242 results about "Resting state fMRI" patented technology

The invention relates to an uncertainty perception passive multi-target field adaptive image classification method, which is used for image recognition of autism spectrum disorder patients. According to the method, firstly, source domain model parameters are obtained and used for initializing a target model, then resting state functional magnetic resonance images of a plurality of imaging centers are preprocessed, and a plurality of target domains are constructed. On this basis, a current most representative target domain is selected through a minimum inter-domain difference strategy, an uncertainty modeling method based on evidence deep learning is adopted to train a target model, and class feature consistency is improved through domain contrast learning based on a class prototype in combination with a dynamically expanded auxiliary data set; and generating a pseudo tag to relieve the influence caused by tag noise. And finally, a trained target model is obtained through fine tuning optimization, and accurate classification of unknown images is realized. The method does not need to access source domain data, has the advantages of high robustness, high generalization ability and the like, and is suitable for actual cross-center medical image analysis scenes.

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 an intelligent cognitive assessment method and system based on a virtual reality environment. According to the method, through accurate eye movement calibration and electroencephalogram calibration, resting-state electroencephalogram information, digital cognitive evaluation task parameters and eyeball movement track parameters of a subject in a virtual reality environment are collected, and through professional preprocessing and feature extraction, deep analysis and fusion are conducted on data through a common-attention multi-mode feature noise reduction fusion model. Through training and prediction of the neural network model, the cognitive function state of the subject can be effectively evaluated, the accuracy and efficiency of cognitive evaluation are improved, and meanwhile, the user experience is optimized. By utilizing the method, early recognition of cognitive impairment is facilitated, a scientific basis is provided for clinical diagnosis and intervention, and the method is of great significance in preventing and delaying dementia of old people.

The invention relates to the technical field of intelligent diagnosis systems for neurodegenerative diseases, and discloses a system and equipment for identifying an early biomarker of Parkinson's disease based on non-invasive electroencephalogram. The Parkinson's disease early-stage biomarker identification system comprises a multi-mode signal acquisition module which is provided with a 64-lead dry electrode electroencephalogram cap, an integrated preamplifier and an ADC converter and is used for synchronously acquiring resting-state electroencephalogram and event-related potential, dynamically completing switching of the resting-state electroencephalogram sampling rate and transmitting acquired original data to a preprocessing module. According to the method, revolutionary improvement is realized in three dimensions of acquisition, analysis and calculation through system-level collaborative innovation, a nanocrystalline shielding dry electrode is combined with a dynamic impedance adjustment technology, so that the signal-to-noise ratio of a home environment is increased to 28dB, and the pathological correlation of key markers such as frontal lobe-basal node loop gamma entropy is verified by Granger causality and has a good application prospect. The cloud distributed model server supports 2000 paths of concurrent analysis, and the real-time bottleneck of home screening is thoroughly solved.

The invention discloses a concentration-fatigue cooperative regulation and control method and system based on electroencephalogram bimodal feedback, and the method comprises the steps: collecting first electroencephalogram data of a student in a resting state before training, and calculating a mean value of the ratio of a theta wave to an alpha wave as a fatigue resting state reference value; after training is started, second electroencephalogram data of the trainee are collected, a concentration index is calculated according to the ratio of the beta wave to the sum of the beta wave, the theta wave and the alpha wave, and a fatigue index is calculated according to the ratio of the theta wave to the alpha wave; calculating a dynamic threshold value according to the fatigue resting state reference value; and generating a corresponding risk prompt according to whether the concentration index and the fatigue index deviate from their thresholds. According to the method, the limitation of traditional single-dimensional biofeedback is broken through, the safety and efficiency of complex task training are remarkably improved, the limitation of the traditional single-dimensional biofeedback is broken through, and a quantitative neurocognitive enhancement scheme is provided for special operation training.

The invention discloses a transcranial magnetic stimulation positioning system based on a deep effect brain region, and the system comprises the steps: obtaining the data of a tested magnetic resonance image, which comprises a resting state functional image and a high-resolution T1 structure image; performing data preprocessing on the function image and the structure image, and establishing an individual space; the deep effect brain region template is converted to an individual space through nonlinear registration, and a target region of interest is defined; calculating functional connections between voxels of the deep effect brain region and the cortical region by adopting a Granger causality analysis method based on wavelet transformation; and screening Top block masses according to the functional connection strength, calculating the gravity centers of the Top block masses, and determining individualized stimulation targets in combination with the skull surface distance. According to the method, spontaneous brain activity function variation of the brain of an individual and the brain signal propagation direction are fully considered, accurate positioning of different individual levels is achieved, relatively stable cortex stimulation targets are obtained, and therefore the clinical curative effect of TMS is optimized.

The invention belongs to the technical field of deep learning and brain science, and particularly relates to an ADHD graph convolution model construction method based on fMRI spatial-temporal characteristics, and the method comprises the following steps: processing resting state functional magnetic resonance data; fMRI sequence processing: randomly cutting fMRI sequences from different sites to obtain fMRI sequences with consistent sequence lengths, and obtaining a functional connection matrix according to Pearson's correlation; model input data: the model input data is composed of an fMRI sequence, ADHD phenotype information and a functional connection matrix, and the input data is divided to obtain a training set, a verification set and a test set; constructing a model; and model training: model evaluation. According to the method, the gated feature fusion module is proposed to fuse the captured fMRI features to obtain the spatial-temporal features, the phenotypic information of the subject is considered, the ADHD graph convolution model based on the fMRI spatial-temporal features is constructed, and the classification accuracy of ADHD diseases is improved.

The invention discloses a disturbance of consciousness electroencephalogram discrimination method and system adaptive to channel deletion. According to the method, on the basis of an electroencephalogram classification model of a deep network, automatic discrimination of disturbance of consciousness (MCS and UWS) is carried out with high accuracy according to resting-state electroencephalogram energy of a patient. The model utilizes a time domain double-branch CNN module and a space-time Transform module to learn features in electroencephalogram data hierarchically; and a model architecture with variable channel dimensions is introduced, an inter-channel association learning method fusing electrode position information and a training strategy of channel random discarding are introduced, so that the model can adapt to a scene in which part of channels are missing, and the clinical applicability of the method is improved.

The invention discloses a myocardial perfusion image classification method and system based on a single resting state, and belongs to intelligent analysis and auxiliary diagnosis of medical images. The method comprises the following steps: acquiring SPECT three-dimensional voxel data in the single resting state, and performing image reconstruction by adopting an OSEM algorithm; segmenting the myocardial region by using a pre-trained U-Net convolutional neural network, and mapping a segmentation result to a two-dimensional polar coordinate graph conforming to the AHA17 segment model; extracting a multi-dimensional feature vector; a single-phase inference model MSR-Net based on biphase labeling is constructed, in the training stage, segment classification labels of resting-load biphase images are used, in combination with segment consistency indexes, label correction is carried out, and in the inference stage, only resting state features are input, and then a pixel-level perfusion defect distribution diagram and ischemia scores of 17 myocardial segments can be output. Quantitative and segmental evaluation of myocardial ischemia can be completed by using single resting state imaging, exercise or drug load examination is avoided, and cardiovascular adverse events and complication risks are reduced.

The invention discloses a multi-mode autism diagnosis method and system based on resting state fMRI and phenotypic text information, a medium and a product. FMRI and phenotypic text information are processed through a pre-trained heterogeneous graph neural network model to output a diagnosis result; the resting state fMRI is utilized to construct a brain function connection graph, and the phenotypic text information is coded into a two-dimensional vector; respectively extracting brain connection feature embedding of the fMRI mode and phenotype information feature embedding of the text mode; fusing the feature embedding in the two modes through a gating fusion network; and constructing a heterogeneous group diagram based on the fusion features and phenotypic information similarity, and obtaining group diagram features on the heterogeneous group diagram by using dual-channel information aggregation and adaptive feature fusion to output a diagnosis result. The invention aims to improve the accuracy of autism diagnosis by using multi-modal information, and can be applied to the fields of neuroimaging analysis, intelligent medical treatment and the like.

The invention provides a method for generating a personalized nerve regulation and control stimulation scheme, and aims to generate a precise nerve regulation and control stimulation scheme for an individual through an electroencephalogram evaluation result. The method comprises the following steps: firstly, constructing a scheme library containing a plurality of transcranial electrical stimulation basic schemes; secondly, collecting resting-state electroencephalogram signals of a user, extracting electroencephalogram characteristic indexes related to emotion, cognition and sleep, and comparing the electroencephalogram characteristic indexes with a norm database to judge cognition risks; generating a primary stimulation scheme according to an evaluation result, and if a cognitive risk exists, further detecting that a feature index is abnormal and generating a targeted correction scheme; all the schemes are subjected to priority ranking, and the sequence is determined according to the abnormal severity degree and the clinical weight; and finally, outputting a personalized treatment scheme sequence of one week. According to the method, the accuracy and effectiveness of treatment are improved, high individuation, systematicness, practicability and dynamic optimization potential are achieved, and powerful support is provided for nerve regulation and control treatment.

The invention discloses an emotion recognition method, device and equipment based on electroencephalogram signals and a medium, and relates to the technical field of electroencephalogram signal emotion recognition. The method comprises the steps that resting-state electroencephalogram signals and task-state electroencephalogram signals are preprocessed, and the preprocessed resting-state electroencephalogram signals and the preprocessed task-state electroencephalogram signals are obtained; performing feature extraction on the pre-processed resting-state electroencephalogram signals and the pre-processed task-state electroencephalogram signals to obtain resting-state electroencephalogram features and task-state electroencephalogram features; calculating a task state-resting state differential feature and / or a state transition differential feature between a resting state task and a task state task according to the resting state electroencephalogram feature and the task state electroencephalogram feature; and inputting the task state-resting state difference feature and / or the state transition difference feature after the generalized difference processing into a trained emotion recognition model to obtain an emotion recognition prediction result of the target user, thereby improving the emotion recognition precision.

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 discloses a dementia identification method based on brain computer network space cross attention fusion. The dementia identification method comprises the steps that resting-state electroencephalogram signals are acquired, preprocessing and brain network construction are carried out, the frequency band power ratio is calculated, and a training set and a test set are divided; the brain network fuses the spatial information, and spatial features are obtained through an isotropic graph neural network and a local feature enhancement module; the frequency band power ratio is coded by a multi-layer perceptron to obtain frequency spectrum statistical characteristics; a bidirectional cross attention module is input, and complementary fusion features are extracted; and performing mixed pooling and splicing on the complementary fusion features, then sending the fused features to a Chebyshev Kolmogorov-Arnold network classifier, and outputting an Alzheimer's disease / frontotemporal dementia / health control (AD / FTD / HC) classification result and a cognitive scale evaluation score (MMSE). According to the method, the multi-feature complementary information is effectively integrated through joint modeling of the spatial diagram features and the global spectrum features, and the accuracy, stability and generalization ability of AD and FTD classification in a complex brain network are improved.

The invention provides a brain function dynamic prediction device and equipment based on rTMS and a medium, and relates to the technical field of brain data processing.The device is used for obtaining multi-mode magnetic resonance imaging data and conducting preprocessing to generate a BOLD time sequence; carrying out Hilbert transform on the BOLD time sequence, and determining a phase coherence matrix of different brain regions at the same time point; performing weighted eigenvector dynamic analysis on the phase coherence matrix to generate a weighted eigenvector; performing clustering analysis on the weighted feature vectors to generate functional connection brain state clustering data; determining the similarity between the function connection brain state clustering data and the Yeo resting state network, and generating a function connection brain network state; state change data between functional connection brain network states is determined, and a state transition probability matrix is generated; and generating an rTMS prediction result according to the transition probability matrix and the brain index. The prediction efficiency can be improved.

The invention discloses a magnetic resonance method for evaluating the rehabilitation curative effect of child cerebral palsy, and relates to the technical field of medical image processing. The method comprises: obtaining structural image data (T1 weighted imaging and diffusion tensor imaging) and resting state image data of each child patient; and extracting brain and tissue boundaries from the T1 weighted imaging by using a 3D-Unet segmentation model. The resting state and structure image data are preprocessed and aligned to the brain boundary, and then the data are registered to a child standard brain template; based on a child standard brain template, obtaining and normalizing various clinical indexes, splicing the clinical indexes into multi-index vector data, inputting the multi-index vector data into a pre-trained support vector machine model, and outputting clinical index differences; and determining an abnormal brain region of each child according to the difference, finally providing a multi-modal fusion index analysis report, and observing changes of brain functions and white matter integrity before and after rehabilitation.

The invention discloses a mild cognitive impairment recognition method and system based on fNIRS data under multi-task normal form fusion. The method comprises the steps that fNIRS data, obtained based on a resting state task and an n-back work memory task, of a testee are obtained; based on the obtained fNIRS data, extracting fNIRS related features; performing feature selection on the extracted fNIRS related features based on a feature selection algorithm to form a feature set; and inputting the formed feature set into the trained deep learning model, and outputting a mild cognitive impairment recognition result. According to the method, the data of the fNIRS under the resting state and the n-back work memory cognition task are obtained, the multi-dimensional fNIRS related features are calculated, feature selection is carried out based on a Gradient Boosting algorithm, and finally accurate MCI recognition is carried out in combination with a deep learning model. The method not only can effectively extract high-dimensional features, but also can realize automatic MCI diagnosis through model training and reasoning, and has relatively high accuracy and relatively strong popularization and application potential.

The invention discloses a processing method, device and equipment for resting-state brain function magnetic resonance imaging, and relates to the technical field of computers. According to the method, the time sequence features of each brain region of the user are obtained based on historical resting state brain function magnetic resonance imaging of the user, and then brain map construction is performed according to the time sequence features of each brain region through two different brain map construction modes to obtain brain maps constructed at different angles; when learning is carried out on the basis, the mastery degree of features in resting-state brain function magnetic resonance imaging is improved; then, further diffusing the brain maps constructed at two different angles through a diffusion model, respectively performing feature extraction through two different encoders to obtain brain map features at different angles, and further applying the brain map features to map contrast learning represented by the brain maps, so that the feeling visual field of the brain map contrast learning method is expanded, and the brain map contrast learning efficiency is improved. And the extraction learning ability of the brain map comparison learning method on the intricate interaction between the brain areas is improved.

The invention relates to the technical field of brain anomaly detection and artificial intelligence auxiliary diagnosis, in particular to a graph convolutional network brain disease diagnosis method based on sub-graph sampling and multi-feature fusion, and aims to improve the accuracy of brain disease diagnosis. The method comprises the following steps: obtaining resting state functional magnetic resonance imaging data, preprocessing the data, and constructing a brain function connection diagram; and the brain function connection graph represents a brain interval collaborative activation relationship in a graph structure. Subgraph sampling is carried out based on function module division and node degree sorting, and an initial subgraph set is generated; and performing optimization selection on the initial sub-graph set by utilizing reinforcement learning to obtain an optimal sub-graph, introducing a node attention mechanism into the optimal sub-graph, screening key nodes based on attention scores, and generating a discriminant sub-graph. And extracting and fusing position features, neighborhood features and structural features of the discriminant subgraphs, and performing brain disease diagnosis based on the fused features.

The invention discloses an autism detection method based on multi-mode collaborative embedding, and belongs to the technical field of medical image analysis and artificial intelligence. The method comprises the following steps: firstly, obtaining resting state functional magnetic resonance imaging data and non-imaging data of a subject; a Markov transition field is utilized to encode the time sequence into an image so as to retain dynamic features, and feature extraction is carried out through an efficient multi-scale attention module; then realizing effective fusion and semantic alignment of multi-modal information by adopting a three-level fusion architecture and a joint loss function; and then adaptively constructing a graph structure based on the fusion features, dynamically learning a node relationship by using a graph attention network, and completing a classification decision. According to the method, the defects of a traditional method in the aspects of dynamic feature modeling, multi-modal fusion and heterogeneous graph structure processing are effectively overcome, the autism detection accuracy and robustness are remarkably improved, and a reliable tool is provided for clinical intelligent diagnosis.

The invention discloses a coupling tensor decomposition method based on sparse low-rank constraint, which is used for characteristic decomposition of a multi-frequency dynamic function network connection tensor in resting state function magnetic resonance imaging data. According to the algorithm, on the basis of the traditional coupling canonical factorization (CCPD), an optimization model of sparse and low-rank constraint is constructed, and the sparse and low-rank constraint optimization model is constructed by the algorithm. On the spatial connectivity dimension, redundant function connection is reduced through an L1 sparse penalty term, and the spatial specificity of the key brain network is enhanced; and in time and frequency band dimensions, low-rank regularization constraint is adopted to improve discrimination of cross-subject time sequence characteristics. Generally speaking, the method can effectively extract connectivity characteristics with statistical significance and time states of different frequency bands from dynamic function network connection tensors of multiple frequency bands, thereby effectively identifying functional connection heterogeneity characteristics between schizophrenia patients and healthy control groups.

The present invention relates to the field of image processing technology, and in particular to a method for image diagnosis and positioning of a supplementary motor area based on a resting state, comprising: acquiring a user's brain magnetic resonance image based on an MRI scanner, creating a database, and storing the acquired user's brain magnetic resonance image; acquiring the user's brain magnetic resonance image, and removing high-frequency physiological noise and low-frequency drift in the user's brain magnetic resonance image; acquiring the supplementary motor area, and performing low-frequency pulsation amplitude analysis, local consistency analysis, and functional connection abnormality analysis; the present invention utilizes brain maps for preliminary positioning and functional connection mutual verification, can more accurately determine the position of the supplementary motor area, reduce positioning errors, and evaluate the state of the supplementary motor area from two aspects of local brain activity and functional connection abnormality analysis, and based on this method, reduces the interference of subjective factors, realizes the quantification of diagnostic indicators, and makes the results more objective and reliable.

The invention discloses an individualized precise brain regulation and control method and device based on dynamic brain network traceability, and relates to the technical field of electroencephalogram regulation and control. The method comprises the steps that resting state electroencephalogram signals of each lead of two resting state tasks are obtained; performing traceability analysis on the resting-state electroencephalogram signals of each lead of the two resting-state tasks, and calculating weighted phase lag indexes of lead pairs according to the traced resting-state data of the two leads in the pairwise lead pairs; constructing dynamic brain networks according to the weighted phase lag indexes of all lead pairs, performing modeling according to the first dynamic brain network and the second dynamic brain network, calculating an abnormal index of each brain region node, performing target stimulation based on the abnormal index of each brain region node by using a multi-channel stimulation controller, and performing target stimulation based on the abnormal index of each brain region node. According to the invention, the problems of inaccurate target spot positioning and single target spot in the existing cranial nerve regulation and control technology are solved.

The invention discloses a calculation method for integrating task induction and intrinsic spontaneous brain function activity. The calculation method comprises the following steps: calculating a brain activation mode when an individual executes a corresponding cognitive task based on task state functional magnetic resonance imaging data and a general linear model; identifying individual large-scale nerve avalanche with spatial continuity based on resting state functional magnetic resonance imaging data; the method comprises the following steps: performing principal component analysis on resting state functional magnetic resonance data of an individual to construct a low-dimensional state space; a task-induced brain activation mode and intrinsic spontaneous nerve avalanche are projected to an individual low-dimensional state space; calculating the Euclidean distance between the task-induced brain activity and the intrinsic spontaneous nerve avalanche in the low-dimensional state space; and detecting the prediction effect of the geometric distance on the performance of the tested task through the regression model. The method is verified on a real data set, and experimental results show that the method not only can integrate two basic brain function activities, but also can significantly predict individual cognitive performance differences.

The invention discloses a method for determining an OAB target based on brain network characteristics. The method comprises the following steps: a) detecting the functional connection strength of the right forehead cortex of a patient in a resting state through functional magnetic resonance imaging; b) identifying significantly weakened brain region connections, including paracentral lobules, cerebellar lower feet and marginal systems, as compared to a healthy control group; c) determining the number of white matter fiber bundles with abnormal structural connection in the brain interval by combining a diffusion tensor imaging fiber tracking technology; and d) selecting a brain region with abnormal function and structure connection as a nerve regulation treatment target.

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 relates to the technical field of brain magnetic signal denoising, and provides a brain magnetic background noise suppression method and device based on multi-scale frequency domain subspace projection filtering. The method comprises the following steps: firstly, converting a multi-channel resting-state noise signal and a multi-channel brain magnetic signal into a time-frequency domain through wavelet packet transformation, and then decomposing data after wavelet packet transformation into different frequency bands so as to separate noise components more clearly; carrying out singular value decomposition on the sub-band coefficient matrix, and adaptively selecting a threshold value by combining an energy accumulation method and a second-order difference method so as to eliminate noise related components; and finally, denoising is performed on each frequency band by using a common subspace projection method, and then the denoised data is reconstructed to obtain the denoised brain magnetic signals, so that the method has a good noise suppression effect, and high-quality clean data can be provided for subsequent brain magnetic signal analysis.

Disclosed is a method for determining a treatment to be applied to a brain of a patient, such as determining a transcranial magnetic stimulation (TMS) treatment location(s) and treatment strength to be applied to a brain of a patient suffering from depression. A search of the brain, based on coarse-scale parcellations, treatment metric and one or more anatomical metrics, is conducted to select the treatment location(s). In one embodiment, this involves progressively varying a value of a parameter associated with a gyrus threshold and for each value, identifying candidate treatment locations based on a functional connectivity of a subgenual anterior cingulate cortex during resting-state functional magnetic resonance imaging (fMRI) recorded over a plurality of multimodal brain imaging sessions, and selecting as the respective treatment location the candidate location that is closest to all other candidate treatment locations.

The invention relates to an intelligent depression detection system based on physiological behavior multi-modal information. The system comprises an Android application program and a wearable device capable of collecting electrocardio, electroencephalogram and PPG signals (photoplethysmography), and voice and video signals are collected through a camera and a microphone. The system is provided with a user information input module, a sensor detection module, a resting state signal acquisition module, a picture video emotion induction experiment module, a multi-mode signal fusion analysis module, an evaluation report generation module and other functional modules. The system combines resting state signals and induced state experiment data, through intelligent analysis and fusion of physiological and behavioral two-dimensional signals, depression identification and detection can be carried out based on comprehensive characteristics of the five signals, the accuracy and reliability of depression detection are effectively improved, and therefore the psychological state of a user is evaluated more comprehensively. The system has the characteristics of noninvasiveness, convenience, real-time acquisition, multi-mode fusion and the like, and can provide a powerful auxiliary tool for clinical depression diagnosis.

The invention discloses a depressive emotion early screening and intervention system based on cross-modal heterogeneous information. The depressive emotion early screening and intervention system comprises a tester information registration module, a sensor test template, a resting state data acquisition module, a task state data acquisition module, a depressive emotion evaluation module, a digital intervention module, a report display module and a terminal control module. According to the method, scale, physiological data and behavioral signals are combined, and multiple scientific evaluation paradigms are constructed to perform multi-dimensional, objective and effective evaluation on the depressive emotion; the system further provides a personalized digital intervention scheme through an evaluation result, and the depressive emotion is preliminarily relieved. According to the invention, early screening and intervention of the depression emotion state can be realized, the detection efficiency is improved, and the method can be applied to various scenes such as schools, hospitals and families.