36 results about "Functional brain" patented technology

The invention relates to the technical field of determination of nerve regulation and control precise stimulation targets, and discloses an individualized multi-frequency regulation and control stimulation system for Rett regulation and control. The individualized multi-frequency regulation and control stimulation system comprises a data acquisition module, a structure and function brain image analysis module and a multi-frequency electroencephalogram self-adaptive regulation and control module, the data acquisition module is used for acquiring resting state functional magnetic resonance imaging data rs-fMRI, structural magnetic resonance imaging data sMRI and EEG data, and the structural and functional brain image analysis module is used for identifying an abnormal brain area of an RTT patient and determining an optimal stimulation target. The multi-frequency electroencephalogram self-adaptive regulation and control module comprises two sub-modules including an electroencephalogram state recognition module and an FUS self-adaptive stimulation module, and the electroencephalogram state recognition module is used for analyzing EEG signals, extracting frequency band power change and instantaneous phase information corresponding to a target brain region and providing real-time feedback signals for self-adaptive regulation and control. According to the system, the target spot is accurately determined, and the individualized accurate stimulation target spot is determined by combining the structure and function image and the disease exposure model.

A system for estimating a subject's functional brain age using non-invasive electroencephalography (EEG), the system includes: an EEG acquisition module (101) configured to record multichannel EEG signals from the subject; a stimulation control module (102) configured to present a predefined sequence of cognitive and sensory tasks that specifically target the frontal, temporal, parietal and occipital brain regions, and to generate synchronized event markers; a data processing module (103) configured to prepare the recorded EEG signals by filtering, artifact removal, normalization and segmentation into task-oriented time windows; a machine learning module (104) comprising one or more self-monitoring contrastive learning encoders configured to transform the preprocessed EEG signals into latent feature representations; and a brain age estimation module (105) configured to process the latent feature representations to generate a BrainAge Score indicating a difference between a predicted biological brain age and the subject's chronological age.

The invention provides a brain function network causal analysis method based on phase-space reconstruction and unified GCA, and relates to the field of functional brain network analys.The method comprises the steps that fMRI data are collected and preprocessed, and a time sequence of interested nodes is extracted from the preprocessed fMRI data; for extracting time sequences X and Y of any two to-be-analyzed interested nodes, constructing a variable time delay unified Granger causal model based on phase space reconstruction; and traversing all to-be-analyzed node pairs of interest, calculating the causal direction and strength between each pair of nodes to construct a whole-brain directed causal connection matrix, and performing network metric attribute analysis. According to the method, phase-space reconstruction is taken as a core, a causal analysis framework is provided by unifying GCA, and end-to-end modeling is realized. The final target is to generate a high-fidelity fMRI data model, so that the causal connection relationship is closer to a brain real neural mechanism, and the reliability and the application value of functional brain network research are improved.

The invention discloses a dynamic brain network analysis method guided by neural heterogeneity, and is suitable for the technical field of brain image processing and recognition. The method comprises the steps that functional magnetic resonance imaging data are acquired and preprocessed, an overlapped sliding window is used for dividing the functional magnetic resonance imaging data to construct a dynamic functional brain network, and then the dynamic functional brain network is decoupled into a topological consistency network and a time trend network which conform to brain activities; capturing space and time heterogeneity weights of different brain regions in the brain based on a topological consistency network and a time trend network, and identifying key nodes for driving brain network recombination; further weighting the topology consistency network and the time trend network to obtain a heterogeneity dynamic function brain network; propagation of neural information in a time dimension is simulated based on time propagation graph convolution operation, and spatial-temporal features of brain images are extracted from a heterogeneous dynamic function brain network; and finally, inputting the obtained spatial-temporal characteristics of the brain image into a multi-layer perceptron to predict the data category of the brain image to be recognized, and analyzing the influence of the brain disease image characteristics on the spatial-temporal heterogeneity of the brain region to complete the dynamic brain network analysis guided by the neural heterogeneity.

The application discloses a complex action-based spatio-temporal frequency domain feature fusion action recognition method and system. The application preprocesses the electroencephalogram signal of the complex action motor imagery paradigm, extracts the global spatio-temporal feature by using a time-space partition branch network TG-Net, extracts the frequency domain feature by using a frequency branch network F-Net for the power spectrum density of the preprocessed signal, and performs action recognition after flattening, splicing and the like of the frequency domain feature and the global spatio-temporal domain feature. The application constructs a time-space frequency domain double branch network, deeply fuses and splices the global spatio-temporal feature and the power feature, breaks through the limitation of the traditional method which is limited in the time-space domain, realizes the complementary feature extraction of the motor imagery electroencephalogram signal in the time, space and frequency dimensions, and adopts the partition weighting splicing mode of the functional brain area in the space dimension to highlight the important channel action recognition method, so that the comprehensive feature is acquired, and the classification precision is improved.

This invention relates to the field of brain function image classification and processing technology, specifically to an adaptive high-order fusion learning method for brain function image classification, comprising the following steps: S1, acquiring the brain function image to be classified and performing image data preprocessing; S2, constructing a high-order functional brain network matrix sequence; S3, adaptively learning the weights of different-order functional brain networks, weighted summing them to further output features; S4, capturing the contextual dependencies of the high-order functional brain network matrix sequence; S5, further extracting features and performing weighted fusion of features; S6, predicting and outputting the classification result; S7, parameter tuning. This invention, through end-to-end adaptive high-order functional brain network fusion learning, utilizes adaptive learning of different-order functional brain network weights to enhance the interpretability of the inference process between input features and prediction results, and captures temporal information before and after the generation of the high-order functional brain network matrix through a self-attention mechanism, accurately and efficiently classifying and identifying images of normal brain function and brain disorders.

An apparatus includes an intraosseous appliance formed to have hollow channels open to a proximal extradermal surface of the appliance. Optical emitters and detectors reside within channels of the intraosseous appliance having distal openings in a distal base of the intraosseous appliance, in a lateral surface of the intraosseous appliance, and in an extradermal surface of the intraosseous appliance, so as to establish optical paths between the optical emitters and detectors and, respectively, the dura, bone wall of a bun hole and skin of the subject. Intracerebral and extracerebral electrodes reside within respective channels of the intraosseous appliance having distal openings in the distal base of the intraosseous appliance and in the extradermal surface of the intraosseous appliance, respectively.

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 invention discloses a time-space frequency domain feature fusion action recognition method and system based on complex actions. According to the method, electroencephalogram signals of a complex action motor imagery normal form are preprocessed, a space-time partition branch network TG-Net is used for extracting global space-time features, meanwhile, a frequency domain feature is extracted from the power spectrum density of the preprocessed signals through a frequency branch network F-Net, and the frequency domain feature and the global space-time domain feature are flattened, spliced and then subjected to action recognition. According to the method, the global spatio-temporal features and the power features are deeply fused and spliced by constructing the spatio-temporal frequency domain double-branch network, the design breaks through the limitation that the traditional method is mostly limited to the time-space domain, complementarity feature extraction of the motor imagery electroencephalogram signals in the three dimensions of time, space and frequency is achieved, and the accuracy of the motor imagery electroencephalogram signals is improved. And the spatial dimension adopts a partition weighted splicing mode of a functional brain region to highlight an action recognition method of an important channel, so that comprehensive features are obtained, and the classification precision is improved.

This invention relates to the field of brain network analysis technology, specifically to a multimodal brain network fusion method based on tensor spectral clustering. The method includes: acquiring multimodal brain network data, including structural magnetic resonance imaging (SMRI) data, functional magnetic resonance imaging (fMRI) data, and diffusion tensor imaging (DTI) data; preprocessing the multimodal brain network data; constructing individual morphological brain networks, functional brain networks, and structural brain networks based on the preprocessed multimodal brain network data, and performing normalization processing; fusing the normalized multimodal brain networks using a tensor spectral clustering algorithm to obtain a fused brain network; acquiring the subject's behavioral data, inputting the behavioral data into the fused brain network, and calculating the subject's behavioral prediction values. This invention not only advances research in neuroscience, cognitive science, and psychology but also provides strong support for related clinical applications and personalized medicine.

The invention discloses a large model semantic prompt guided dynamic brain network analysis method and system, and belongs to the application technology of a large model in a dynamic functional brain network. According to the method, semantic priori is introduced, brain region function semantic representations are constructed and aligned with node-level features, and subnet function semantic representations are constructed and aligned with subnet-level structures. On the basis, a time convolution module is constructed to simulate the time change process of the dynamic network. Furthermore, the subnet strength change semantics are used as prompt information to be injected into the model for adjusting the sensitivity of the time features to the stage change. Next, a spatial structure feature extraction module is constructed to further extract a stable topological structure relationship, and graph paper semantic information is introduced to assist in fusing topological features, so that the modeling capability of the model for the importance of a network structure is enhanced; and finally, inputting fusion features from a plurality of semantic hierarchies into a multi-layer perceptron to realize disease state classification, and based on semantic weight and feature contribution analysis, revealing the explanation ability of semantic priori on disease-related brain network changes.

The application relates to a system for realizing depression subtype classification processing based on deep learning multiple fusion brain network graph technology, wherein the system comprises the following modules: a data acquisition and processing module for acquiring resting-state functional magnetic resonance imaging data of a subject; a data preprocessing module for preprocessing the acquired data; a multiple functional brain network construction module for generating three functional connection matrices from obtained functional magnetic resonance imaging (fMRI) data and constructing a graph representation for each coefficient connected matrix; a multiple brain network graph fusion module for improving performance under small sample capacity by using a regularization term based on data enhancement and mapping each graph representation to a feature space by using GAT fusion area groups and difference pool area groups; and a depression subtype classification module for classifying depression subtypes. The application also relates to a corresponding method, device, processor and storage medium thereof. The system, method, device, processor and storage medium thereof help to further optimize the target intervention scheme of neural regulation technology in depression treatment.

Embodiments of the present application provide a brain image processing method, device, equipment and storage medium. The method comprises: determining a target classification feature of each brain region according to an imageomics feature and a functional brain network in a brain image; determining a corresponding target site according to the target classification feature of each brain region; and determining the functional connectivity of the target site according to the classification importance of each brain region facing the target site. The embodiments of the present application can realize accurate analysis of the functional connectivity of the target site of the brain, which helps to assist doctors in effectively analyzing the functional connection damage degree of the target site of the brain, thereby providing accurate clinical reference information for the analysis of the target site by the doctors.

The invention relates to a multi-modal prelingual emotion recognition system for a deaf person, and the system comprises an information collection and preprocessing unit, a functional brain network emotion recognition unit, a facial expression recognition unit, and an optimal decision fusion unit. The emotion of a deaf person subject is predicted through the methods of expression and electroencephalogram signal collection, signal preprocessing and calculation, facial area expression image processing, facial expression recognition, functional brain network emotion recognition and optimal decision fusion. Through fusion of a functional brain network and facial expression recognition, complex dynamic characteristics in electroencephalogram signals of the deaf people can be deeply analyzed, subtle emotional changes of the deaf people before speech are captured, the recognition accuracy and robustness are improved through an algorithm of adaptively adjusting the weight of each mode, and the method has relatively high precision and adaptability, and is suitable for popularization and application. And a more effective technical support is provided for emotion recognition of the deaf people.

The present application relates to the field of medical image analysis and computer-aided diagnosis of brain diseases, and proposes a dual cooperative learning brain network feature classification system and its training method, which specifically comprises: using an automatic anatomical labeling template, constructing static and dynamic functional brain networks based on the average time series signals of brain regions using Pearson correlation coefficients; using a dual-flow network to extract static and dynamic feature representations of the functional brain network; using a pruned and grafted Transformer module to dynamically adjust the features between the static and dynamic functional brain networks and their respective interiors; introducing a cooperative contrast loss function to learn high-level feature representations; and using a multi-layer perceptron to perform feature classification based on the output high-level features. The brain network feature classification system and its training method proposed by the present application not only significantly improve the feature classification performance, but also provide new insights into the interactive dynamics of brain activity.

A method for achieving super-resolution in localizing electrical fields measured at the head surface with electroencephalography through a generative model of the cerebral cortex that has a very high resolution of cortical surface dipoles constructed from the known properties of human cerebral cortex and adapted to optimize the Bayesian explanation the individual's cortical surface electrical fields. The iterative optimization of the prior (generative) with the posterior (observed) fields with extensive data from extended recordings provides a probabilistic estimation of the individual's functional brain activity that can be used to train artificial neural network approximations of the individual's mental activity.

The application discloses a depression image classification method based on a multi-modal dynamic graph convolutional neural network, belongs to the field of medical image processing and artificial intelligence, and utilizes a sliding time window method to divide a time sequence after functional magnetic resonance data preprocessing into multiple time windows, constructs a functional connection matrix of each window, utilizes multiple spatial attention contrast networks to capture time information across time windows, and obtains a high-order dynamic functional brain network; and utilizes a cross-modal graph neural network and cross-modal knowledge distillation to realize complementary information transmission and mode fusion between modes. The application can fully utilize dynamic high-order information of a multi-modal brain network to realize effective classification of depression.

Disclosed is a computer-implemented method for generating a brain dynamics foundation model. The method involves receiving functional neuroimaging data, such as fMRI time series, and identifying regions of interest (ROIs) corresponding to known functional brain areas. A positional embedding is computed using functional connectivity gradients derived from the neuroimaging data. The data is patchified into spatial and temporal patches, from which an observation block is selected and encoded to produce a latent representation. A set of target regions is then sampled from Cross-ROI, Cross-Time, and Double-Cross regions, each comprising patches not present in the observation block. The observation encoder is trained to predict representations of the target regions based on the observation block and their positional encodings. The trained model is adapted to downstream tasks using further neuroimaging data by applying a linear head and fine-tuning the encoder, enabling the model to generalize and make predictions on new task-specific functional neuroimaging inputs.

This invention provides a brain image classification method based on hierarchical graph convolution, comprising the following steps: acquiring resting-state functional magnetic resonance imaging (fMRI) images and constructing functional brain network features; sparsifying the functional brain network features and constructing individual graph convolutional networks to obtain graph embedding features; training a network model based on the graph embedding features and initializing the parameters of the group graph convolutional network using an edge weight encoding mechanism, and redetermining the edge weight parameters of the network model; testing the correctness of the trained network model using a test set and statistically analyzing the classification results. The brain image classification method based on hierarchical graph convolution of this invention, firstly, can extract brain network features using prior brain region information and graph embeddings, exhibiting good accuracy; secondly, by using an edge weight encoding mechanism to encode the edge weights of the group graph convolutional network model, it reduces the influence of non-image information such as age, gender, and image acquisition equipment, resulting in better generalization. The method of this invention has better classification performance compared to existing methods.

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

The present application relates to a method for recognizing motor imagery based on cross-rhythm multilayer coupling brain network of electroencephalogram signals, and a plurality of kinds of motor imagery tasks of normal people are collected by multi-lead electroencephalogram (EEG). According to the collected signals, data preprocessing is carried out: after the node common average reference, a FIR filter band-pass filter is used to obtain the mu rhythm and beta rhythm which are active and have ERS / ERD phenomenon of motor imagery, and then an independent component analysis method is used to remove noise interference such as electrooculogram and electromyogram. On this basis, a multilayer functional brain network is established, the lead signal is used as the node, the phase-locked value is used as the connection, the adjacency matrix is established, and the brain function network features reflecting the activity characteristics of motor imagery are extracted. Three kinds of global network features in the mu rhythm and beta rhythm single-layer network and two kinds of interlayer feature parameters between networks are extracted, and after normalization, the multilayer network features for motor imagery recognition are generated, and the motor imagery is recognized by using a machine learning method such as a support vector machine and using the multilayer network features.

A kind of machine learning-based human brain syntax function analysis method, system, device and medium, method includes: first, construct extreme gradient boosting algorithm (XGBoost) regression model sample set, and train XGBoost regression model for voxel, then calculate each XGBoost regression model output signal by Shapley plus method interpretation theory, obtain the sample interaction matrix corresponding to XGBoost regression model and statistically obtain the syntax vector of voxel, to obtain the syntax function intensity of voxel and the functional brain area when human brain processes different syntax structure, finally, construct the syntax network corresponding to voxel by interaction matrix, and based on the structure of syntax network, the region classification of voxel is carried out, and the function correlation of different regions based on syntax is obtained;System, device and medium are used to realize a kind of machine learning-based human brain syntax function analysis method;The present application shows the contrast of the advantage processing brain area and functional connection of different syntax structure, can be in-depth, comprehensive understanding and explanation syntax function details in human brain and intuitive presentation.

The application discloses a kind of multi-task fMRI generation methods and related equipment based on common-specific functional brain network guidance, method includes the following steps: obtaining the first fMRI data of different diagnostic tasks;Wherein each task corresponds to several fMRI sub-data;First fMRI data input to diffusion model is gradually added noise in time sequence, to train diffusion model to learn the transfer distribution of first fMRI data to pure noise data;Pure noise data input to diffusion model is gradually removed noise in time sequence, until pure noise data is changed into noiseless data as second fMRI data;First fMRI data is decomposed, and the common component and characteristic component of fMRI classification task are obtained;Based on common component, characteristic component and diffusion model, second fMRI data is guided, and multi-task fMRI data set is obtained.The application can be widely used in artificial intelligence technical field.

The application discloses a brain network generation system and method based on a functional brain network classification task, and the system comprises a time sequence encoder, a graph generator and a graph predictor based on a graph convolutional neural network which are sequentially connected; the time sequence encoder is used for encoding a bold signal sequence; the graph generator is used for converting the encoded time sequence feature into a functional brain network graph; the graph predictor is used for classifying and predicting the functional brain network graph; the time sequence encoder comprises a transformer encoder layer and a multilayer perception A which are sequentially connected; the transformer encoder layer is used for extracting a time sequence feature, and the multilayer perception A is used for generating node features of multiple ROIs after dimension reduction processing of the extracted time sequence feature. The application extracts a BOLD signal sequence from each brain region directly to generate a correlation matrix of a brain network, and improves the classification accuracy of a functional brain network.

The invention discloses a multi-agent self-organizing collaborative decision-making method and device driven by a multi-brain-region-like neural mechanism, and relates to the field of multi-agent collaborative decision-making, and the method comprises the steps: constructing a multi-agent cluster with a brain-region-like cognitive architecture according to a task target; according to the cognitive state of each agent in the multi-agent cluster, judging whether each agent and a neighbor agent are in cognitive synchronization or not, and performing information interaction during cognitive synchronization so as to update the cognitive state of each agent; according to the updated cognitive state of each agent, multi-agent self-organization collaborative decision based on game learning is carried out, and an execution decision of each agent is obtained to complete a task target. According to the method, a cognitive synchronization architecture for simulating a cooperative working mechanism of multiple functional brain regions of a human brain is constructed, a plurality of agents are endowed with a distributed reasoning capability, and efficient, robust and adaptive cooperative decision-making based on a game relationship is realized under a centerless scheduling condition.

The present invention relates to a device (20) for analyzing neural activity in the brain of a patient by functional brain imaging to diagnose, assess, follow-up and / or classify a migraine condition of the patient. The device comprises an input (21) for receiving functional brain imaging data of the brain of the patient and an an output (22) for outputting at least one value. Furthermore, the device comprises a processor (23) for determining at least one activity measure indicative of brain activity in at least one predetermined region of interest, and / or between such regions of interest, based on the functional brain imaging data. The processor is adapted to determine and output the at least one value based on (at least) said activity measure(s), The value(s) represent(s) a patient-specific migraine score, a migraine type classification, a prognostic / predictive value, an assessment of influence of different types of migraine triggering events and / or of efficacy and / or suitability of different treatments.

The invention discloses a template-based near-infrared channel brain region automatic mapping and display method, system and device and a storage medium, and the method comprises the steps: taking each detector as a collection channel, correspondingly collecting near-infrared signals, and carrying out the interference removal of each near-infrared signal, so as to calculate the blood oxygen concentration change value of each collection channel; the method comprises the following steps: establishing a corresponding relation between signal intensity of near-infrared signals and activation degrees of brain regions, calculating the activation degree of each brain region, drawing a boundary of a functional region on a preset three-dimensional brain map, marking activation hot spots in the functional region to form an activation map, and marking significant points of blood oxygen concentration change on a preset blood oxygen response curve. The blood oxygen distribution curve graph is formed, automatic positioning of the position of the collection channel is achieved, the manual comparison workload during data analysis is greatly reduced, the analysis efficiency is improved, subjective errors are eliminated through an automatic mapping method based on the template, and the corresponding judgment accuracy of the channel and the anatomical or functional brain area is improved.

1. Name of the product in this design: Intelligent Transcranial Magnetic Therapy Device. 2. Purpose of this design: for the treatment of functional brain diseases. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

The embodiment of the invention discloses a brain signal analysis system based on a global-local coupling spiking neural network and application, and relates to the technical field of biological information analysis, and the system comprises a graph embedding layer, a global-local coupling module, a spiking neural network feature extraction module and a jump enhancement output module which are connected in sequence. According to the embodiment of the invention, through the global-local coupling module, the discharge anomaly (microcosmic) of neurons in the functional brain region and the connection anomaly (macroscopic) of the neurons across the functional brain region are captured at the same time, and the two characteristics verify each other, so that the probability of missed judgment and misjudgment is greatly reduced; the brain signal analysis system based on the global-local coupled pulse neural network provided by the embodiment of the invention not only can output a classification result of whether a patient is ill or not, but also can reversely trace key pathological features.

The application discloses a functional brain network construction method and device, medium and equipment, and relates to the technical field of functional brain network construction. The method comprises the following steps: firstly, based on the resting-state brain functional magnetic resonance imaging of a patient with a potential brain disease, determining the time sequence characteristics of each brain region according to the brain region division of an automatic anatomical marker map, and constructing an initial functional brain network; then, performing spatial convolution on the time sequence characteristics of each brain region, fusing the information of adjacent brain regions of the initial functional brain network into the time sequence characteristics of each brain region after spatial convolution; then, performing clustering on the time sequence characteristics of each brain region after spatial convolution to obtain a clustering mapping matrix, and performing clustering mapping based on the clustering mapping matrix to obtain the time sequence characteristics of each brain region after clustering of the brain regions; and finally, constructing a functional brain network after clustering of the brain regions according to the time sequence characteristics of each brain region after clustering. The functional brain network constructed by the application can more directly represent the characteristics of the potential brain disease.