27 results about "Brain activation" patented technology

The invention discloses a brain decoding system based on a functional magnetic resonance image and a potential diffusion model. The brain decoding system comprises a functional magnetic resonance image feature extraction module, an image generation module and an image output module, the functional magnetic resonance image feature extraction module combines spherical convolution and a mask auto-encoder, neural activity features related to visual stimulation are extracted from functional magnetic resonance imaging fMRI data, and the image generation module generates an image with high semantic information by using the neural activity features as conditions. And the output module processes and outputs the generated image. The method solves the major challenges of reconstructing visual stimulation from fMRI data, including the problems of low signal-to-noise ratio and high dimension, effectively reduces the dimension of fMRI data, accurately captures the distance between brain regions, and learns local and global brain activation modes by introducing a feature learning algorithm based on surface fMRI data; besides, the design of an anti-noise potential diffusion model (NR-LDM) further improves the quality of a reconstructed image by reducing the influence of fMRI noise, an image with more accurate semantic information can be generated, and improvement of the accuracy of brain decoding based on fMRI data is facilitated.

The invention provides an emotion recognition method based on brain activation region multi-view comparative learning. The method comprises the following steps: acquiring a to-be-recognized electroencephalogram emotion signal; performing space-time transformation on the to-be-recognized electroencephalogram emotion signal to obtain a to-be-recognized space-time signal; inputting the space-time signal to be recognized into a pre-trained double-path decoupling multi-view model to obtain an emotion recognition result; two encoders used for extracting time features and space features are arranged in a final double-path decoupling multi-view encoder, so that multi-dimensional information in a space-time signal to be identified can be fully excavated and utilized, and decoupling of corresponding space-time features is realized; a space-time signal sample is divided according to the activation degree of a brain region to obtain a high activation region and a low activation region, a targeted data enhancement strategy can be executed according to different activation degrees of the brain region, and on the basis, a pre-trained double-path decoupling multi-view model is obtained through training by using the sample subjected to data enhancement; and the emotion recognition accuracy and reliability of the pre-training double-path decoupling multi-view 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 relates to a gradient-based tactile topology mapping model construction method. The method comprises the steps of obtaining functional imaging data and structural imaging data of magnetic resonance scanning of a testee; constructing a voxel-level functional connection matrix based on a voxel space in the functional imaging data; constructing a vertex-level structure connection matrix based on a cortex space in the structure imaging data; based on a gradient dimension reduction analysis processing method, matrix similarity calculation and dimension reduction analysis are carried out on the function connection matrix and the structure connection matrix, and a plurality of gradient components and variance interpretation quantities are obtained; and on the basis of the variance interpretation quantity, determining a functional main gradient and a second structural gradient, and constructing a tactile topology mapping model in combination with a tactile topology distribution rule. According to the method, the problem that the current tactile perception topological mapping is not accurate is solved, and more accurate mapping of tactile perception and brain activation is realized.

The invention discloses a sacral marrow magnetic stimulation urination disorder rehabilitation system and method based on an electroencephalogram closed loop, and relates to the technical field of biomedical engineering, the system comprises an electroencephalogram signal acquisition module, a signal processing module, a task guiding module, a control module and a sacral marrow magnetic stimulation module; the electroencephalogram signal acquisition module is used for acquiring an electroencephalogram signal when a user executes a urination-related motor imagery task; the signal processing module carries out preprocessing, feature extraction and intention recognition on the collected electroencephalogram signals. According to the urination disorder rehabilitation system and method provided by the invention, accurate rehabilitation treatment of urination disorder is realized by fusing electroencephalogram signal recognition and sacral marrow magnetic stimulation technologies, the activity characteristics of the brain urination center can be captured in real time, the opportunity and intensity of sacral marrow magnetic stimulation can be dynamically adjusted, high matching with the brain activation rhythm is ensured, and the rehabilitation effect of urination disorder is improved. The recovery of the urination function of the patient is effectively promoted, and the recovery effect is improved.

EEG cap for recording electrical brain activity, comprising: a) a textile basic structure, b) a plurality of defined electrode positions, c) a central sensor module with amplifier, analog-digital converter and wireless data transmission unit, d) a mechanically and electrically contactable connection system between electrodes and module, e) an integrated energy supply using a lithium polymer cell.

The invention discloses a child learning disorder detection and evaluation system based on a near-infrared N-back task normal form. The system comprises a task stimulation presentation device, a near-infrared brain function imaging device, a data processing module and a result interpretation module. Key brain activation and behavior indexes are extracted by presenting an interesting N-back task and synchronously collecting brain blood oxygen signals and behavior data. The core of the method is that a result interpretation module compares indexes with threshold groups and grades the indexes and the threshold groups according to a preset quantitative interpretation rule, and then automatically outputs four types of refined evaluation results of high, moderate, low cognitive load type and task insensitive type according to a determined mapping relation between brain activation and behavior grades. According to the method, the problems that a traditional method is high in subjectivity, brain behavior data are disjointed and cognitive strategies cannot be distinguished are solved, and objective and automatic accurate evaluation is achieved.

This invention provides an apparatus, method, and storage medium for assessing schizophrenic tendency based on near-infrared data. The apparatus includes a processor configured to: acquire near-infrared data of a subject; process the near-infrared data and convert the processed near-infrared data into a test image; input the test image into a classification model to obtain a classification result for the subject, wherein the classification result indicates the subject's tendency to be either healthy or schizophrenic. This invention normalizes near-infrared data based on a preset data set formed by fusing second data sequences from multiple healthy subjects and multiple schizophrenic individuals. This avoids the loss of brain activation information caused by normalization at different scales, resulting in higher accuracy when the classification model classifies the test image converted from the normalized near-infrared data.

The present invention discloses a brain decoding system based on functional magnetic resonance imaging and latent diffusion model, comprising: a functional magnetic resonance imaging feature extraction module, an image generation module and an image output module; the functional magnetic resonance imaging feature extraction module combines spherical convolution and masked autoencoder to extract neural activity features related to visual stimuli from functional magnetic resonance imaging (fMRI) data, the image generation module uses these neural activity features as conditions to generate images with high semantic information, and the output module processes and outputs the generated images. The present invention solves the major challenges of reconstructing visual stimuli from fMRI data, including its low signal-to-noise ratio and high-dimensional problems. By introducing a feature learning algorithm based on surface fMRI data, the dimension of fMRI data is effectively reduced, the distance between brain regions is accurately captured, and local and global brain activation patterns are learned; in addition, the design of the noise-resistant latent diffusion model (NR-LDM) further improves the quality of the reconstructed images by reducing the influence of fMRI noise, and can generate images with more accurate semantic information, which helps to improve the accuracy of brain decoding based on fMRI data.

A method for mapping functions of a brain includes receiving an MRI data set for a subject comprising fMRI data acquired with the subject lying in MRI scanning equipment in a state of rest, generating a voxel-wise correlation map that identifies, for each of a plurality of voxels of the brain, a measure of the degree of time correlation between spontaneous brain activations at one voxel of the brain as revealed in the resting-state fMRI data and spontaneous brain activations at each of the other voxels of the plurality of bran voxels as revealed in the resting-state fMRI data. The voxel-wise correlation map is input to a trained 3D convolutional neural network based machine learning algorithm to generate a functional connectivity map identifying a location where a predefined brain function is performed within the subject's brain by identifying the voxels involved in performing that predefined brain function.

To provide a sensory transmission system and a sensory transmission method that can appropriately protect personal information of a subject.SOLUTION: A sensory transfer system includes: a first device that detects, when perceived by the first subject, brain activation information of a first subject; an estimation device that estimates, on the basis of the detected brain activation information of the first subject, recollected sensory information that a second subject different from the first subject recalls in response to a perception and encodes the recollected sensory information using the brain activation information of the first subject; and a second device that decodes the encoded recalled sensory information and provides a stimulus to the second subject so as to recall the decoded recalled sensory information.SELECTED DRAWING: Figure 2

The application discloses a kind of about dysphagia magnetic resonance intelligent image diagnosis system, it is related to medical image processing and diagnostic technique field, including: swallowing state functional magnetic resonance acquisition module;Swallowing movement timing decomposition module;Virtual swallowing contrast parameter quantification module;Aspiration pneumonia risk prediction module;Diagnostic report generation module.The application will be obtained by traditional dependence X ray swallowing contrast leakage-Aspiration degree, pharyngeal residual state and pharyngeal transport time such as key quantitative index, by the brain activation timing and functional connection characteristics of functional magnetic resonance imaging Equivalent mapping, so that patients do not need to bear ionizing radiation can obtain and contrast gold standard highly comparable quantitative evaluation parameters, the application not only can realize no radiation equivalent quantitative evaluation, also can create new "functional survival brain area" evaluation and rehabilitation target positioning ability, establish the model for preventing aspiration pneumonia to occur, provide reliable quantitative standard for clinical rehabilitation.

The present invention relates to a method for identifying potential surgically operable target areas in the brain of an epilepsy patient. According to the present invention, the method comprises the following steps: providing a computerized platform that models the various areas of the primate brain and the connectivity between the areas; providing a model of the epileptogenic area and a model of the propagation of epileptic discharges from the epileptic area to the propagation area; obtaining a personalized computerized platform for the patient; deriving potential target areas based on modularity analysis; evaluating the effectiveness of the target areas by simulating epileptic seizure propagation in the personalized patient computerized platform; evaluating the safety of the target areas by simulating the spatio-temporal brain activation pattern under defined state conditions and comparing the simulated spatio-temporal brain activation pattern obtained before removing the target areas with the spatio-temporal brain activation pattern obtained after removing the target areas; and identifying the target areas that meet both the effectiveness and safety evaluation criteria as potential surgically operable target areas.

The present invention discloses a cognitive activity prediction method integrating network topology and physical constraints, comprising the following steps: (1) constructing a brain network adjacency matrix based on resting-state functional magnetic resonance imaging data and a functional connectivity method; (2) calculating the physical distance between brain regions according to a brain template to obtain a spatial constraint matrix; (3) constructing a functional asymmetric path connection considering the functional hierarchical differences between brain regions; (4) calculating the shortest path between brain regions based on the functional asymmetric path connection matrix and considering physical space constraints to predict cognitive brain activity; (5) executing the model by the leave-one-out method to obtain the predicted brain activity of each brain region, and finally calculating the similarity between the predicted and actual brain activations. This method not only considers the topological properties of the brain network, but also combines the spatial geometry and functional hierarchical characteristics of the brain. By verifying this method on a real data set, the experimental results show that the present invention significantly improves the accuracy of cognitive activity prediction.

The invention discloses a potential cargo anchor prediction method and device based on neural imaging psychological topic analysis, relates to the technical field of cargo anchor potential prediction, and solves the problems of relatively high subjectivity, long evaluation period and high trial and error cost of cargo anchor potential evaluation in the prior art. The method comprises the following steps: obtaining functional magnetic resonance imaging signals and behavior data of tested audiences, information of goods in a goods carrying video and market performance data of a goods carrying anchor, and analyzing the preprocessed functional magnetic resonance imaging signals to obtain an average brain activation image, individual psychological theme score data are generated and calculated to obtain psychological theme score data of each anchor carrying goods, a data set is constructed and trained to obtain a market performance prediction model and apply the model, and total consumer feedback of the market is predicted through data of laboratory small sample testees. The efficiency, accuracy and scientificity of potential cargo anchor prediction are improved, and the trial and error cost is remarkably reduced.

The application discloses a transient co-activation pattern analysis method and system, and relates to the field of functional magnetic resonance technology. fMRI data of natural stimulus processing of a plurality of subjects is acquired; the fMRI data of each subject is preprocessed to obtain preprocessed fMRI data; a subject-to-subject analysis method is used to calculate the preprocessed fMRI data to obtain stimulus-induced signals; spontaneous activity signals and non-neural signals in the stimulus-induced signals are filtered out by a linear regression method to obtain task-induced signals; a co-activation pattern analysis method is used to analyze the task-induced signals frame by frame to obtain brain co-activation patterns; a K-means clustering method is used to identify and extract repeatedly appearing subject-to-subject co-activation states in the brain co-activation patterns; the subject-to-subject co-activation states are reordered and visually displayed to obtain visualized transient brain activation patterns. The application improves the accuracy and sensitivity of a dynamic co-activation pattern analysis method of the brain under continuous stimulation.

The present invention discloses a method for selecting neural control targets based on brain-muscle interaction information decoding. The method includes synchronously collecting EEG data and electromyographic data corresponding to the EEG leads and electromyographic leads of a subject during a motor control task; forming a first weight set with the corresponding eigenvalues ​​in a preset first EEG lead number set according to the numerical sorting results of all brain activation eigenvalues ​​calculated based on the EEG data; forming a second weight set with the corresponding eigenvalues ​​in a preset second EEG lead number set according to the numerical sorting results of all brain-muscle interaction eigenvalues ​​calculated based on the EEG data and electromyographic data; calculating lead weights based on the first weight set and the second weight set, and selecting the lead with the largest weight as the neural control target brain area. The present invention can effectively improve the accuracy of neural control target selection.

A method for measuring brainstem-cortical activation characteristics in migraine subjects involves measuring brainwaves in pain-related brainstem and cortical regions, evoking somatosensory responses through electrical stimulation, and capturing brainstem and cortical activation signals to establish a judgment model. The method includes the following steps: wearing an EEG sensing unit and electrical stimulation device on the subject and emitting electrical stimulation; recording thousands of EEG data points; locating the brain current source to establish a hybrid brain model integrating volumetric and surface detection; estimating the current source distribution based on the hybrid brain model and reconstructing a dynamic brain activation map; extracting the amplitude of components from pain-related cortical and subcortical brainstem regions from the EEG data; and comparing the effective connectivity strength between the pain-related cortical and subcortical brainstem regions with the judgment model to generate comparison information and output migraine characteristic information.

The present invention is applicable to the field of brain science and technology, and provides a brain network analysis method for the social conformity effect, the method comprising: performing magnetic resonance imaging on the brains of several participants when they are performing risk decision-making tasks with different decision-making conditions, obtaining a first brain function image of each participant, preprocessing each first brain function image, performing brain activation analysis on each second brain function image obtained after the preprocessing, obtaining active brain regions significantly activated in the risk decision-making task of each participant, performing a psychophysiological interaction analysis on the second brain function image based on the active brain regions, obtaining a dynamic connection pattern between different brain regions in the individual's brain, so as to reveal the dynamic neural network of the brain in the social conformity effect decision-making situation, thereby realizing a more accurate and comprehensive method to reveal the action mechanism of the social conformity effect at the neural level, and laying a scientific foundation for the development of targeted risk management and behavioral intervention strategies.

The emotion recognition method based on brain activation area multi-view contrast learning provided by the application comprises the following steps: obtaining an emotion signal to be recognized; performing space-time transformation on the emotion signal to be recognized to obtain a space-time signal to be recognized; inputting the space-time signal to be recognized into a pre-trained dual-path decoupling multi-view model to obtain an emotion recognition result; by setting two encoders for extracting time features and space features in the final dual-path decoupling multi-view encoder, multi-dimensional information in the space-time signal to be recognized can be fully mined and utilized, and the decoupling of corresponding space-time features is realized; by dividing the space-time signal sample into a high activation area and a low activation area according to the activation degree of the brain area, a targeted data enhancement strategy can be performed according to the different activation degrees of the brain area, and the pre-trained dual-path decoupling multi-view model is trained by using the data-enhanced sample, thereby improving the accuracy and reliability of the pre-trained dual-path decoupling multi-view model for emotion recognition.

An object of a system according to an embodiment is to generate a dialogue by utilizing a user's memory, reduce a sense of loneliness, and promote activation of the brain.SOLUTION: A system includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a photograph or a memory from a user. The analysis unit analyzes the information received by the reception unit. The generation unit generates a dialogue on the basis of the information analyzed by the analysis unit. The providing unit provides the user with the dialogue generated by the generating unit.SELECTED DRAWING: Figure 1

The invention discloses a method for improving fMRI analysis performance by applying a phase generated by Hilbert transform, and belongs to the field of biomedical signal processing. First, phase data is generated from fMRI amplitude data using a Hilbert transform method. The complex data is then constructed along with the fMRI magnitude data. Furthermore, a data driving method, such as a complex independent component analysis method of blind source separation, is adopted to extract interested space brain activation components. According to the method, an extraction result which is similar to complex data constructed by MRI scanning phase data but is obviously superior to that of amplitude data is obtained. Taking default network component extraction as an example, the total number of effective voxels is increased by 90% compared with an amplitude data result, and is reduced by less than 0.6% compared with an extraction result of complex data constructed by scanning phase data. Therefore, the method can widely improve the analysis performance of the existing amplitude-only fMRI data, and has an important value in the aspects of promoting brain function research and objective marker development and pathogenesis research of neuropsychiatric brain diseases.

A data acquiring unit (21) that acquires visual information or auditory information, and that acquires brain activation information; a tag generating unit (32) that generates a tag in which the visual information or the auditory information, the brain activation information, and a keyword are associated with an object identified from the visual information; and a storage unit (14) that stores the tag.

The invention is applicable to the technical field of brain discipline, and provides a brain network analysis method for social conformity effect, which comprises the following steps: performing magnetic resonance imaging on brains of a plurality of participants when performing risk decision-making tasks with different decision-making conditions to obtain a first brain function image of each participant, preprocessing each first brain function image, performing brain activation analysis on each second brain function image obtained after preprocessing to obtain an active brain region significantly activated by each participant in a risk decision task, and performing psychological and physiological interaction analysis on the second brain function images based on the active brain region to obtain a risk decision task. According to the method, a dynamic connection mode between different brain regions in an individual brain is obtained to reveal a dynamic neural network of the brain in a social conformity effect decision-making situation, so that a more accurate and comprehensive method is realized to reveal an action mechanism of the social conformity effect on a neural level; and a scientific foundation is laid for developing targeted risk management and behavior intervention strategies.

A method for mapping functionally related brain regions of a subject. The method includes receiving a structural brain image and a dataset of resting-state functional MRI (rs-fMRI) three-dimensional (3D) image frames of a brain of the subject includes 3D image frames of the subject's brain over time. A functional connectivity map identifying groupings of functionally connected voxels is overlaid on the structural brain image. Spontaneous brain activations associated with each voxel in a grouping of functionally connected voxels are time-correlated with spontaneous brain activations in one or more other voxels in the grouping of functionally connected voxels. A reference location map for a pre-defined resting state network is generated, including reference information output. The reference location map includes brain reference locations that are informative of the one or more resting-state networks to assist a user in identifying the resting state network of the functional connectivity map.

A data acquiring unit that acquires visual information or auditory information, and that acquires brain activation information; a tag generating unit that generates a tag in which the visual information or the auditory information, the brain activation information, and a keyword are associated with an object identified from the visual information; and a storage unit that stores the tag.

The present application relates to a method, system, computer device and storage medium for determining the association of brain activation areas, and relates to the field of neural data analysis. The method includes: obtaining a structural image and a functional magnetic resonance imaging activation map; aligning the structural image with the functional magnetic resonance imaging activation map to obtain an activation distribution map; aligning the structural image with a standard brain area map to obtain a brain area segmentation map; obtaining the number of whole-brain pixels and the number of segmented brain area pixels; obtaining the number of whole-brain activated pixels according to a preset probability threshold; corresponding the number of whole-brain activated pixels to the segmented brain area to obtain the number of segmented brain area activated pixels; obtaining a probability mass function of the number of activated pixel distributions based on the number of whole-brain activated pixels, the number of segmented brain area pixels and the number of segmented brain area activated pixels; and determining whether each segmented brain area is associated with a stimulation site based on the probability mass function. The present application has the effect of reducing the computational cost and storage cost of determining the association between brain activation areas and stimulation sites.