256 results about "Entire brain" patented technology

The invention relates to the field of electroencephalogram signal processing, and particularly discloses a multi-mode electroencephalogram characteristic cognitive ability evaluation system based on a neural network, comprising: an acquisition module used for synchronously acquiring whole brain signals and eye movement data during pilot simulation training; the data preprocessing module is used for primarily processing the received whole brain signals and the eye movement data, monitoring the primarily processed data in real time by utilizing a preset monitoring mechanism, and triggering to generate interaction test starting signals if preset symbolic fluctuation occurs in the whole brain signals or the eye movement data; and the test interaction sub-module is used for generating arithmetic test questions according to a preset rule after receiving the interaction test starting signal, and synchronously recording answer correlation parameters of the pilot. By adopting the technical scheme of the invention, the deep-level reaction mechanism of the brain under the stimulation of the special complex cognitive task of flight training can be fully considered, and the cognitive load change of the pilot in the simulated training can be comprehensively and accurately reflected.

The invention belongs to the technical field of food microorganisms, and relates to lactobacillus plantarum and application thereof in preparation of products for relieving insomnia. The preservation number of the lactobacillus plantarum is GDMCC No: 65061. The lactobacillus plantarum can be applied to efficient fermentation of spina date seed juice, the content of GABA, polypeptide, total phenols and total flavonoids in the spina date seed juice is increased, and the content of spinosin, 6 ''-feruloyl spinosin, jujuboside A, jujuboside B and betulinic acid is increased; the reduction of whole brain coefficient and the increase of liver coefficient caused by long-term insomnia are relieved, the bad condition caused by long-term insomnia in a hippocampus DG region in the brain of the mouse is improved, the sleep time of the mouse is prolonged, and the insomnia symptom of the mouse is relieved; the content of inhibitory neurotransmitters 5-hydroxytryptamine, melatonin and gamma-aminobutyric acid in the hypothalamus of the mouse is increased, the content of excitatory neurotransmitter glutamic acid is reduced, and the reduction of organism immunity and oxidative stress of brain tissues caused by insomnia are relieved.

The invention discloses a Parkinson's dyskinesia individualized SCAN network positioning method based on a multi-modal image and deep learning. The method comprises the steps of obtaining multi-modal medical image data, preprocessing the multi-modal medical image data, obtaining a multi-modal structure image and functional connection data, and calculating a spontaneous neural activity index of a whole-brain voxel level; taking a priori brain region related to the spontaneous neural activity index and dyskinesia as a seed point, constructing a seed point voxel function connection graph representing individual brain function connection, and performing nonlinear feature fusion and extraction through the deep learning network model; the bilinear attention network is adopted to capture the interaction information of the feature data and the individual dyskinesia symptom which is significantly related, an individualized SCAN network positioning result is obtained, the structure-function coupling characteristics of the individual brain are comprehensively described, the cross-modal pathological features related to the dyskinesia can be more sensitively recognized, and the accuracy and accuracy of the diagnosis and treatment of the dyskinesia can be improved. And the accuracy and robustness of abnormal brain region detection are obviously improved.

A juvenile depression cognitive impairment subtype classification and prognosis prediction method relates to the technical field of medical treatment, and mainly comprises the following steps: performing clinical evaluation and therapeutic response evaluation on a subject, performing MRI and magnetoencephalogram data acquisition, constructing a whole brain MSN of the subject, identifying MSN abnormal characteristics, obtaining functional connection change of a frequency band when magnetoencephalogram is abnormal, and determining the cognitive impairment subtype classification and prognosis prediction of the cognitive impairment subtype of the subject. A subtype classification model is established by fusing the MSN and cognitive function evaluation data, and a prognosis prediction model is established by analyzing MSN abnormal features, functional connection changes of frequency bands during abnormality, multi-dimensional treatment reactions and high-risk behaviors. According to the method, different levels of fusion measurement are carried out on the juvenile depression with cognitive function impairment brain mechanism through multi-modal brain images, a subtype classification model with diagnosis and treatment values is established, and a prognosis prediction model with clinical transformation potential is constructed; therefore, a theoretical basis and a technical means are provided for individualized precise diagnosis and treatment of the cognitive impairment of the juvenile depression.

The invention relates to the field of biomedicine, and particularly discloses an epileptic focus artificial intelligence positioning system and method based on magnetic resonance and electroencephalography, and the system comprises the following contents: a data collection module is used for synchronously collecting T1 weighted magnetic resonance images and electroencephalogram signals of scalp; the data preprocessing module is used for performing brain tissue segmentation on the magnetic resonance image, obtaining structural features of each brain region, constructing vectors including whole brain structural features, and obtaining magnetic resonance structural feature vectors; artifacts of the electroencephalogram signals are removed, electroencephalogram features of all brain areas are extracted through time-frequency analysis, a matrix containing whole electroencephalogram physiological features is constructed, and the electrophysiological features are obtained; the cross-modal confidence coefficient dynamic evaluation module is used for establishing a bidirectional constraint rule to perform confidence coefficient calibration on the magnetic resonance structure feature vector and the electrophysiological feature; a positioning model construction and training module; a positioning result output module; according to the technical scheme, noise can be reduced during magnetic resonance and electroencephalogram fusion, and the epileptic focus positioning precision is high.

The invention discloses a tumor space-occupying brain network neural image alignment method based on multi-modal fusion, and belongs to the technical field of medical image processing and artificial intelligence crossing. The method comprises the following core steps of multi-modal image heterogeneous feature decoupling, tumor occupation deformation field modeling, functional network topological structure maintenance, cross-modal feature adversarial alignment, dynamic deformation constraint optimization and clinical interpretability verification, and construction of a three-dimensional non-rigid registration network based on a double attention mechanism. And differential homeomorphic mapping of a tumor focus area and normal brain tissue is realized through the cascaded spatial transformation module. Aiming at the problems of insufficient multi-modal feature alignment and brain network topology distortion in the prior art, the invention provides a function connection constrained cross-modal fusion strategy, a graph convolution network is adopted to encode resting state function connection features, and network node displacement caused by tumor occupation is dynamically corrected in combination with deformable convolution and a bidirectional feature competition mechanism; a space consistency loss function based on white matter fiber bundle tracing is designed, and through diffusion tensor imaging feature guide structure-function bimodal joint optimization, the problems of insufficient registration precision in a focus area and whole brain network connection distortion of a traditional method are solved. Experiments show that the registration precision of the method in glioma cases reaches 0.82 mm and is improved by 37% compared with that of a traditional method, and dissection-function consistency of functional network reconstruction around tumors is remarkably improved.

The invention discloses a sleep real-time staging modeling method, a sleep real-time encoding and decoding method and a sleep real-time staging modeling system. EEG, EOG and EMG signals of a subject are collected, statistics, frequency domain and frequency domain characteristics and other characteristics are extracted after preprocessing, sleep stages marked by experts serve as real labels, a model is trained in a supervised learning mode, and real-time classification of the sleep stages is achieved. Furthermore, high-precision encoding and decoding of sleep content are realized by applying stimulation prompt during sleep, collecting and preprocessing whole-brain EEG signals, distinguishing NREM and REM stages, training encoding and decoding models respectively, and aligning nerve characterization during waking and sleep by utilizing comparative learning.

According to the multi-scale brain age prediction model construction method based on the magnetic resonance image and the application, the constructed brain age prediction model is higher in generalization and robustness, higher prediction precision is kept, the whole brain-sub-network-voxel brain age can be predicted, the predicted brain age has better interpretability in the physiological sense, and the brain age prediction accuracy is improved. The difference of brain ages among different sub-networks and a specific mode of PAD and cognition association are explored, the specific sub-network for regulating cognition is found, the difference mode of aging of different brain regions is seen from the voxel level, and the prediction performance of the model is superior to that of a current mainstream neural network model. The method comprises the following steps: (1) data collection; (2) data preprocessing; (3) constructing a whole-brain and functional sub-network brain age prediction model based on a simple full convolutional neural network SFCN method; (4) constructing a voxel level brain age prediction model based on a ScaledDense U-Net method; and (5) carrying out offset correction on the brain age deviation.

The invention discloses a disturbance of consciousness stimulation regulation and control system and method fused with electroencephalogram connection recognition. The system comprises a simulated electroencephalogram signal data acquisition stage, a connection recognition analysis stage, a stimulation parameter optimization stage and an executable stimulation instruction conversion stage. The method has the following advantages and effects that a whole-electroencephalogram activity distribution diagram is generated by simulating an electroencephalogram signal data acquisition stage, a key connection area is identified, and a brain function connection map is generated by utilizing a function connection analysis network and a phase synchronization algorithm in a connection identification analysis stage; in the stimulation parameter optimization stage, space-time correlation between a whole electroencephalogram activity distribution map and a brain function connection map is established, a multi-objective optimization algorithm is adopted to generate an optimized stimulation parameter set through a fusion network, and finally, in the executable stimulation instruction conversion stage, the optimized stimulation parameter set is converted into an executable stimulation instruction based on a self-adaptive control model. Therefore, the accuracy and the self-adaptive capability of electroencephalogram signal stimulation regulation and control are remarkably improved.

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

The invention relates to the technical field of medical image processing, and discloses a brain network image analysis method, system and device for a glaucoma patient and a medium, and the method comprises the steps: carrying out the brain region scanning of the glaucoma patient, and obtaining a high-resolution structure image, a resting state function image and a diffusion tensor image; pre-processing the resting state function image and the diffusion tensor image, and respectively registering the resting state function image and the diffusion tensor image with the high-resolution structure image to obtain a registered resting state function image and a registered diffusion tensor image; performing brain region division on a glaucoma patient, wherein each region is used as a node; on the basis of registration resting state functional imaging, Pearson's correlation coefficients between the nodes are calculated to construct a whole-brain functional network; on the basis of registration diffusion tensor imaging, the number of fiber bundle connections between nodes is calculated to construct a whole-brain structure network; and carrying out coupling analysis on the whole brain function network and the whole brain structure network to obtain an F-S coupling coefficient. The method is helpful for deeply researching the influence of glaucoma on the brain network.

The invention provides a quantitative risk assessment method and system for head and neck artery stenosis, and the method comprises the steps: collecting the medical image of the head and neck artery of a patient, the blood pressure of the upper arm artery, and the heart rate; a narrow local form, a cerebral artery network anatomical structure / geometric size and a microcirculation blood flow automatic regulation mechanism are comprehensively incorporated into hemodynamic analysis through a geometric multi-scale modeling technology, and information such as an arterial stenosis far-end and near-end pressure ratio, cerebral artery blood flow and hemodynamic parameters of a narrow part is output; quantitative information support is provided for risk assessment of head and neck artery stenosis; the method has the advantages that patient data are obtained only through noninvasive measurement, and the risk that complications are increased due to invasive measurement is avoided; according to the method, a geometric multi-scale modeling method is adopted to achieve integrated analysis of the three-dimensional flow field and the whole-brain circulation hemodynamic parameters of the stenosis part, the blood flow regulation effect of brain microcirculation is considered, and the physiological significance and clinical application value of the evaluation result can be remarkably improved.

The invention discloses a brain connection coupling analysis method based on grey matter morphological similarity-white matter fiber bundles, relates to the technical field of neuropsychiatric imaging, and has the technical key points that the method can be used for analyzing grey matter-white matter brain connection group coupling modes of the whole brain level, the level in brain network communities and the level between the brain network communities; the integration characteristics of structural tissues of the brain on different scales can be understood; the reliability of statistical analysis is improved by adopting a spatial autocorrelation correction method; based on a specific machine learning fusion model, the clinical application value of the model is verified.

The invention discloses a control device and computer equipment for a brain health therapeutic apparatus, and relates to the technical field of brain stimulation control, and the method comprises the steps: synchronously collecting an fMRI activity map, sMRI data and whole-brain voxel-level DTI data of a subject, extracting a cortex target region to be stimulated, and reconstructing and generating a triangular mesh model; non-linear registration is carried out on whole-brain voxel-level DTI data and the sMRI space, and a target region vertex direction field is generated; calculating a direction field average direction and a direction field covariance of the direction field at the vertex of the target region; if the covariance of the direction field does not exceed a preset covariance threshold value, controlling the brain health therapeutic apparatus to perform magnetic stimulation operation on the cortex target region according to the average direction of the direction field; and if the direction field covariance exceeds a covariance threshold, screening a first target region vertex sector and a second target region vertex sector, and controlling the brain health therapeutic apparatus to perform time division multiplexing vector scanning between sectors. Therefore, the accuracy of targeted stimulation and the individual suitability are greatly improved.

The invention discloses a white matter microstructure calculation system and method based on multi-shell diffusion-weighted imaging, and the system comprises a data collection module which is used for obtaining diffusion-weighted imaging and structural image magnetic resonance scanning of a testee; the first data processing module is used for obtaining individual fiber orientation distribution and generating a public template of group-specific fiber orientation distribution at the same time; the second data processing module is used for co-registering the individual fiber orientation distribution diagrams to a common template and segmenting the fiber orientation distribution to obtain white matter microstructure indexes of a whole brain level; the third data processing module is used for obtaining a white matter microstructure index of each anatomical fiber bundle level; and the fourth data processing module is used for obtaining a whole brain fiber streamline based on probabilistic fiber bundle tracking and spherical deconvolution information filtering, and generating a white matter microstructure index of a network level in combination with a prior brain map. By adopting the technical scheme of the invention, quantitative calculation and analysis of the white matter microstructure change of the brain are realized.

The invention relates to the field of ultrasonic sleep aiding, and provides an ultrasonic-infrasound coupled sound wave whole-brain sleep regulation and control method and device. The whole-brain sleep regulation and control method based on the ultrasound-infrasound coupling sound waves comprises the steps that electroencephalogram signals of a user are collected through electroencephalogram collection equipment; physiological parameters of the user are obtained through physiological state monitoring equipment; a control device is adopted to control and adjust the fundamental frequency, pulse width, pulse repetition frequency, difference frequency, intensity and other parameters of two columns of ultrasonic waves generated by a sound wave emission device according to the electroencephalogram signals and the physiological parameters; a sound wave emitting device is adopted to generate and emit two columns of ultrasonic waves which are close in frequency and face to face, the two columns of ultrasonic waves are fed into the brain face to face, and the two columns of ultrasonic signals inhibit the cerebral cortex activity when passing through the cerebral cortex; meanwhile, interference occurs in the deep brain target nuclear region, low-frequency infrasound beat frequency waves are formed, electroencephalogram slow wave rhythm resonance is induced, sleep-related neural activities of the deep brain target nuclear region are synchronized, and coordinated regulation and control of the whole brain region are achieved.

The invention belongs to the technical field of brain dynamics modeling, particularly relates to a regulation and control scheme auxiliary analysis method based on a disease specificity whole brain dynamics model, and aims to realize accurate screening of nerve regulation and control schemes. Comprising the steps that S1, multi-mode magnetic resonance imaging data are obtained and preprocessed, structural connection, a brain region BOLD time sequence, static function connection and dynamic function connection are obtained, and prior constraint values of myelination and cortical atrophy are obtained through calculation based on the preprocessed data; s2, parameterizing the local return coupling strength of the model based on a prior constraint value, coupling the models of different brain regions through the structural connection obtained in the step S1, and constructing a whole brain dynamic model through parameter optimization; s3, training a variational auto-encoder by using different groups of static function connections and dynamic function connections; and S4, applying different regulation and control schemes on the whole brain dynamic model, obtaining a brain state evolution trajectory induced by regulation and control through the trained variational auto-encoder, and screening an effective regulation and control scheme.

Methods, systems, and devices are described for reconstructing spatially resolved electrical activity in the brain. In some example embodiments, EEG and MRI data are used to estimate volumetric distribution of electrostatic potential inside the MRI domain throughout the entire brain. Spatially and temporally varying field estimates can be generated using a brain wave model which is based on weakly evanescent transverse cortical wave propagation and constrained using the tissue properties gained from the MRI data. The disclosed techniques enable brain activity imaging with high spatial and temporal resolution, thereby providing a tool for assessing functional brain states and monitoring changes in those states in relation to various normal and pathological conditions.

The invention discloses a dynamic space-time CNN-Transform emotion brain-computer interface decoding method, and relates to the technical field of brain-computer interfaces, a dynamic time feature extraction module is designed according to the sensitivity of multi-scale convolution to an electroencephalogram sequence along a time dimension, and electroencephalogram sequence time feature information is mined by using convolution kernels of different sizes; constructing a local-global spatial feature extraction module by referring to close correlation between asymmetry of left and right brain regions of the brain and an emotional state, and sequentially extracting spatial feature information of the left brain, the right brain and the whole brain of the electroencephalogram sequence; a spatial-temporal feature fusion module is designed, and spatial-temporal feature relations among the left brain, the right brain and the whole brain are mined; the long-time dependency relationship in the electroencephalogram sequence is captured through an attention mechanism by referring to the advantage of Transform on long-time sequence processing, so that the emotion electroencephalogram decoding precision is effectively improved; and finally, performing emotion recognition on the feature sequence subjected to Transform coding by using a multi-layer perceptron to realize end-to-end emotion electroencephalogram decoding.

The invention discloses a PET-based epilepsy postoperative prognosis information generation system and method, and the system comprises a preprocessing module, a target training data generation module, a training module and an application module which are sequentially connected. Through processing the whole brain health PET image, the preoperative whole brain PET image and the postoperative whole brain magnetic resonance imaging, the obtained target training data focuses on the metabolic connection characteristics of the focus area and the whole brain, a new view angle is provided for the postoperative prognosis prediction, so that the target training data is adopted to train the deep learning model, and the prognosis prediction accuracy is improved. A deep learning model focusing on the metabolic connection characteristics of the focus area and the whole brain can be obtained, so that the postoperative whole brain magnetic resonance imaging is accurately processed, and prognosis information is obtained.

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 application discloses a kind of nnUNet zebra fish juvenile whole brain vascular system segmentation methods based on autonomous data set training, it is related to high-resolution imaging technology, image processing and medical image segmentation field, the method makes full use of zebra fish live transparency and fluorescent label advantage, obtains high-resolution whole brain three-dimensional vascular image data, and constructs high-quality segmentation truth value database by semi-automatic segmentation and artificial correction, training is carried out using nnU-Net deep learning model, realize the three-dimensional automatic segmentation of zebra fish brain vascular system signal.The application method significantly improves the degree of automation and precision of image segmentation, effectively solves the problems of low efficiency, high artificial dependence and poor repeatability of traditional brain vascular segmentation.The method is suitable for large-scale high-throughput data processing, can provide efficient, standardized image processing scheme for zebra fish brain vascular development mechanism and brain vascular disease model research, and has wide application prospect.

Targeted and individualized methods are provided herein for determining specific treatment sites based on an individual patient. Embodiments described herein may use functional and / or structural connections to create brain mapping of the patient and / or control groups having the same metrics as the patient, and combinations thereof in determining target locations for stimulation treatment.

The invention discloses an electroencephalogram electrode, an electroencephalogram phase-locked acquisition module and an electroencephalogram acquisition system and method.The electroencephalogram electrode sequentially comprises an ion dielectric layer, an ion regulation and control transistor, a substrate and an extraction electrode which are connected with one another. According to the method, interference noise can be greatly weakened, lower electroencephalogram signal changes can be detected, higher common-mode rejection performance is achieved in the transmission process, electroencephalogram changes can be detected more meticulously, the density of whole-brain detection points can be encrypted by the system, and the spatial resolution of electroencephalogram signals is increased.

The invention discloses a grey matter brain region anti-magnetic component susceptibility map construction and analysis method and application thereof, which can be used for exploring a global distribution mode of cortical iron deposition of an AD patient on a voxel level and positioning a cortical region with susceptibility related to cognition. The method comprises the following steps: (1) carrying out MR data acquisition on a subject on a magnetic resonance scanner; (2) unwrapping the multi-echo phase image by using a phase unwrapping method based on Laplacian; performing brain stripping processing on the amplitude image of the first echo by using a BET algorithm built in FSL software and generating a binary brain mask image; removing a background field of the unwound phase diagram by using a V-SHARP algorithm in combination with the brain mask diagram; calculating a magnetic susceptibility map from the local field map by using an STAR-QSM algorithm; (3) calculating the cortex thickness by using the T1 weighted structure image, and performing post-processing on the 3D-FSPGR sequence structure image; and (4) carrying out voxel-based whole-brain QSM analysis and cortex thickness statistical analysis.

The invention provides a whole cerebral cortex nerve photoelectric information processing method and device under a memory mechanism. The method comprises the following steps: collecting whole cerebral cortex calcium imaging signals and hippocampus sharp ripple signals generated by an experimental animal aiming at each group of visual stimulation sequences; under the same time step, phase-locked correlation analysis is carried out on the whole cerebral cortex calcium imaging signal and the hippocampal sharp ripple signal to obtain a neural activity association relationship, and the neural activity association relationship comprises a corresponding relationship between different visual stimuli and different signal combinations. The neural photoelectric information incidence relation between the whole cerebral cortex and the deep brain nuclei under the memory mechanism can be analyzed in the visual cognition prediction process, namely visual prediction is carried out by analyzing the whole cerebral cortex activity and the hippocampus sharp ripple discharge, and a basis is provided for constructing a visual prediction model based on the neural activity incidence relation; the attention mechanism about the neural activity association relationship is added for constructing the visual prediction model, so that the visual prediction accuracy of the visual prediction model can be improved.

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 provides a multi-element emotion recognition method and device based on electroencephalogram space-time dynamic representation, and relates to the technical field of biomedical engineering and artificial intelligence crossing. The multi-element emotion recognition method comprises the following steps: acquiring an electroencephalogram signal generated when a user watches a target video; performing frequency decomposition on the electroencephalogram signal to obtain a time domain feature map representing time sequence dynamic characteristics of the electroencephalogram signal on a plurality of frequency scales; according to the time domain feature map, obtaining electroencephalogram space-time dynamic representation representing a dynamic evolution mode of the electroencephalogram signal in a whole brain space; determining a target emotion subgroup type to which the user belongs in the plurality of emotion subgroup types, and obtaining a target decoding strategy corresponding to the target emotion subgroup type; and decoding the electroencephalogram space-time dynamic representation through a target decoding strategy to obtain the multi-element emotion of the user. By adopting the method provided by the invention, the defects existing in the traditional emotion recognition method can be effectively overcome, and the accurate recognition of the multi-element emotion of the user is realized.

Systems and methods to allow for generating a simulated humanoid. The simulated humanoid operates in a simulation space with simulated humanoid having a whole brain emulation module. The whole brain emulation module includes a virtual stimuli input module that is configured to receive or capture stimuli input data. The whole brain emulation module includes an encoder that is configured to translate the stimuli input data into a simulated functional neurodata frame. The whole brain emulation module also includes a brain state module that maintains a current brain state corresponding to a current functional neurodata frame of the simulated humanoid.

The invention relates to a brain anomaly network positioning method and system based on functional connection network mapping in the technical field of neural image data processing. The brain abnormal network positioning method comprises the following steps: calculating a whole brain function network diagram connected with each abnormal site by using resting state function connection data of large-scale health subjects based on a plurality of dispersed abnormal sites reported in previous literatures; then superposing the function network diagrams to obtain a network probability graph; and finally, filtering the probability graph through a threshold value to obtain a final core anomaly network graph. According to the method, the inherent functional connection architecture of the brain is used as a reference system, abnormal sites which seem to be uncorrelated in different researches are successfully traced and unified to a common and stable functional network, and compared with a single brain region marker, the generated network-level biomarker integrates more source evidences, so that the network-level biomarker has the advantages that the network-level biomarker can be widely applied to the field of biomarkers of the brain region. Therefore, the problems of result heterogeneity and inconsistency in brain abnormality discovery are solved.