196 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 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 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 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 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 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 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 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 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.

The invention discloses a method for controlling an unmanned aerial vehicle through a brain-computer interface based on signal fusion, a medium and equipment. According to the method, electroencephalogram signals of a user are collected in real time, power spectrum density analysis is carried out, a dynamic attention score is calculated, and concentration is divided into three levels of a resting state, a preparation state and a concentration state; meanwhile, head inertial attitude parameters and masseter surface electromyogram signals are collected. And generating a take-off instruction when the concentration state continuously reaches a preset duration, generating a direction control command based on weighted fusion of the electroencephalogram signal and the attitude parameter, and generating a landing instruction when an occlusion action is detected and the electroencephalogram power is lower than a resting state reference value. According to the invention, full-process natural control of the unmanned aerial vehicle from takeoff, flight to landing is realized through multi-mode signal fusion, and the control precision and reliability are improved.

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.

The invention relates to the technical field of medical health, in particular to an epilepsy auxiliary evaluation method and device based on heart rate variability, equipment and a medium. The method comprises the following steps: acquiring at least two target physiological states in a current acquisition combination, wherein the target physiological states comprise a resting state and one or two stimulation states; based on the target physiological state, electrocardiosignals of the subject are collected; based on a preset signal processing algorithm, heart rate variability analysis is conducted on each group of electrocardiosignals, a corresponding resting state HRV feature set and at least one stimulation state HRV feature set are obtained, and each HRV feature set comprises a plurality of feature items; determining a group of target feature items suitable for the current collection combination according to the independent judgment efficiency, the correlation judgment efficiency and / or the global judgment efficiency of each feature item on epilepsy; and generating a screening report for assisting epilepsy risk assessment based on the target feature item. And the operation efficiency is improved while the evaluation accuracy is guaranteed.

The invention discloses a brain function training device and method, and belongs to the field of brain function training.The method comprises the steps that before training is started, the historical average value of resting state numerical values of target brain function indexes of a user and the current resting state numerical value are determined; determining a dynamic offset according to a difference value between the current resting state numerical value and a historical average value; adjusting and updating the reference threshold sequence through the dynamic offset to obtain a target threshold sequence; after training is started, a target stage corresponding to a real-time interval (the interval where a real-time numerical value of a target brain function index is located in a target threshold sequence) in the multi-stage feedback content can be determined; feedback content of the target stage is fed back to the user; on one hand, the target threshold sequence can be dynamically determined by referring to the current and historical resting state numerical values (of the target brain function index) of the user, the training effect is improved, on the other hand, the interestingness of brain function training is improved through the multi-stage feedback content, and the training enthusiasm of the user is improved.

The application discloses a closed-loop dynamic modeling system for heart, blood vessels and brain, and relates to the field of physiological system modeling.The system comprises a signal processing module, a closed-loop dynamic modeling module, a personalized training module and a state analysis module; the signal processing module extracts a feature sequence from multi-modal physiological signals; the closed-loop dynamic modeling module constructs a dynamic skeleton with physical differential equations, and embeds a neural network subject to physical constraints in nonlinear regulation nodes of brain central control, autonomic nervous regulation, heart blood pumping and blood vessel transmission to generate a dynamic numerical trajectory of system state variables; the personalized training module firstly fixes the neural network on resting state data to determine physical model parameters, and then jointly optimizes the neural network and the physical model parameters based on data fitting terms and physiological constraint terms on load task data; and the state analysis module calculates brain-heart sympathetic drive gain, pressure reflex sensitivity and blood vessel hardening index to determine dominant factors of cardiovascular regulation function.

This invention discloses a novel intelligent rapid screening device for depression / Alzheimer's disease, used for early auxiliary detection of either condition. It comprises: an EEG acquisition module, a human-computer interaction module, a wireless transmission module, and an intelligent analysis module. The EEG acquisition module collects resting-state and task-oriented EEG signals from the subject. The human-computer interaction module selects the screening mode, inputs subject information, performs impedance detection, signal verification, and provides task guidance. The wireless transmission module enables data transmission between the modules. The intelligent analysis module receives the EEG signals, analyzes the data using a machine learning model, and outputs the screening results. This device uses dry electrode technology, eliminating the need for conductive adhesive. The brain-computer interface is made of skin-friendly material for comfortable wear. Wireless transmission and cloud analysis provide high flexibility. AI-based data analysis and pattern recognition improve diagnostic efficiency and accuracy.

The invention relates to a brain induced activity-based electroencephalogram accurate decoding method and a brain-computer interface system. The brain induced activity-based electroencephalogram accurate decoding method comprises an electroencephalogram acquisition module for acquiring original electroencephalogram signals in a resting state and a motion state; an electroencephalogram preprocessing module; the brain dynamics model takes the pure resting state EEG signals and the stimulation state EEG signals processed by the electroencephalogram preprocessing module as input, the internal state of brain spontaneous activity and the internal state of brain spontaneous activity and induced activity mixture are obtained respectively, and then the internal state of brain induced activity is obtained; the internal state-EEG conversion network takes the pure signal processed by the electroencephalogram preprocessing module as input, and obtains a reconstructed EEG signal by using a coding and decoding network; the training module is used for optimizing model parameters and internal state-EEG conversion network parameters at the same time; and the decoding module is used for decoding the motion behavior according to the internal state of the brain induced activity output by the brain dynamics model. The accuracy of model parameter and internal state estimation is improved, and interference generated by spontaneous activity is eliminated.

The embodiment of the invention provides an electroencephalogram emotion recognition method based on a graph neural network and federal learning. The method is applied to the technical field of artificial intelligence. The method comprises the following steps: acquiring a resting state functional magnetic resonance imaging time sequence and non-image personalized data; preprocessing the resting state functional magnetic resonance imaging time sequence, and constructing a dynamic graph sequence for the preprocessed resting state functional magnetic resonance imaging time sequence based on a plurality of preset brain maps by adopting a sliding window technology; inputting the dynamic graph sequence into a shared feature layer for feature extraction to obtain a space-time shared feature vector; inputting the non-image personalized data into an independent personalized layer for feature extraction to obtain a personalized feature vector; performing feature fusion processing on the space-time sharing feature vector and the personalized feature vector to obtain a fused feature; and the fused features are mapped into the electroencephalogram emotion category probability through the classifier, an electroencephalogram emotion recognition result is obtained, and the electroencephalogram emotion recognition accuracy and the generalization ability of the model are improved.

This invention relates to a precise EEG decoding method and brain-computer interface system based on evoked brain activity, comprising: an EEG acquisition module for acquiring raw EEG signals in resting and motor states; an EEG preprocessing module; a brain dynamics model that, using the purified resting-state and stimulated-state EEG signals processed by the EEG preprocessing module as input, obtains the internal states of spontaneous brain activity and the mixed internal states of spontaneous and evoked brain activity, thereby obtaining the internal states of evoked brain activity; an internal state-EEG conversion network that, using the purified signals processed by the EEG preprocessing module as input, obtains reconstructed EEG signals using an encoding-decoding network; a training module that simultaneously optimizes the model parameters and the internal state-EEG conversion network parameters; and a decoding module that decodes motor behavior based on the internal states of evoked brain activity output by the brain dynamics model. This improves the accuracy of model parameters and internal state estimation and eliminates interference from spontaneous activity.

The invention provides a lower limb rehabilitation evaluation method, system and device based on a brain-computer interface and a medium, and relates to the technical field of rehabilitation medicine and medical electronics. According to a lower limb rehabilitation evaluation technology based on a brain-computer interface, a two-way evaluation system of peripheral muscle form-central nervous activity is constructed through synchronous acquisition of wearable A-type ultrasound and electroencephalogram signals; a-type ultrasound monitors the change of the thickness of the rectus femoris in real time, and electroencephalogram signals analyze the coherence of Alpha / Beta frequency bands, so that functional difference evaluation of a stroke patient in a resting state, a passive motion state and an active motion state with different resistances is realized, the limitation of single subjective scoring of a traditional scale is avoided, and a multi-dimensional quantitative index is provided for rehabilitation evaluation.

The invention provides an EEG (electroencephalogram)-based self-sensitivity quantitative evaluation method, and belongs to the field of physiological signal processing and mode recognition. The method comprises the following steps: collecting resting state and task state electroencephalogram signals of a subject, and obtaining a self-efficacy feeling score of the subject in combination with a scale; the electroencephalogram signals are preprocessed, window segmentation is carried out on the preprocessed signals, and multi-dimensional features such as a time domain, a frequency domain, a power spectrum entropy and a micro state are extracted; and inputting the features and the performance feeling score into a deep learning model based on LSTM-Attention for training, and constructing an evaluation model. The model can output a quantitative score according to the electroencephalogram signal, and the evaluation result continuously iterates and optimizes the model through continuous feedback data, so that the evaluation precision is improved. According to the method, objective and accurate quantification of self-effectiveness is achieved by combining the electroencephalogram signal features and the deep learning technology, the internal association between the self-effectiveness and electroencephalogram activity is revealed, and high practicability is achieved.

Disclosed are a disease prediction method, system and apparatus based on a multi-relation functional connectivity matrix. A Pearson correlation coefficient matrix and a DTW distance matrix are respectively calculated according to resting state functional magnetic resonance time series extracted from a brain atlas, the DTW distance matrix is converted in combination with the Pearson correlation coefficient matrix into a DTW′ matrix which includes correlation degree and correlation direction information and whose numerical range is equivalent to the value range of a Pearson coefficient, and a functional connectivity matrix is obtained after weighted combination. The present disclosure combines DTW distance information to weaken the dynamic change of functional connectivity and the influence of asynchrony of functional signals in different brain regions on the functional connectivity matrix, so that the calculated functional connectivity matrix can better reflect the correlation between the functional signals in different brain regions.