532 results about "Electro encephalogram" patented technology

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 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.

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 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.

The invention relates to the technical field of intelligent early warning, and discloses a multi-mode intracranial pressure intelligent early warning method and system.The method comprises the steps that an intracranial pressure waveform signal, an electroencephalogram signal and a transcranial Doppler blood flow velocity signal are synchronously collected, and a multi-mode physiological data set is obtained; extracting time domain features, frequency domain features and nonlinear features of the preprocessed multi-modal physiological data set to obtain a standardized feature parameter set; distributing dynamic weights for features in the standardized feature parameter set according to feature modal types of the standardized feature parameter set to obtain a fusion feature vector; performing intracranial pressure prediction on the patient according to the fusion feature vector after nonlinear transformation to obtain an intracranial pressure change sequence; according to the deviation condition of the intracranial pressure change sequence and a clinical preset safety threshold value, an intracranial pressure early warning signal is output; the accuracy of intracranial pressure early warning can be improved.

The invention discloses a five-dimensional dynamic emotion visual chemotherapy healing method and system, and relates to the technical field of emotion visual chemotherapy healing, and the method comprises the steps: employing a multi-sensor fusion method to collect physiological data, psychological assessment questionnaire results and historical emotion data of a user, and obtaining a basic emotion feature vector of the user; mapping the emotional state of the user to a five-dimensional emotional space based on the basic emotional feature vector, and initializing a particle set; collecting multi-mode biological signals of electroencephalogram, heart rate variability, electrodermal response, body temperature and voice emotion recognition of the user, and processing to obtain a feature vector reflecting the current emotion state of the user; the geometric morphology and kinetic parameters in the particle set are dynamically adjusted, a visualization engine is used for rendering particles, a two-way feedback adjustment mechanism is constructed, particle behaviors are adjusted according to the emotional state of the user, and the emotional adjustment ability of the user is enhanced; and designing an interactive emotion regulation game.

The invention discloses a biomedical multi-mode signal anomaly detection and prediction method and system, and the method comprises the steps: modeling a biomedical multi-mode signal into a time-varying non-local dynamic system, capturing the long-time-history dependence characteristic of the signal through a memory mechanism of a Caputo fractional derivative, and introducing a time-varying input item to process interference; the method comprises the following steps of: carrying out high-precision numerical integration by adopting an Adams-Bashform-Module solver; optimizing model parameters in combination with a local domain normalization pre-training strategy and a fusion loss function; abnormal detection is realized by calculating comparison between signal reconstruction deviation and a self-adaptive threshold value; a future anomaly probability is generated based on the trajectory prediction. According to the method, the problems of insufficient non-local dependence capture, poor time-varying interference robustness, high false positive rate and the like in the prior art are effectively solved, the accuracy and real-time performance of abnormal detection and prediction of biomedical signals such as electroencephalogram and electrocardiogram are remarkably improved, and the method is suitable for wearable medical equipment and clinical monitoring systems.

Provided herein are systems, kits, and methods for monitoring brain activity. In some implementations, a system includes a plurality of wearable sensors having a housing with an extended, rounded shape are removably attached to the scalp of a patient and monitor electroencephalogram (EEG) signals. Approaches for instructing a user to position and active that wearable sensors are disclosed. Approaches for facilitating collection, synchronization, and processing of EEG signals are disclosed. Approaches for handing off control of the wearable sensors between portable computing devices are disclosed.

The invention relates to the technical field of electroencephalogram analysis, in particular to a domain-adaptive cross-subject electroencephalogram signal emotion recognition method, which comprises the following steps: acquiring an electroencephalogram signal, and preprocessing the electroencephalogram signal; inputting the preprocessed electroencephalogram signals into a trained electroencephalogram signal emotion recognition model to obtain an emotion classification result; the electroencephalogram emotion recognition model comprises a graph convolution feature extraction module, an attention module, a domain confrontation module and a classifier module. According to the method, the confrontation module formed by combining the gradient inversion layer and the domain discriminator is designed, and the change rule of the electroencephalogram of a person in positive, neutral and negative states is focused instead of the intensity of the reaction, so that the extracted emotional features have domain invariance, and the extraction accuracy is improved. And identification deviation caused by difference of different individuals in cross-subject emotion identification is eliminated.

The invention relates to electroencephalogram signal acquisition, in particular to an electroencephalogram signal acquisition and monitoring system, which comprises a mobile terminal, a signal acquisition device, an EEG (electroencephalogram) signal, an fNIRS hemodynamic signal and IMU (inertial measurement unit) motion data, a physical information neural network method of a self-adaptive sampling strategy is adopted to identify a BCG vascular pulse pseudo-film segment in an EEG signal by using an fNIRS hemodynamic signal, and then a multiple linear regression model of motion artifacts is established based on the fNIRS hemodynamic signal and IMU motion data so as to identify a motion pseudo-film segment in the EEG signal. Performing interpolation restoration on the detected pseudo-film segments to eliminate artifacts in the EEG electroencephalogram signals, and drawing and displaying an electroencephalogram in real time according to the EEG electroencephalogram signals after the artifacts are eliminated; according to the technical scheme provided by the invention, the defect that BCG vascular pulsation artifacts and motion artifacts in the EEG signals are difficult to effectively eliminate can be effectively overcome.

The present invention describes an artificial intelligence (AI) enabled electroencephalography (EEG) system that integrates Pathway Hierarchical Adaptive Referencing (PHAR) for localized signal detection, large language models (LLMs) for automated EEG reporting, and Internet of Medical Things (IoMT) connectivity for adaptive neuromodulation control. The system can also deliver transcranial electrical stimulation (tES) pulses and function as an electrical impedance tomography (EIT) system. PHAR employs a multi-layered multiplexer hierarchy and adaptive referencing topologies to optimize EEG signal acquisition and spatial resolution. LLM integration enables automated generation of human-readable EEG reports. IoMT connectivity allows closed-loop neuromodulation, where real-time EEG analysis guides the adjustment of stimulation parameters. The system can deliver tES pulses and perform EIT expands its functionality, allowing for targeted neuromodulation and impedance-based brain imaging. This integrated system revolutionizes EEG-based diagnostics, treatment, and research in neurology and neuroscience, offering a comprehensive and versatile tool for understanding and modulating brain function.

The invention discloses an electroencephalogram signal source space function connection estimation system and method based on deep learning, and the system comprises a training data set construction module which is used for constructing a training data set containing N training sample pairs, and each training sample pair 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 preprocessing the real electroencephalogram signal; the sensor space function connection matrix calculation module is used for calculating a corresponding sensor space function connection matrix true value; and 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, receiving a true value of the sensor space function connection matrix and outputting a predicted value of the source space function connection matrix. According to the method, the source space function connection matrix can be efficiently and accurately estimated.

The invention provides a transcranial magnetoacoustic stimulation closed-loop feedback method and system based on electroencephalogram guidance. The method comprises the steps that original electroencephalogram signals of a target brain area are collected and preprocessed; target brain region activity features are extracted based on the preprocessed electroencephalogram signals, and the neural activity state of the target brain region is judged according to the target brain region activity features; according to the state judgment result, initial stimulation parameters are generated and optimized in combination with a preset stimulation response model; the generated initial stimulation parameters are converted into actual stimulation signals, the transcranial magnetoacoustic stimulation device is controlled to execute ultrasonic and magnetic field stimulation on the target brain area, and closed-loop feedback is conducted according to the activity state of the target brain area. According to the method, the limitation of a traditional transcranial stimulation method in the aspects of real-time feedback, individualized adaptation, artifact interference and safety control is effectively solved, accurate, controllable and individualized intervention of the neural activity of the target brain region is achieved, and a repeatable, safe and efficient technical means is provided for nerve regulation and control research and clinical application.

A system and method is disclosed for testing and training a brain capability of planning and executing motion activity. The system comprises the following: an electroencephalographic sensor arrangement attachable to a head of said trainee; a processor configured for receiving and analyzing electroencephalographic signals obtained from said trainee in response to said visual stimulus displayed to said trainee; a memory storing instructions fori. instructing the trainee to imagine executing said motion action;ii. measuring electroencephalographic signals on the electroencephalographic sensor arrangement;iii. calculating at least one of a concentration index; a motor control index; an alertness index;iv. and iteratively providing the trainee with a feedback patternIn some embodiments a display is provided as a visual stimulus to the trainee to which the trainee responds by imagining executing a motion activity.

The invention provides a multi-modal emotion recognition method and system based on knowledge distillation. The method comprises a pre-training stage, a knowledge distillation stage, a fine adjustment stage and a prediction stage. The pre-training stage comprises the following steps: independently training a preset neural network by using electroencephalogram, electrocardiogram and facial expression data to obtain three independent teacher models; the knowledge distillation stage comprises the step of migrating the representation learned by the teacher model to the student model through a knowledge distillation technology; the fine tuning stage comprises the step of carrying out joint optimization on the student model by utilizing a small amount of labeled multi-modal data; the prediction stage comprises the steps of dynamically fusing the multi-modal features by adopting an attention mechanism, and inputting the fused features into a classifier to obtain an emotion recognition result. The method is oriented to three modes of electroencephalogram, electrocardio and facial expression, and the characterization ability of the model to complex emotion and state change is improved; and a knowledge distillation mechanism is introduced, so that the complexity of the model is reduced, and the deployment feasibility and the operation efficiency of the model in practical application are improved.

The invention discloses a digital electroencephalograph, and belongs to the technical field of brain-computer interface signal processing. An electroencephalogram signal processor of the digital electroencephalograph acquires digital neural signals of a plurality of channels for a target object, wherein the digital neural signals comprise LFP signals and pulse signals; performing feature extraction on the two signals based on a unified time window and a sliding step length, and integrating a time-aligned feature matrix into a first fusion feature tensor; performing dynamic normalization processing on the first fusion feature tensor, wherein the statistical magnitude is updated along with the change of the input data; and n parallel LSTM networks are used to decode the normalized second fusion feature tensor to obtain n decoding results of different dimensions, the n LSTM networks are mutually independent, and each LSTM network has an independent optimizer and an independent parameter. According to the method, multi-modal features can be aligned in time, signal drift is compensated, decoding robustness is improved, and inter-task optimization decoupling and personalized training are achieved through independent parameters and an optimizer.

The invention discloses an EEG-based epilepsy automatic detection method and system, and belongs to the field of disease automatic detection. The method comprises the steps of collecting multi-channel electroencephalogram EEG signals in real time, conducting signal preprocessing, time-frequency feature extraction, deep learning analysis and multi-modal prediction, achieving accurate detection and prediction of epileptic seizure, generating real-time risk assessment of epileptic seizure through a prediction module in combination with multi-modal feature information, and achieving real-time risk assessment of epileptic seizure. The epilepsy early warning system sends an early warning signal to a user or a medical worker, starts corresponding intervention measures, has high detection precision and real-time response capability, and can provide effective early warning and management for epilepsy patients.

In various embodiments, computer-implemented systems and methods for extracting pixel trajectories representing waveforms from a digital image, formed by lines and columns of picture elements, pixels, of a recording of an electrical activity of a human organ detected by on-skin electrodes, such as an electrocardiogram, ECG, or an electroencephalogram, EEG, are provided.

The utility model relates to the technical field of electroencephalogram cap bodies, in particular to a silica gel electroencephalogram cap with an automatic adjusting function. The electroencephalogram cap comprises an electroencephalogram cap body, longitude lines are installed in the electroencephalogram cap body, a plurality of electric heads are installed in the electroencephalogram cap body, the electroencephalogram cap body is electrically connected with a connecting line, the lower surface of the electroencephalogram cap body is fixedly connected with two binding plates, and the binding plates are fixedly connected with the connecting line. The lower surfaces of the two binding plates are connected with a first connecting plate and a second connecting plate respectively, the lower surface of the first connecting plate is fixedly connected with a binding belt, a plurality of adjusting holes are formed in the surface of the binding belt, one side of the binding belt is fixedly connected with a connecting block, and one side of the second connecting plate is fixedly connected with a square frame plate. The electroencephalogram cap solves the problems that the electroencephalogram cap does not have good conductivity and softness, cannot be tightly attached to the scalp, signal transmission errors exist, discomfort is easily caused after the electroencephalogram cap is worn for a long time, and indentations and pressure sores are generated on the head of a user.

A method is provided. The method comprises obtaining, using electroencephalogram (EEG) sensors, a first set of EEG signals that comprises a plurality of first waveforms, and each of the plurality of first waveforms is associated with a frequency band from a plurality of frequency bands; training a plurality of machine learning-artificial intelligence (ML-AI) models using the first set of EEG signals, wherein each of the plurality of ML-AI models is trained for a different frequency band; obtaining, using the EEG sensors, a second set of EEG signals, wherein the second set of EEG signals comprises a plurality of second waveforms; inputting each of the plurality of second waveforms associated with the frequency band into a corresponding ML-AI model associated with the respective frequency band to generate a plurality of outputs; and performing one or more actions based on the plurality of outputs.

The invention discloses a cerebral apoplexy patient postoperative neural function real-time monitoring method. The method comprises the steps that brain MRI image data and clinical information of a to-be-monitored patient and post-operation electroencephalogram data collected in real time are collected and preprocessed; respectively extracting neurophysiological features and radiomics features of the patient to be monitored based on the preprocessed data; performing significant feature screening on the clinical information, the neurophysiological features and the radiomics features, performing preprocessing on the screened data, and combining minimum absolute contraction and selection operator regression analysis to obtain quantitative electroencephalogram data feature indexes and radiomics scores; inputting the screened clinical information, the quantitative electroencephalogram data characteristic index and the radiomics score of the to-be-monitored patient into a trained prediction model, and predicting a risk index of early neurological deterioration of the to-be-monitored patient; the problem that the evaluation result is inaccurate due to the fact that a single clinical feature is adopted to evaluate the neural function in a traditional method is solved.

The invention discloses a personalized electroencephalogram emotion recognition method and system based on dynamic brain region division, and belongs to the technical field of electroencephalogram emotion recognition. The method comprises the steps that electroencephalogram signals are preprocessed, and an electroencephalogram signal matrix is constructed; on the basis of the preprocessed electroencephalogram signals, five-frequency-band differential entropy feature values are extracted, multi-head self-attention calculation is carried out in combination with rotation position coding, and channel feature representation with strong emotion intensity is output; on the basis of the constructed electroencephalogram signal matrix, dynamically dividing a brain function region by using a CNM algorithm, optimizing the structure of the brain function region by using a modularity value, and outputting brain region characteristics; and fusing the channel feature representation and the brain region features, predicting an emotion tag through a graph neural network, and outputting an emotion recognition result. According to the method, the individual brain function region can be dynamically divided, the function integration information of the brain region is effectively captured, and deep fusion of brain region function connection and channel emotion information is realized.

The invention provides a sleep stage method based on prototype data comparative representation learning, which comprises the following steps of: S1, acquiring an electroencephalogram signal related to sleep, and respectively performing strong enhancement processing and weak enhancement processing on the electroencephalogram signal to obtain a strong enhanced electroencephalogram signal and a weak enhanced electroencephalogram signal; s2, respectively carrying out coding processing to obtain strong enhancement coding features and weak enhancement coding features; respectively extracting time features to obtain a strong enhancement time view and a weak enhancement time view; s3, performing cross prediction on the strong enhancement time view and the weak enhancement time view to obtain time prediction features; introducing prototype data, and comparing the time prediction features with the prototype data to obtain comparison features; and S4, completing sleep staging based on the comparison characteristics. According to the method, the SPC model is designed by adding the prototype data into the context comparison module, so that the comparison learning efficiency is improved, the overall staging accuracy is improved, and the staging accuracy of each sleep stage is good.

A method for predicting a development level difference of language and communication capability normality of a healthy infant or toddler. The method comprises obtaining electroencephalogram (EEG) or magnetoencephalogram (MEG) waveform data of a healthy infant or toddler caused by external auditory stimulus; obtaining, from the EEG or MEG waveform data, quantitative data that represents measurement index data of a central nervous system caused by the external auditory stimulus; and using a data set to train a machine learning classifier, so as to obtain a prediction result. Further provided are a relevant tool and an integrated system.

The electroencephalograph system comprises an MCU (Microprogrammed Control Unit), an acquisition unit and a buffer, the acquisition unit comprises a plurality of acquisition chips, and the plurality of acquisition chips are connected in series in a daisy chain mode through serial interfaces; the acquisition chip at the first stage is connected with the MCU unit through a serial interface; in this way, application of multiple channels is achieved, and the device can be suitable for collection of electroencephalogram signals with different functions; and the buffer can increase the driving capability of driving the DI N pin of the lower-level acquisition chip by the DOUT pin of the upper-level acquisition chip, so that the reliability of data transmission is improved.

The invention discloses a sleep staging method based on multi-view gating interactive attention fusion. The sleep staging method comprises the steps that a single-channel electroencephalogram signal is preprocessed; an original electroencephalogram sequence and a time-frequency graph obtained through continuous wavelet transform are generated and serve as multi-view-angle input; time sequence features are extracted from the original electroencephalogram sequence through a feature extraction module, and time-frequency features are extracted from the time-frequency graph; fusing the time sequence features and the time frequency features through a feature fusion module, including respectively applying convolution attention to highlight internal key features at an electroencephalogram view angle and a time frequency graph view angle, and performing interaction between view angles through cross attention; convolutional attention output and cross attention output are adaptively fused through a hierarchical expert hybrid mechanism; and outputting a sleep stage classification result through the time convolution network. According to the invention, more comprehensive feature representation is realized.

The invention discloses a transcranial stimulation discharge and electroencephalogram acquisition system for simulating acupuncture and moxibustion. The system comprises an electroencephalogram acquisition module, a multi-mode traditional Chinese medicine large language model and a transcranial stimulation discharge module, the electroencephalogram acquisition module is used for acquiring electroencephalogram signals and somatosensory expression voice information of a user, performing preprocessing and feature extraction on the electroencephalogram signals of the user to generate an electroencephalogram of the user, and performing textualization processing on the somatosensory expression voice information of the user to generate text information; the multi-modal traditional Chinese medicine large language model is used for performing traditional Chinese medicine pathology analysis in combination with the electroencephalogram and the text information of the user to generate a treatment suggestion text; and the transcranial stimulation discharge system is used for generating a stimulation signal according to the treatment suggestion text and applying the stimulation signal to the second electrode to execute transcranial stimulation discharge so as to simulate acupuncture treatment. Closed-loop diagnosis and treatment of noninvasive detection, intelligent analysis and precise regulation can be realized, and the problems that traditional Chinese medicine treatment depends on experience and the curative effect is difficult to quantify are solved.

The invention discloses an intelligent asthenopia detection method, system and device, and relates to the field of asthenopia detection.The method comprises the steps that feature extraction is conducted on electroencephalogram information and eye movement information to obtain an electroencephalogram feature set and an eye movement feature set; respectively inputting the electroencephalogram feature set and the eye movement feature set into a plurality of preset asthenopia detection models to obtain a plurality of asthenopia detection results; determining a final asthenopia detection result according to the plurality of asthenopia detection results; wherein different preset asthenopia detection models correspond to different machine learning models, and each preset asthenopia detection model is obtained by training the machine learning model according to the asthenopia detection sample set. According to the invention, real-time, efficient, objective and accurate asthenopia detection can be realized.

The invention relates to a multimodal sleep staging method based on a bidirectional interactive RWKV network, and belongs to the technical field of data processing, and the method comprises the following steps: obtaining an electroencephalogram signal, an electrooculogram signal and an electromyogram signal; carrying out depth feature extraction on the electroencephalogram signal, the electrooculogram signal and the electromyogram signal; the first RWKV module is used for realizing context modeling of information in the modals and fusion of information between the modals by taking the first RWKV module as a main part and the second RWKV module as an auxiliary part; the first RWKV module and the second RWKV module are used as a main part and an auxiliary part, and context modeling of information in modals and fusion of information between the modals are achieved through the second RWKV module parallel to the first RWKV module; the outputs of the two RWKV modules are integrated through element-by-element addition to form a fusion feature; and generating a category with the highest probability for the posterior probability distribution of each sleep stage as a sleep stage prediction result. The method is high in staging efficiency and high in accuracy.