989 results about "Electro encephalogram" patented technology

The invention relates to the technical field of sleep aiding, in particular to a method and a system for extracting feature information of a sleep state monitoring model. The method comprises the following steps: carrying out multi-mode physiological signal acquisition on a detected object to obtain original sleep monitoring data; performing noise removal and signal enhancement processing on the original sleep monitoring data, and performing signal segmentation to obtain multi-channel sleep signal fragment data; performing feature extraction based on electroencephalogram, eye movement diagram, electromyogram, electrocardiogram and accelerometer signals on the multi-channel sleep signal fragment data to obtain multi-dimensional feature data; and performing ultrasonic feature and acoustic feature extraction on the multichannel sleep signal fragment data to generate acoustic feature data. According to the method, dynamic modeling and trend prediction are carried out on the conversion process of the sleep state, potential sleep disorder events can be accurately recognized, influence evaluation is carried out on the potential sleep disorder events through deep learning, and support is provided for personalized sleep diagnosis.

The present invention discloses a dyskinesia rehabilitation training method and system based on electroencephalogram signal recognition; the method includes: the convolutional neural network is used to recognize the electroencephalogram signal, analyze the patient's motor intention, convert the motor intention into a control instruction for the exoskeleton device, drive the patient's limb movement by controlling the movement of the exoskeleton device, and assist the patient to complete the movement disorder rehabilitation training. In this method, the self-attention mechanism and self-distillation training were used to establish an electroencephalogram signal recognition model, analyzing the patient's motor intention to help motor nerve remodeling, which broke the passive and single problem of traditional rehabilitation methods, realized active rehabilitation of patients, and significantly improved the rehabilitation efficacy.

The invention provides an electromagnetic photoacoustic synchronous integrated brain function imaging method and device, and relates to the field of multi-mode brain function imaging research. According to the invention, electroencephalogram, magnetoencephalogram, functional near infrared spectrum and functional ultrasonic imaging technologies are fused, important advantage complementation is formed in signal specificity and temporal-spatial resolution, and neural activity can be described from the angle of human cranial nerve electrophysiology and neural metabolism can be observed from hemodynamic information at the same time. And electrophysiology and hemodynamic activities of the brain can be synchronously recorded.

The invention relates to the technical field of brain-computer interfaces, in particular to an electroencephalogram signal amplification method, device and equipment based on topological gradient field coding. The method comprises the following steps: acquiring a first data set corresponding to a scalp electroencephalogram of a testee through electroencephalogram acquisition equipment, and performing Hilbert transform to extract phase information so as to calculate a topological gradient field; key phase features meeting the energy concentration ratio feature and the duration feature are extracted from the topological gradient field, so that the key phase features with the energy concentration ratio threshold value larger than a preset value and the duration exceeding a time threshold value are screened out; inputting the key phase features to a pre-constructed generative adversarial network, and generating an enhanced phase field by a generator of the generative adversarial network through adversarial training; and combining the enhanced phase field with the low-frequency amplitude component, subjected to wavelet decomposition, of the first data set to construct a complex field, simulating spatio-temporal evolution of the complex field based on a nonlinear dynamic model, and generating a finally enhanced and amplified electroencephalogram signal. And efficient characterization of electroencephalogram phase information is realized.

The invention discloses an emotion recognition method based on a space-time multi-scale attention convolutional neural network, and the method comprises the steps: collecting electroencephalogram data of a subject, and carrying out the preprocessing of the electroencephalogram data, so as to obtain electroencephalogram (EEG) data containing a space dimension and a time dimension; constructing a lightweight convolutional neural network comprising a double-flow spatial-temporal feature construction layer, a mixed attention mechanism layer, a high-order fusion layer and a classification layer; wherein the double-flow spatial-temporal feature construction layer comprises a temporal feature extraction module and a parallel spatial feature extraction module; the mixed attention mechanism layer combines a channel attention mechanism, a space attention mechanism and a self-attention mechanism; the high-order fusion layer is used for carrying out re-learning from the learned global convolution kernel to the representation of the local hemisphere convolution kernel; and recognizing the EEG data by using the trained lightweight convolutional neural network to obtain an emotion recognition result of the subject. The precision and efficiency of emotion recognition driven by electroencephalogram signals are improved by constructing a lightweight model with a small number of parameters.

The invention provides an anesthesia depth monitoring method, a medium and an electronic device, and belongs to the technical field of anesthesia depth monitoring, and the anesthesia depth monitoring method comprises the following steps: collecting an electroencephalogram signal, an electrocardiogram signal and a respiratory activity signal of a patient, and carrying out preprocessing, grouping and feature extraction to realize anesthesia depth monitoring. The method comprises the following steps: firstly, segmenting a signal, calculating the similarity, and classifying subsequences into stable and fluctuating subsequences; and then, determining a step length according to the alternative interval, processing stable and fluctuating subsequences, and meanwhile, filtering, reducing noise and recording an abnormal waveform segment. Then, the signals and the abnormal waveform segments are processed in parallel on the GPU and the CPU, and features are extracted; and finally, through clustering and dimension reduction optimization features and in combination with abnormal waveform segment features, obtaining final anesthesia depth features, and inputting the anesthesia depth features into a monitoring model to obtain a monitoring result. The technical problem that the anesthesia depth monitoring result is not accurate enough due to the fact that interference or small fluctuation is often difficult to process in an existing anesthesia depth monitoring technology is solved.

The invention discloses an emotion recognition method based on electroencephalogram (EEG) and expression data, which combines fusion analysis of electroencephalogram (EEG) signals and facial expression data, and provides an innovative solution for the complexity and accuracy of emotion recognition. The method comprises the following specific implementation steps: 1, finding an optimal frame in an expression video by using multi-instance learning, extracting global visual features by adopting Swin Transform, and realizing dimension reduction and balance representation of cross-modal information through a cross attention mechanism; 2, preprocessing the electroencephalogram signal by using band-pass filtering and a fast ICA algorithm, and extracting effective features by using PSD; and 3, designing a fusion module based on Transform, combining the characteristics of the two modes, and giving full play to the complementary advantage of the modes. Experimental results show that 94.85% of classification accuracy is achieved in four types of emotion classification tasks, and compared with an existing method, the accuracy is remarkably improved.

The invention relates to an epileptic seizure detection method and system based on self-attention mechanism and GRU-LSTM fusion, and the method comprises the following steps: (1) carrying out the preprocessing of original electroencephalogram signal data, and extracting time domain and nonlinear features; (2) randomly dividing the data set into a training set, a test set and a verification set; (3) constructing a deep learning model fusing a self-attention mechanism, a gating circulation unit and a long short-term memory network; (4) extracting fusion features from the trained deep learning model, and inputting the fusion features into a support vector machine classifier to perform epileptic seizure and non-seizure classification; and (5) outputting a classification result through multi-modal feature fusion, long and short term dependence modeling and adaptive feature selection. The method has the advantages that a self-attention mechanism, a gating circulation unit, a long-short-term memory network and a support vector machine classifier are combined, deep features of electroencephalogram signals are extracted through a multi-stage processing flow, and finally epileptic seizure and non-seizure classification is carried out.

The invention provides a high-quality 3D Mesh reconstruction method based on brain EEG (electroencephalogram) signals. The electroencephalogram signals can be quickly and efficiently converted into a high-fidelity three-dimensional mesh model. Firstly, an EEG encoder extracts time domain and frequency domain features from an EEG signal to enhance the representation capability of the signal. The time-frequency embedding is then input to a two-stage image generation module guided by the EEG, generating a high-quality two-dimensional image. And finally, through a multi-view diffusion model and a sparse view reconstruction technology, the generated two-dimensional image is used as input, and reconstruction of the three-dimensional grid is rapidly completed. According to the scheme, the technical problem of converting the EEG signal into the three-dimensional object is effectively solved, an important breakthrough is made in the fields of electroencephalogram signal processing and three-dimensional reconstruction, and a new view angle and a new tool can be provided for research in the fields of acknowledging science, psychology and neuroscience.

A method for recognizing a motor imagery electroencephalography (MI-EEG) signal based on a capsule network (CapsNet) is provided, and relates to the technical field of deep learning and brain-computer interfaces (BCIs). An electroencephalography (EEG) time series is mapped into a three-dimensional (3D) array form based on a spatial electrode distribution. By combining 3D convolution, a CapsNet constructs a three-dimensional capsule network (3D-CapsNet) for recognizing an MI-EEG signal. A 3D convolution module performs feature extraction from both a temporal dimension and an inter-channel spatial dimension through a plurality of layers of 3D convolution to obtain a low-level feature. The low-level feature output by the 3D convolution module is integrated through the CapsNet to obtain a high-level spatial vector containing an inter-feature relationship. A primary capsule and a motor capsule are connected through dynamic routing, and finally a CapsNet module outputs a classification result through a nonlinear activation function squash.

The invention discloses an intelligent brain-controlled muscle electrical stimulation rehabilitation therapeutic apparatus, and relates to the technical field of electrical stimulation rehabilitation therapeutic apparatuses, the intelligent brain-controlled muscle electrical stimulation rehabilitation therapeutic apparatus comprises an electroencephalogram signal acquisition module, an electromyographic signal acquisition module and a mixed signal processing module, the mixed signal processing module is connected with the electroencephalogram signal acquisition module and the electromyographic signal acquisition module; the method is used for preprocessing electroencephalogram and electromyogram signals, fusing multi-modal features and classifying motion intentions. By installing the mixed signal processing module, preprocessing, multi-modal feature fusion and motion intention classification of electroencephalogram and electromyogram signals are achieved, a preprocessing unit can effectively remove ocular artifacts and power frequency noise, meanwhile, independent component analysis and a dynamic baseline calibration strategy are adopted, the signal quality is further improved, and the accuracy of motion intention classification is improved. According to the multi-mode signal processing mode, the limitation of single signal processing in the prior art is overcome, and the accuracy and effectiveness of rehabilitation treatment are improved.

The invention discloses an epileptic seizure auxiliary prediction and cognitive function evaluation system and method based on multi-modal data fusion and intelligent adaptive optimization. All the modules work cooperatively, and intelligent optimization from multi-dimensional electroencephalogram signal, motion data and physiological data collection, intelligent preprocessing and feature deep extraction and fusion to epileptic seizure prediction and cognitive function evaluation is achieved. Through the combination of neural architecture search and self-supervised comparative learning, the network structure can be automatically optimized on the basis of multi-modal data, and the ability of learning and identifying an epileptic seizure mode and a cognitive state is effectively improved. The system realizes high-accuracy epileptic seizure prediction and cognitive state evaluation through adaptive optimization, and remarkably reduces the missing report rate and the false report rate. The system has the characteristics of high efficiency, intelligence and accuracy, can provide timely early warning for epilepsy patients, provides scientific cognitive function intervention suggestions for doctors, and has wide application prospects.

The invention discloses an automatic marking system for an abnormal wave band of an epilepsy electroencephalogram image, and relates to the technical field of epilepsy electroencephalograms, the system carries out frequency domain analysis and time domain feature extraction on an electroencephalogram signal through Fourier transform and convolution operation, abnormal waveforms such as sharp waves, ratchet waves and slow waves can be accurately identified, and the accuracy of the abnormal wave band marking is improved. The sensitivity of the system to fine abnormal wavebands is enhanced, highly similar abnormal wavebands can be combined into a complete abnormal event, redundant annotation and repeated event annotation are avoided, further refinement is performed through calculation of the spectrum entropy and the spectrum entropy difference value, setting of a reasonable threshold value and combination of the ratio difference value, and the abnormal event annotation accuracy is improved. The system can identify and label different epilepsy stages more accurately, the system optimizes labeling through multi-dimensional feature differences such as frequency spectrum entropy difference and ratio difference values of different wavebands, seizure, precursor and normality of epilepsy in different stages can be distinguished more accurately, and the multi-level labeling method effectively improves the epilepsy prediction capacity.

A speech brain-computer interface neural decoding system based on Chinese language, a method for controlling the speech brain-computer interface neural decoding system, and a method for implementing the speech brain-computer interface neural decoding system are disclosed. The speech brain-computer interface neural decoding system includes an electroencephalography (EEG) data acquisition module, a significance feature screening and verification module, a speech imagery EEG data decoding module, and an understandable speech synthesis module. The speech brain-computer interface neural decoding system integrates EEG data collection, EEG feature extraction, EEG feature screening, EEG signal decoding for reconstructing speech spectrum information and understandable speech synthesis. After obtaining reconstructed spectrogram features, a Pearson correlation analysis may be performed with original spectrogram features, with a correlation generally being ≥80%. The decoding performance of the speech brain-computer interface neural decoding system is superior to traditional decoding models, effectively improving communication between patients with speech disorders and the outside world.

The invention relates to a deep learning and emotion recognition technology, in particular to a two-way electroencephalogram emotion recognition method and system combining general path learning and medical priori. The method comprises the following steps: preprocessing an electroencephalogram signal, and extracting a difference entropy feature; constructing an adjacent matrix AC based on a general path and an adjacent matrix AR of a brain region divided based on medical priori; taking the difference entropy features as node features, respectively taking the adjacent matrixes AC and AR as edges, and constructing a path graph structure and a prior graph structure; performing graph convolution operation on the path graph structure and the prior graph structure to update node features according to the adjacency matrixes AC and AR; fusing the two paths of updated node features to obtain high-dimensional fusion feature matrix representation; and the electroencephalogram emotion is identified and classified through a multi-layer perceptron. According to the method, general emotion recognition features and biological prior of brain region division are effectively combined, and more comprehensive and accurate feature representation is provided for emotion recognition.

State of art techniques, need a decoder following the encoder to encode EEG signals, whose morphology is undefined. Embodiments herein disclose a method and system for a Spiking Neural Network (SNN) based low power cognitive load analysis using electroencephalogram (EEG) signal. The method receives a raw EEG signal from multichannel EEG set up, wherein each of the raw EEG signal is re-referenced and encoded into a spike train using a Light-Weight-Lossless-Decoder less-Peak-based (LWDLP) encoding. Further, the spike trains are processed by the SNN architecture using backpropagation based supervised approach, wherein the spatial information and the temporal information are learnt by the SNN in form of neuronal activity and synaptic weights. Post learning the SNN architecture applies an activation function on the neuronal activity for classifying a cognitive load level experienced by a subject from among a plurality of predefined cognitive load levels using a SNN classifier.

The invention discloses an EEG signal spine wave detection method and system based on KAN feature fusion, and the method comprises the steps: carrying out the preprocessing of electroencephalogram data, and obtaining a training set and a test set; performing parallel feature extraction to obtain multi-KAN parallel features, and performing splicing and dimension reduction to obtain mixed features; performing gating dynamic selection on the obtained mixed features to obtain fusion features of comprehensive multi-scale analysis of the EEG signals; focusing the key period features based on a double attention mechanism to obtain weight fusion features; inputting the obtained weight fusion features into a classifier, and training a prediction model based on a cross entropy loss function; evaluating the performance of the model, and outputting a classification result. The epileptic seizure risk prediction is realized by extracting the electroencephalogram heterogeneous features, and inputting the electroencephalogram heterogeneous features into the classifier after gating dynamic fusion and double attention time sequence compression. The feature expression capability is obviously improved, the focusing capability in a key period is enhanced, and the classification performance is optimized.

For use in transcranial photobiomodulation (PBM) or brain wave or heart rate variability (HRV) biofeedback and using event-related potential (ERP) and induced and induced EEG oscillation (ERO) measurements, devices and methods for analyzing and monitoring electroencephalogram (EEG) electrical activity evoked during, prior to, and / or after a neurocognitive test of a human user to stimulate and modulate brain activity. An electroencephalogram (EEG) and photo-biometric modulation (PBM) apparatus in the form of a wearable device with headphones is described for stimulating the brain with PBM or monitoring and evaluating electrical activity generated by the brain of a person during stimulation using biofeedback training and simultaneous occurrence of EEG, also described are methods for testing the cognitive and physiological state of a person during a cognitive test using the provided devices. The PBM light is pulsed to stimulate an increase in target brain activity by providing additional energy to the mitochondria and activating other photosensitive brain processes. PBM stimulation is combined with EEG sensors for detecting EEG responses during cognitive testing using event related potentials (ERPs) and induced and induced EEG oscillations (EROs) for evaluating cognitive status and therapeutic function outcomes of a user.

A system of fibers, filaments, and / or other electrically conductive materials forms an electrically conductive-to-semi-conductive yarn that can be assembled into a textile for measurement of voltage, current, resistance, capacitance, inductance, RF, and / or EM signals. Textiles are formed through weaving, knitting, lacing, and / or non-woven mechanical methods of yarn-making into 2D / 3D structures. Textile-based electrodes can be formed via folding, cutting, layering, sewing, and / or embroidering patterns to control signal transmission within / through the electrode. Multiple electrodes are positioned on a surface (e.g., a body) to sequentially or simultaneously perform multiple diagnostic modalities (e.g., electrocardiography, electromyography, electrooculography, electroencephalogram, bioelectrical impedance analysis, skin impedance analysis, and / or electrodermal activity). These modalities are multiplexed using an optimized electrode set through amplitude and frequency deconvolution and filtering algorithms to minimize the quantity of electrodes and connections on the surface while maximizing signal-to-noise ratio, differential and common mode noise rejection, and elimination of external signals (e.g., RF and EM noise).

The invention relates to a three-dimensional spiral high-density neural electrode and a preparation method and application thereof. The three-dimensional spiral high-density neural electrode comprises a probe structure and a plurality of electrode sites. The probe structure is formed by curling a planar flexible electrode precursor, and at least one end of the probe structure is provided with a spiral outer surface of a three-dimensional spiral line structure. Electrode sites are distributed along a spiral path, have a size of 5-1000 [mu] m, and are used for contacting biological tissues. On the planar flexible electrode precursor, electrode sites are arranged on one or more straight lines forming an inclined angle alpha with the axial direction of the probe, and the spatial distribution is matched with the edge. Compared with the prior art, the method has the advantages that the constraint of a traditional wiring mode is broken through, the integration of high-density three-dimensional channels is realized under a micro size, and a new generation of solution is provided for a high-precision brain-computer interface, deep brain stimulation and a three-dimensional electroencephalogram.

The invention discloses a brain function excitation inhibition state evaluation method, which comprises the steps of detection tool selection, signal acquisition, preprocessing, characteristic parameter extraction and result evaluation, the detection tools comprise electroencephalogram, functional near infrared spectrum imaging and functional magnetic resonance imaging, the detection tools are selected according to the adaptability of a patient, and a treatment strategy is customized in a personalized manner. The method has high pertinence, and the brain function state of the patient can be rapidly evaluated; by extracting a plurality of characteristic parameters as an evaluation basis, the dynamic attributes of the brain are comprehensively considered, and the accuracy and comprehensiveness of a detection result are ensured; and meanwhile, based on assignment of characteristic parameters, dynamic changes of the brain regions can be evaluated in real time during interaction between the brain regions, and the accuracy and reliability of a detection result are ensured by deeply considering and integrating dynamic characteristics of the brain.

The invention belongs to the technical field of signal decoding, and relates to an electroencephalogram signal decoding method and device, equipment and a medium. The method comprises the following steps: acquiring a data set of electroencephalogram signals, and obtaining a training set and a test set; constructing a teacher model, and inputting the long-length electroencephalogram signals in the training set into the teacher model for training to obtain a pre-trained teacher model; constructing a student model, respectively inputting the long-length electroencephalogram signal and the short-length electroencephalogram signal in the training set into the pre-training teacher model and the student model, and then calculating the characteristic distillation loss based on frequency mask generation distillation by taking the middle layer characteristics of the pre-training teacher model as transmitted knowledge to obtain the total loss of the student model; performing supervised training on the student model by adopting the pre-trained teacher model to obtain an optimized student model; and decoding the short-length electroencephalogram signals in the test set by adopting an optimized student model. According to the invention, the decoding precision of the short-length electroencephalogram signal can be improved.

The invention relates to the field of intelligent health management, and provides an epileptic seizure early warning method and system, which uses a data processing technology based on deep learning to perform noise reduction processing and feature extraction on an electroencephalogram signal of a user to be monitored in a predetermined time period, and performs operation state feature extraction on a time queue of motion data at the same time. And intelligently identifying whether the to-be-monitored user has an epileptic seizure risk or not according to the cross-modal aggregation representation between the motion state time sequence coding feature and the electroencephalogram signal feature, and generating a corresponding epileptic seizure early warning prompt. Therefore, more accurate epileptic seizure early warning can be realized by comprehensively capturing subtle modes in different user data and fully considering the interrelation between the features.

The invention relates to the technical field of brain-computer interfaces, in particular to a self-adaptive calibration method and system for a brain-computer interface earphone, and the method comprises the following steps: obtaining electroencephalogram data; based on the electroencephalogram data, constructing an initial model through federal learning; selecting a user specific sample by using an active learning strategy; performing dynamic parameter optimization on the initial model; based on the result of the dynamic parameter optimization, adaptive personalized calibration is executed; performing anomaly detection and correction on the result of the self-adaptive personalized calibration; performance evaluation is carried out on the result obtained after anomaly detection and correction, the evaluation result is fed back to the previous steps, and the steps form a closed-loop optimization process; through the combination of federal learning and active learning, the calibration time is shortened from 45 minutes of a traditional method to about 5 minutes, and the efficiency is improved by nearly 9 times; the accuracy rate of the method reaches 95.8% and is improved by 8.6% compared with that of a traditional method.

The invention relates to the technical field of electroencephalography, in particular to a sleep staging automatic identification method and system based on multi-mode electroencephalography, and the method comprises the following steps: extracting a waveform period to construct dominant distribution, correcting label boundary positioning mutation response, unifying a time axis to form an alignment structure, clustering mutation fragments to establish an alternating relation, and identifying the sleep staging based on multi-mode electroencephalography. And identifying a main channel range to construct an automatic identification scheme. According to the method, a reference channel is positioned through area distribution difference, a boundary change section is judged in combination with a frequency band energy change trend, a channel region with dense and stable mutation points is extracted as an anchor point, time axis unified adjustment is completed according to main response starting and ending time, and an alternating fragment graph is further constructed through a cross-channel time coverage relation of the mutation points. The sequential structure coordination ability and mutation form aggregation expression efficiency among multi-source signals are improved, and accurate recognition of boundary drift and asynchronous response and dynamic extraction of steady-state rhythm in sleep stage division are achieved.

A system, a method, and a computer program product is provided for alerting users based on brain signals. The alerting system receives brain signals from a wearable device worn by a user. The brain signals correspond to electroencephalography (EEG) signals associated with a brain of the user. The alerting system detects a spike in the received brain signals. The detected spike indicates an increase in a stress level of the user. The alerting system further determines one or more actions to be triggered based on the detected spike in the received brain signals. The alerting system controls a user device to trigger a first action associated with an initiation of a first call, indicating the increased stress level of the user, to a first set of user devices associated with a first set of users. The first action is included in the determined one or more actions.

The invention discloses a multi-modal deep learning traceability method and system fusing magnetoencephalogram and electroencephalography, and relates to the technical field of artificial intelligence and neuroimage.Real magnetoencephalogram signals and electroencephalography signals are preprocessed and then input into a traceability model, the probability of occurrence of a source in a corresponding area is predicted, and the traceability of the source in the corresponding area is obtained by combining an imported source partition distance matrix. A final traceability result is obtained; the training process of the traceability model is as follows: constructing a generative adversarial network, and generating a multi-modal neural electrophysiological data set; inputting the multi-modal neural electrophysiological data set into a residual network of a double-branch structure, and performing stage hierarchical extraction and decoupling on magnetoencephalogram signals and electroencephalogram signals respectively; extracting features in different stages by using a multi-scale convolution module, fusing the extracted features, inputting the fused features into a classifier, defining a loss function, and updating trainable parameters of the traceability model; the traceability method improves the accuracy and generalization ability of traceability positioning.

The invention relates to a cross-domain electroencephalogram emotion recognition method and device based on multi-scale relation learning, and the method comprises the steps: inputting an electroencephalogram signal into a multi-scale relation learning network, and obtaining a fused emotion feature through a multi-scale separation module, a connection relation learning module and a multi-scale fusion module which are connected in sequence in the multi-scale relation learning network; wherein the fused emotional features comprise information of a plurality of source domains; and inputting the fused emotion features into a general feature induction network, eliminating interference of the personalized emotion features on emotion recognition by using a Dirichlet priori network in the general feature induction network, and obtaining an emotion recognition result. According to the technical scheme, the multi-scale relation learning network is used for capturing the emotional characteristics, the brain connection relation better conforming to cranial neuroscience can be learned, emotional information of multiple source domains is comprehensively considered, the fused emotional characteristics are concluded, personalized emotional interference is eliminated, and the generalization ability is improved.

The invention discloses a brain region correlation analysis system and method for autistic children. The system comprises a brain region division module used for dividing the cerebral cortex into a plurality of brain regions; the training data set construction module is used for constructing a training data set, and each training sample comprises a sensor space function connection matrix and a source space function connection matrix corresponding to the sensor space function connection matrix; the signal preprocessing module is used for acquiring a real electroencephalogram signal and calculating a real value of a corresponding sensor space function connection matrix; the deep learning mapping module is used for learning a mapping relation from the sensor space function connection matrix to the source space function connection matrix and outputting a predicted value of the source space function connection matrix; and the brain region correlation analysis module is used for calculating the correlation between the brain regions. The method can be used for accurately analyzing the correlation between the brain areas of the autism children.

The invention discloses a multi-mode postoperative child pain identification method. The method aims at improving the accuracy of child postoperative pain recognition by fusing ECG (electrocardiogram), SPO2 (blood oxygen) and EEG (electroencephalogram) multi-mode data. The method comprises the following steps of: firstly, respectively acquiring and preprocessing each modal data, and converting EEG (electroencephalogram) into two types of two-dimensional frames; then, feature extraction is automatically conducted on the data of all the modes through specific neural networks including the frequency domain flow neural network, the time domain flow neural network, the oxyhemoglobin saturation CNN-GRU neural network and the electrocardio CNN-GRU neural network; and finally, inputting the extracted features into an MLP feature fusion layer to carry out pain identification. Compared with a traditional single-mode identification method, the postoperative child pain related characteristics can be more comprehensively captured, the pain identification accuracy is effectively improved, and powerful support is provided for medical staff to accurately evaluate the postoperative child pain degree in time.