70 results about "Brain functioning" patented technology

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 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 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.

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 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.

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 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.

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.

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 invention discloses a dementia identification method based on an electroencephalogram norm network and double-current attention fusion, which comprises the following steps: collecting resting-state electroencephalogram data of a subject, and preprocessing the resting-state electroencephalogram data; constructing a normalized standard brain network mode based on age change; calculating the deviation degree of each brain function connection of the subject relative to the healthy norm; extracting an image feature vector Ie and a numerical feature vector Ve from the deviation matrix by adopting a parallel double-flow architecture; according to the image feature vector Ie and the numerical value feature vector Ve, respectively obtaining an image feature ViTfeed and a numerical value feature zfeed, and fusing the ViTfeed and the zfeed to obtain a final feature vector Ffuse; and sending the final feature vector Ffuse into a downstream classifier, carrying out final classification discrimination, and outputting a final prediction result. The dementia identification method based on the electroencephalogram norm network and the double-current attention fusion has the advantages that the image features and the numerical features can be effectively fused, and the identification accuracy and the early diagnosis sensitivity of the dementia are improved.

The invention relates to an electroencephalogram emotion recognition method and system based on a self-adaptive multi-view neural network, and belongs to the technical field of brain-computer interfaces and emotion calculation. The method comprises the following steps: dividing a multi-channel electroencephalogram signal into continuous time windows, and forming a time sequence input sample by four adjacent time windows; extracting a multi-band differential entropy feature of each time window as a node initial feature; fusing electrode space proximity and brain biological symmetry prior to construct a basic matrix, modulating and applying sparse constraint through a learnable attention mechanism, and generating an individualized brain function connection topological graph; a parallel double-branch deep network is designed, a graph convolution branch extracts global spatial-temporal features from graph structure sequences corresponding to four time windows, and a one-dimensional convolution branch extracts local frequency-space features and fuses the local frequency-space features; node-level domain confrontation and graph structure collaborative regularization are comprehensively applied in training, and emotion categories are output. According to the invention, the cross-subject identification performance can be improved.

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.

The invention belongs to a network construction evaluation method, and aims to solve the technical problems that an existing brain function network construction method is lack of personalized partitions, insufficient in nonlinear neural association description, weak in cross-scene generalization ability and difficult to support precision and large-scale clinical application of brain-computer interface neural regulation. According to the brain-computer interface regulation-oriented personalized brain function network construction and evaluation method provided by the invention, a personalized brain function network is constructed based on neural activity mode characterization, and personalized brain function network construction of heterogeneous fMRI data is realized by fusing neurodynamics knowledge constraint and data-driven modeling; the method can be used as a universal modeling tool for heterogeneous fMRI data, and provides reliable technical support for brain-computer interface nerve regulation and control.

This invention provides a parameter conversion method and system for multimodal brain network maps. The method includes: acquiring and comparing multiple source brain regions and multiple target brain regions of a single subject; constructing a set of overlapping brain regions between each target brain region and the source brain region; and statistically analyzing the number of white matter fiber tracts and brain functional connectivity coefficients between each set of overlapping brain regions. Based on this, the remapping coefficients of the white matter fiber brain network and the brain functional connectivity coefficient brain network are calculated respectively. The method also includes: acquiring and statistically analyzing the set of overlapping brain regions between the target brain regions and the source brain regions of multiple subjects, as well as the corresponding number of white matter fiber tracts and brain functional connectivity coefficients; calculating the variance of the brain connectivity strength of the first experimental group and the first control group under the source map, and the second experimental group and the second control group under the target map; and weighting and summing the source brain connectivity statistics between the overlapping brain region sets using influence weights to obtain the target brain connectivity statistics between the target brain regions.

The invention relates to the technical field of brain wave scanning monitoring, in particular to a brain wave scanning user eyelid state recognition method and device, equipment and a storage medium. The method comprises the following steps: responding to brain wave scanning task triggering, and continuously obtaining a user face image; performing feature collection on the user face image to obtain an eyelid feature coordinate pair and a reference feature coordinate pair; determining a reference feature spacing value based on the reference feature coordinate pair, and determining an upper and lower eyelid spacing value of the current detection single eye based on the eyelid feature coordinate pair; combining the upper and lower eyelid spacing value with the reference feature spacing value to obtain a normalized spacing value corresponding to the current video frame; judging whether the normalized interval value is in a preset eye closing judgment interval in the continuous video frames or not, and if yes, determining that the current detected single eye is in an eyelid closing state; and when and only when both the left eye and the right eye of the user are in the eyelid closed state, judging that the user is in the eyelid closed state. The method can support self-service brain function detection in a non-professional environment.

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 discloses an FNIRS brain function state decoding method and system based on a graph information bottleneck, and belongs to the technical field of brain-computer interface and neural signal processing, and the method mainly comprises the steps: constructing a brain network graph based on a channel space position of an fNIRS data segment; a data segment is input into a deep learning model which sequentially comprises a graph convolutional network (GCN) module, a graph information bottleneck (GIB) module and a time sequence processing module, a unique cascade processing flow of'spatial modeling-point-by-point compression-time modeling 'is provided, and an information bottleneck mechanism is embedded between spatial feature extraction and time feature extraction. And carrying out instant compression and regularization on the spatial representation of each time point. Through a space-time separation processing flow and embedding a point-by-point information bottleneck, fNIRS space-time feature representation which is most useful and robust for a decoding task can be effectively learned, and the decoding accuracy and the model generalization ability are remarkably improved.

Proposed is a novel method capable of presenting information relating to brain functions.　The information processing method of an information processing device includes: calculating, on the basis of a time-frequency analysis, time-frequency information in a set time range from brain wave signals acquired from a plurality of electrodes; and outputting information relating to brain functions based on the time-frequency information.

The invention discloses a multi-agent-based brain network Hub node identification method. The method comprises the following steps: firstly, constructing a brain function connection matrix by using an ALL brain map; modeling a brain network graph according to the brain function connection matrix, and performing graph learning on the obtained brain network by using a graph convolutional network and a multi-head graph self-attention mechanism to obtain a node feature vector matrix; a multi-agent system composed of a plurality of agents is constructed, the function connection matrix, the brain network diagram and the node feature vector matrix are used as the states of agent input, and the agents are randomly initialized and distributed at different nodes in the brain network diagram; each agent in the multi-agent system selects the action with the maximum Q value through environmental factors, and then the agent moves in the nodes of the brain network diagram according to the selected action; the Laplacian destruction coefficient is used as a reward to be fed back to the intelligent agent; finally, the node set serves as input, and the selected hub node position is output through the multi-agent system.

The invention belongs to the technical field of electroencephalogram signal processing and brain-computer interfaces, and aims at solving the problems that an existing method is insufficient in task related feature extraction and low in classification precision. The method comprises the following steps: collecting and preprocessing a multi-channel electroencephalogram signal; fusing the phase lock value, the amplitude square coherence and the Pearson's correlation coefficient to construct a multi-modal brain function connection matrix, and constructing a discriminative brain function connection diagram based on a task label; a graph Laplacian operator is used to transform an original signal, a task related mode is enhanced, and redundant information is suppressed; inputting the processed signal into a multi-scale time fusion convolution module, and adaptively extracting deep features of different time scales; and finally realizing classification through a full connection layer. According to the method, an end-to-end classification framework is formed through combination of multi-modal function connection analysis, task oriented graph signal enhancement and multi-scale deep feature learning, the brain cooperation mode is comprehensively captured, and the accuracy and robustness of electroencephalogram mode recognition are remarkably improved.