is wearing the wearable apparatus, respectively; obtaining a first three-dimensional model and a second three-dimensional model of the user's head based on the first video data and the second video data, respectively; obtaining relative position information between a detection device in the wearable apparatus and the user's head based on the first three-dimensional model and the second three-dimensional model; and registering the first three-dimensional model with a predetermined standard spatial template, and obtaining a brain area position of the user corresponding to the detection device based on a registration result and the relative position information.

This invention discloses a brain network representation learning method and system based on multi-view diffusion. First, a shared adjacency matrix is ​​constructed from the same resting-state functional magnetic resonance imaging (fMRI) data based on the Pearson correlation coefficient. Under this shared topology, both the FC (front-end view) and LA (back-end view) views are built. Second, the commonality strength scores of node features in the FC and LA views are calculated. Singular value decomposition is used to identify and discard common noise features across subjects. Then, intra-view topology enhancement and inter-view feature propagation are performed synchronously on a unified graph topology, and information interaction is achieved through a cross-attention mechanism. Finally, the dual-view representations are adaptively fused using an attention mechanism to complete disease diagnosis. This invention solves the problems of incomplete brain functional state representation and insufficient information utilization caused by the reliance on a single perspective in existing brain network analysis methods. It can effectively suppress noise and significantly improve the classification performance of brain diseases in complex scenarios such as class imbalance.

The application belongs to a network construction evaluation method, aiming at the technical problems that the existing brain function network construction method lacks personalized partition, the nonlinear neural correlation is not well described, the cross-scene generalization ability is weak, and it is difficult to support the precision and large-scale clinical application of brain-computer interface neural regulation, a personalized brain function network construction and evaluation method for brain-computer interface regulation is provided, the personalized brain function network is constructed based on the neural activity mode representation, the personalized brain function network construction for heterogeneous fMRI data is realized by fusing the neural dynamics knowledge constraint and the data-driven modeling, and the personalized brain function network construction for heterogeneous fMRI data can be used as a general modeling tool, and reliable technical support is provided for brain-computer interface neural regulation.

This invention discloses a method and system for early autism screening based on fNIRS and action feature bimodal fusion, comprising a neural signal acquisition module, a behavioral video acquisition module, a brain network analysis unit, an action analysis unit, and a multimodal fusion decision unit. The brain network analysis unit constructs a dynamic brain functional connectivity map and introduces a graph attention network to automatically learn the importance of key brain regions and their connections. The action analysis unit uses the AlphaPose algorithm to extract key point sequences of the human body and combines bidirectional long short-term memory networks to model action temporal dependencies. The multimodal fusion decision unit employs an attention-based decision-level fusion mechanism, adaptively weighting the classification probabilities of the two branches. This invention achieves multidimensional information complementarity and intelligent fusion for autism by fusing brain signals and behavioral action features, exhibiting strong screening objectivity, high interpretability, and excellent classification accuracy, providing an efficient and automated solution for early autism screening in primary healthcare settings.

This invention proposes a method and system for extracting dynamic brain functional networks for continuous cognitive tasks based on functional near-infrared spectroscopy. The method includes: performing concentration conversion and preprocessing on acquired fNIRS signals; using cubic spline interpolation to achieve temporal alignment of multiple subject sequences; constructing an individual-level dynamic functional connectivity matrix using a sliding time window and Fisher-Z transform; splicing multiple subject data along the time dimension to construct a population matrix; applying temporal population independent component analysis to perform blind source separation; and integrating DIFFIT order selection and ICASSO stability assessment mechanisms to extract stable components; finally, performing spatial topological mapping and temporal evolution analysis. This application can simultaneously acquire the spatial structure and continuous activation trajectories of brain networks, effectively solving the problems of population modeling bias and discontinuous state division, and significantly improving the accuracy of dynamic brain network extraction.

To provide a method for evaluating dementia, and a composition for preventing or treating a decline in brain function, or for maintaining or improving brain function. [Solution] The gut microbiota of healthy individuals, individuals with mild cognitive impairment, and individuals with Alzheimer's disease were compared. As a result, microorganisms belonging to the genus Faecalibacterium were selected as gut microbiota associated with cognitive function. Furthermore, it was revealed that Faecalibacterium prausnitzii, which possesses specific DNA, has an improving effect on cognitive decline such as learning and memory impairment.

This invention belongs to the field of medical image analysis technology, specifically relating to a brain disease identification method and system based on a spatiotemporal multi-view functional brain network. The method includes dividing a preprocessed brain functional image into several brain regions based on a predefined brain region template, extracting a time series matrix, and dividing the time series matrix into several time windows. Within each time window, a correlation matrix is ​​calculated using several functional connectivity metrics to construct a spatiotemporal multi-view functional brain network. This invention uses a sliding window technique to divide the brain region time series into multiple time windows, capturing the dynamic time-varying characteristics of brain functional connectivity and avoiding static analysis from obscuring key temporal information. Simultaneously, it integrates four functional connectivity metrics—Pearson correlation, higher-order functional connectivity, mutual information, and sparse representation—to characterize linear relationships, higher-order interactions, nonlinear dependencies, and sparse dependencies between brain regions, respectively, fully utilizing complementary information from multiple views to significantly improve the comprehensiveness and richness of the functional brain network representation.

A cognitive impairment auxiliary analysis method based on multi-view brain network feature fusion, belonging to the field of medical information processing technology, solves the technical problem that existing technologies struggle to fully exploit the correlations and complementarities between multi-level structural features of patients' brain functional connectivity data, leading to low accuracy in assessing patients' brain functional status. This method uses rs-fMRI as input data, extracts multi-level brain connectivity information by constructing functional connectivity networks and higher-order functional connectivity networks, and builds a multi-view local-global graph neural network model (MVLG-GNN) to achieve collaborative modeling, adaptive fusion, and auxiliary analysis combining group relationships of subjects from multiple views of brain connectivity features. This invention is also applicable to the field of Alzheimer's disease brain data processing.