179 results about "Entire brain" patented technology

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.

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 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 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.

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 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.

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.

This invention belongs to the technical field of monitoring methods, specifically disclosing a method for clinical monitoring of craniocerebral diseases based on electromagnetic field biodetection technology. The method includes preparing four electrode pads, which are respectively fixed to the left and right sides of the patient's brain, the forehead, and the occipital region. The electrode pads are then connected to a monitor. Based on the detection data from the four electrode pads, the monitor obtains detection parameters at each time point: R1 represents the perturbation coefficient generated by the left and forehead electrode pads, R2 represents the perturbation coefficient generated by the right and forehead electrode pads, R3 represents the perturbation coefficient generated by the left and occipital region electrode pads, R4 represents the perturbation coefficient generated by the right and left sides electrode pads and the occipital region electrode pads, and R5 represents the perturbation coefficient generated by the left and right sides electrode pads or the perturbation coefficient of the entire brain. The method assesses the degree of edema, the presence of hemorrhage, or fluid accumulation. Using four electrodes positioned at different locations in the brain allows for targeted, three-dimensional, multi-parameter detection of lesions in different parts of the brain.

The invention relates to the technical field of brain disease target positioning, in particular to a brain disease target positioning system and method based on functional magnetic resonance imaging. The method comprises the following steps: calculating a whole brain function connection map of an individual patient based on 4D rs-fMRI data, calculating a Z score of each voxel function connection strength in the whole brain function connection map of the individual patient, and screening abnormal voxels based on a preset threshold value; aggregating the abnormal voxels through a clustering algorithm so as to determine a cluster; the therapeutic effect scores of the multiple clusters can be predicted through the machine learning model, the clusters and the candidate therapeutic targets can be screened based on the therapeutic effect scores, the positions of the candidate targets can be accurately determined, and multiple selections of the candidate targets can be provided; the target spot is converted into an accurate target spot suitable for an individual through a nonlinear registration algorithm; and determining a target positioning state according to the deviation between the accurate target and the actual treatment target, and adjusting corresponding parameters based on the target positioning state. The invention provides a plurality of targets and effectively determines the positions of the targets.

Embodiments of the present application provide a whole brain and whole spinal cord automatic delineation method and device, electronic equipment and medium, the method comprises: acquiring a CT image, segmenting the CT image according to a first adaptive threshold to obtain a human body contour region of interest, segmenting the human body contour region of interest according to a second adaptive threshold to obtain a human body skeleton contour region of interest, performing inflation operation and corrosion operation on the human body skeleton contour region of interest to obtain a brain tissue region of interest, performing inflation translation superposition subtraction processing and corrosion operation on the human body skeleton contour region of interest to obtain a spinal cord target contour, and combining the brain tissue region of interest and the spinal cord target contour to obtain a whole brain and whole spinal cord region of interest. By using the method, the whole brain and whole spinal cord region of interest can be automatically, quickly and accurately delineated, which greatly helps image analysis, accurate treatment and reduction of the workload of clinicians for patients.

The application discloses a new use of Leonurine or a pharmaceutically acceptable salt thereof in the preparation of a medicament for treating radiation-induced brain injury, specifically, providing a safe and effective therapeutic drug for iron death, neuroinflammation, neuron damage and lipid metabolism disorder accompanying the radiation-induced brain injury, solving the technical problem of lack of specific treatment means in the prior art, and having a wide clinical application prospect. Experiments adopt a C57BL / c mouse 30 Gy whole brain X-ray radiation model, and results show that Leonurine can significantly improve the body weight decrease and survival rate of the model mice, reduce the serum IL-6 and IFN-alpha levels, inhibit the activation of microglial cells in the cerebral cortex and hippocampus, protect and repair damaged neurons, and improve the ultrastructure of mitochondria and synapses; meanwhile, the expression of GPX4 and SLC7A11 proteins in the brain tissue is up-regulated, the GSH content is increased to inhibit iron death, and the levels of GPE, GPS and GPC related to lipid metabolism are restored.

The application discloses a brain age estimation method based on adversarial learning, extracts corresponding image features specific to age based on whole brain structural T1 magnetic resonance imaging, and realizes prediction of brain age, and comprises the following steps: step one: data collection and pretreatment, more than 2000 cases of data from 5 sites are collected, the age span is 5-94, the collected data is pretreated, all brain image data is registered to MNI standard space, and the size is standardized; step two: based on the adversarial learning deep neural network, the network is trained and optimized, and prediction of brain age is realized; step three: performance evaluation of brain age prediction task, optimization of prediction results, model feature extraction capability and generalization capability evaluation. Compared with the prior art, the application reduces the phenomenon that the model training result is unstable and the prediction effect is not ideal due to uneven age distribution of samples.

The present application relates to medical image analysis and brain network research technical field, specifically to glioma induced brain network remodeling visualization and quantitative analysis method based on fMRI. The method comprises obtaining patient fMRI and structural MRI data and preprocessing, excluding tumor area by lesion mask registration strategy, reducing quality effect interference; dividing tumor core area, peritumoral abnormal area and normal brain area; registering Yeo-17 network template to individual brain area to realize mapping; defining tumor core area as independent network unit, and 17 normal networks to form a new set; calculating whole brain voxel and network functional connection strength, and determining functional connection voxel according to threshold; quantifying intratumoral function proportion RIFR and peritumoral connection proportion RPTR, and generating visualization atlas. The present application accurately maps individual brain function network, overcomes tumor heterogeneity interference, provides repeatable quantitative index, and provides reliable imaging analysis tool for brain glioma function protection and clinical research.

The present invention relates to a forebrain assembloid that exhibits a maturity similar to that of the human brain, and a schizophrenia biomarker identified using the forebrain assembloid. More specifically, the present invention relates to: a method for preparing a forebrain assembloid; a forebrain assembloid having a single rosette structure that includes six cortical layers and cavities, the cortical layers including glial cells; a composition for diagnosing schizophrenia, comprising an agent for measuring the expression level of UCN, an agent for measuring the expression level of PTPRF, an agent for measuring the expression level of WNT11, and an agent for measuring the expression level of THBS4; and a kit comprising the composition for diagnosing schizophrenia.

The invention discloses a whole brain positioning and health state evaluation method and system based on an adaptive attention mechanism, electronic equipment and a computer readable storage medium. The whole-brain positioning and health state evaluation method based on the adaptive attention mechanism comprises the following steps: collecting whole-brain MRI data, and marking the structure of each part of the region of the whole brain; performing data preprocessing on the whole brain MRI data; on the basis of the whole-brain MRI data after data preprocessing, detecting each partial region of the whole brain by using a whole-brain positioning network based on an adaptive weight and residual error reconstruction mechanism; and according to the regional form of each part of the whole brain in the detection result, performing comprehensive health form evaluation. The method can solve the problems that the edge detection precision of each region of the whole brain is low and each partial region of the whole brain is difficult to accurately distinguish at present; the generalization ability is improved; the health state is accurately evaluated; manual intervention is reduced, one-key rapid whole-brain positioning and accurate health state evaluation are achieved, and simplicity and high efficiency are achieved.

The invention discloses a non-degreasing biological tissue dyeing method and device. The dyeing method of the non-degreasing biological tissue comprises the following steps: a sample fixing step: fixing a biological tissue sample by adopting paraformaldehyde and / or glutaraldehyde; and a dyeing step: carrying out electrophoresis dyeing on the fixed biological tissue sample which is not degreased by using a nucleic acid dye and a beta-amyloid protein small molecule dye. According to the non-degreasing biological tissue dyeing method disclosed by the invention, the non-degreasing biological tissue is directly dyed, so that the damage of degreasing operation to a sample is avoided; moreover, the nucleic acid dye is combined with the beta-amyloid protein small molecule dye, so that the whole brain sample can be quickly and efficiently dyed, and high-resolution fluorescence labeling and three-dimensional space distribution imaging of beta-amyloid protein plaques in the whole brain range can be realized; the method is of great significance in revealing the spatial distribution pattern of beta-amyloid protein and explaining the pathogenesis of Alzheimer's disease.

The present invention belongs to the field of brain-computer interface technology application technology, and discloses a brain-computer information fusion classification method and system for shared subspace learning, wherein the brain-computer information fusion classification method includes a training phase and an inference phase; wherein the training phase utilizes paired images and brain response data, optimizes the shared subspace model parameters of images and brain responses through a comparative learning strategy of positive and negative sample sampling, and trains an image classifier; the inference phase extracts image features for classification, and achieves the application goal of the entire brain-computer information fusion classification system. The brain-computer information fusion classification system for shared subspace learning of the present invention can train shared subspaces end-to-end, achieve efficient transfer of brain cognitive information, and improve the performance of image classification tasks in complex open scenarios; through the application of "brain out of the loop", it improves efficiency and stability in real-world applications, and has broad application prospects under the new paradigm of brain-computer information collaboration.

The invention provides a method and device for generating information based on brain image data, and the method comprises the steps: carrying out the longitudinal brain image analysis of the brain image data, collected at multiple time points, of a target individual, carrying out regional division on the brain of the target individual at each time point and extracting morphological measurement values of each region based on a longitudinal brain image analysis result and a cortex partition map; based on the brain form norm, the deviation degree of the morphological measurement value of each region of the brain of the target individual at each time point relative to the brain form norm is determined, and the individual brain form deviation vector corresponding to the target individual at each time point is obtained; performing correlation calculation on the individual brain form deviation vector corresponding to the target individual at each time point and the statistical data of the plurality of mental diseases in the whole brain range to obtain cross-disease brain form similarity features; and inputting the cross-disease brain form similarity features into a pre-trained machine learning model, and outputting classification information about whether the target individual is a schizophrenia patient or not, and / or prediction information about clinical indexes.

The application relates to the field of intelligent medical treatment, in particular to a double-encoder comparison method, equipment and program product based on whole-brain voxel levels. The method comprises the following steps: acquiring a three-dimensional whole-brain image dataset; constructing a double-encoder comparison decoding model by using the three-dimensional whole-brain image dataset; the construction process of the double-encoder comparison decoding model comprises the following steps: grouping normal or diseased three-dimensional whole-brain images, selecting an arbitrary normal three-dimensional whole-brain image as a reference image, inputting the reference image and other normal or diseased three-dimensional whole-brain images into a to-be-trained encoder model in parallel to obtain output features, comparing the output features to obtain a whole-brain difference classification result; repeating the steps of updating the to-be-trained encoder model and comparison calculation according to the reference image and other normal or diseased three-dimensional whole-brain images until a preset stop condition is reached, and obtaining a double-encoder comparison decoding model. The application has good clinical application value.

The present invention discloses a method and apparatus for constructing a brain-computer interface system for neurofeedback training, relating to the field of neurofeedback training. The method comprises: designing a T-shaped channel layout for a near-infrared brain imaging device, including: employing a T-shaped arrangement of paired light sources and receivers on a plane, wherein adjacent light sources and receivers are combined to form data acquisition channels, and the multiple data acquisition channels thus formed can be mapped to the entire brain; configuring a BCI communication module, a bandpass filtering module, a baseline calculation and adaptation module, a feedback mode selection and calculation module, and an interactive display module within a brain-computer interface computer connected to the near-infrared brain imaging device. The present invention can determine the regulatory effects of resting-state brain states during various task feedback training sessions.

The invention relates to the technical field of neurological rehabilitation, in particular to a transcranial direct current closed-loop regulation and control method based on near-infrared brain oxygen real-time feedback, which comprises the following steps: collecting brain oxygen baseline signals of each region of the whole brain, calculating the mean value and the standard deviation of each brain region, and establishing a whole brain blood oxygen regulation and control threshold matrix of sub-regions and sub-parameters; dynamically adjusting the core target brain region, and adjusting the sampling frequency and the single detection duration according to the fluctuation frequency of the oxyhemoglobin concentration of the core target brain region; according to the signal fluctuation frequency, continuously adapting the detection parameters, and combining the comparison result of the core characteristic parameters and the whole brain blood oxygen regulation threshold matrix, optimizing the transcranial direct current regulation parameters, and outputting optimized data; by means of the mode, dynamic adaptation of the detection parameters and the signal fluctuation characteristics is carried out, and regulation and control collaboration is improved.

The application discloses a kind of whole brain segmentation method, system, equipment and medium, method includes obtaining CT image data, and standard image data is obtained by preprocessing;Standard image data is input into whole brain segmentation model, and whole brain segmentation result is obtained.Wherein, whole brain segmentation model is obtained based on sample annotation data training, and whole brain segmentation model includes CNN encoder, Transformer encoder, multiple cross-domain fusion modules and the feature enhancement module corresponding to cross-domain fusion module one by one, and decoder.The application has the advantages that the soft tissue contrast of CT image data is low, it is difficult to distinguish cerebral grey matter, cerebral white matter, cerebrospinal fluid and brainstem brain tissue problem, by CNN encoder extraction local feature and Transformer encoder extraction global semantic feature, and local feature and global semantic feature dynamic interaction and fusion, accurately segmented advantage is carried out to whole brain cerebral grey matter, cerebral white matter, cerebrospinal fluid and brainstem brain tissue.