32 results about "Neural function" patented technology

A system for estimating a subject's functional brain age using non-invasive electroencephalography (EEG), the system includes: an EEG acquisition module (101) configured to record multichannel EEG signals from the subject; a stimulation control module (102) configured to present a predefined sequence of cognitive and sensory tasks that specifically target the frontal, temporal, parietal and occipital brain regions, and to generate synchronized event markers; a data processing module (103) configured to prepare the recorded EEG signals by filtering, artifact removal, normalization and segmentation into task-oriented time windows; a machine learning module (104) comprising one or more self-monitoring contrastive learning encoders configured to transform the preprocessed EEG signals into latent feature representations; and a brain age estimation module (105) configured to process the latent feature representations to generate a BrainAge Score indicating a difference between a predicted biological brain age and the subject's chronological age.

The application provides a personalized rehabilitation path making method and system based on amygdala function evaluation. First, the application obtains multi-modal neural function data of an individual. Second, the amygdala function lateralization mode is determined based on amygdala sub-region functional connection data. Third, the amygdala function lateralization mode is cooperatively analyzed with preset target individual behavior representation data. Finally, the personalized rehabilitation path is generated according to the cooperative analysis result obtained by the analysis. The technical scheme provided by the application not only realizes sub-region level accurate evaluation of amygdala function by integrating multi-modal neural function data and individual behavior representation data, but also generates a highly individualized rehabilitation path with precise coupling of neural activity and rehabilitation training, thereby effectively improving the pertinence and effect of intervention.

The application discloses a high-fidelity three-dimensional face reconstruction and generation method based on an implicit neural function, and proposes a high-fidelity parameterized face model which can be semantically decoupled and edited, and simultaneously introduces new identity, expression representation and semantic segmentation networks. With the aid of the implicit neural representation, the method can perform high-fidelity reconstruction and synthesis on face details, difficult expressions and large-angle camera poses, so that the generated face pictures have good reality and clarity.

The invention provides a personalized rehabilitation path making method and system based on amygdala kernel function evaluation. The method comprises the following steps: firstly, acquiring multi-modal neural function data of an individual; secondly, determining an amygdala function bias mode based on the amygdala subregion function connection data; then carrying out collaborative analysis on the amygdaloid nucleus function bias mode and preset target individual behavior characterization data; and finally, generating a personalized rehabilitation path according to a collaborative analysis result obtained by analysis. According to the technical scheme provided by the invention, the subregion-level accurate evaluation of the amygdala kernel function is realized by integrating the multi-modal neural function data and the individual behavior characterization data, and a highly individualized rehabilitation path in which neural activity and rehabilitation training are precisely coupled is generated accordingly, so that the pertinence and effect of intervention are effectively improved.

The invention discloses a wind power AC / DC system transient stability evaluation method based on a positive definite structure neural Lyapunov function. The method comprises the following steps: constructing a state space physical model containing a wind power AC / DC system, and collecting real-time operation data; determining a candidate Lyapunov function based on the neural network; updating parameters of the Lyapunov function based on a gradient method; generating a counter example set based on a projection gradient descent algorithm, and verifying a Lyapunov function based on an SMT solver; the method comprises the following steps: acquiring the operation state of a wind power-containing AC / DC system to obtain a training set, constructing a total training set based on the training set, a counter-example set and a verification set, and training based on the total training set to obtain a trained neural Lyapunov network; and evaluating the transient stability state of the wind power AC / DC system based on the trained neural Lyapunov network. According to the method, the local Lyapunov function of the complex power system can be effectively learned, and the transient stability of the alternating-current and direct-current hybrid system containing wind power can be quickly and accurately evaluated.

The invention discloses an emotion recognition, consciousness evaluation and auxiliary rehabilitation method and device based on closed-loop olfactory stimulation, and a medium, and relates to the technical field of neural function diagnosis and artificial intelligence, and the method comprises the steps: obtaining a first face image sequence of a patient under odor stimulation; inputting the first facial image into a facial action unit intensity estimation model, and obtaining a plurality of facial action unit recognition results output by the model online; determining the facial expression change intensity of the patient based on the recognition results of the plurality of facial action units; and under the condition that the facial expression change is lower than a preset threshold value, adjusting the concentration and the type of the odor stimulation in real time based on a preset concentration gradient, and re-collecting the image for evaluation. Through multiple expression intensity results, the consciousness level of the patient is determined, the olfactory consciousness brain network remaining condition is judged, a rehabilitation training scheme is formulated, and longitudinal tracking is implemented. The emotion and consciousness level of the patient can be accurately evaluated in real time, and the method is applied to olfactory stimulation assisted rehabilitation.

Presented herein are techniques that make use of objective measurements obtained in response to acoustic stimulation signals. More specifically, at least one measure of outer hair cell function and at least one measure of auditory nerve function are obtained from a tonotopic region of an inner ear of a recipient of a hearing prosthesis. The at least one measure of auditory nerve function and the least one measure of outer hair cell function are then analyzed relative to one another.

The application discloses a construction method and application of a post-cardiopulmonary resuscitation neural function prognosis prediction model, and a method, which comprises the following steps: acquiring clinical data of patients who have achieved spontaneous circulation recovery after cardiopulmonary resuscitation, and screening clinical patients meeting the inclusion criteria; taking brain function classification as a dependent variable, respectively adopting LASSO regression analysis and Boruta feature screening to screen characteristic variables, and selecting common characteristic variables; based on the common characteristic variables, constructing multiple machine learning models, and drawing a ROC curve and a decision curve corresponding to each machine learning model; adopting AUC to evaluate the discrimination of the machine learning models, adopting the decision curve to evaluate the clinical benefits of the machine learning models, and screening an optimal prediction model; and adopting SHAP tools to explain the optimal prediction model and the common characteristic variables. The prediction model constructed by the application can conveniently and quickly predict the neural function prognosis within 24 hours after cardiopulmonary resuscitation.

The invention relates to a self-adaptive closed-loop exoskeleton system based on a multi-modal brain-computer interface. The self-adaptive closed-loop exoskeleton system comprises a) a multi-modal signal acquisition unit; b) an adaptive neural decoding algorithm module; c) the three-joint exoskeleton follows the motion intention in real time through a hierarchical control strategy to control exoskeleton joints to move, and gait parameters are dynamically adjusted; d) a redundancy switching unit which is switched to an EEG full-function mode within 200ms when the ECoG signal is abnormal; and e) a spinal cord electrical stimulation module which is used for providing closed-loop auxiliary stimulation when the electroencephalogram signal is weak. High-precision and high-reliability motion control can be provided, the self-adaptive complex environment interaction capability is achieved, nerve function remodeling and rehabilitation can be promoted, and a safe solution is provided for motion control of a spinal cord injury patient.

The invention discloses a preparation method and application of neural stem cells capable of being used for in-vivo visual neural circuit connection. The method comprises the following steps: constructing a tool virus vector which is driven by a broad-spectrum promoter EF1 alpha, is optimized by a mammal codon and is expressed by a fusion reporter gene of wheat germ lectin and tyrosinase, and transfecting the vector into neural stem cells. TYR catalyzes tyrosine in cells to synthesize melanin through a multi-step reaction, and the in-vivo visualization of a brain region connected with a neural circuit formed by transplanted neural stem cells can be realized by utilizing a nuclide labeling probe of targeted melanin. According to the method, the neural stem cells for in-vivo tracing of neural circuit connection of the neural stem cells are established for the first time, and when and where the neural stem cells are dynamically and visually traced to establish connection with the host neural network in a living body; and a powerful research tool is provided for exploring a specific brain region in which the neural stem cells form loop connection in vivo and an action mechanism of the neural stem cells in neural function repair.

This invention provides a head-mounted intelligent wearable neuromodulation and sleep state regulation device and method based on neural coupling tuning and state transfer induction, including a flexible contact array, a state recognition module, a state transfer induction module, a neural coupling modulation module, a stimulation output module, and a feedback optimization module. The system acquires brain state feature vectors and, based on sleep stages and state regulation goals, divides the head into multiple neural functional node regions. Based on the brain state feature vectors, it dynamically generates node combination relationships, node activation sequences, and node collaborative paths. The state transfer induction module controls multimodal stimulation signals to form rhythmic synchronization, phase coupling, and node collaborative regulation, and outputs the signals to the probes corresponding to the target nodes. Simultaneously, it collects feedback signals in real time and dynamically optimizes the topological relationships between nodes and stimulation parameters to achieve closed-loop transfer regulation of brain states between different sleep stages. This invention can improve the individual adaptability, regulation accuracy, and safety of sleep state regulation.

The application relates to the technical field of a brain-computer interface driven wearable adaptive brain-spinal cord synergistic neural regulation system, and particularly discloses a brain-computer interface driven wearable adaptive brain-spinal cord synergistic neural regulation system, which aims to solve the problems of brain and spinal cord regulation device synergistic stimulation, brain signal decoding based on a brain-computer interface, and a closed-loop adaptive neural regulation mechanism. The system comprises a brain-computer interface signal acquisition and decoding module, a multi-modal physiological state sensing module, and an adaptive brain-spinal cord synergistic neural regulation decision and execution module. The system realizes closed-loop adaptive neural intervention by collecting electroencephalogram and electromyogram signals in real time and dynamically generating brain and spinal cord synergistic regulation parameters. The application improves the individualization, precision and continuity of neural function repair, and has wearable integration and safety monitoring capabilities.

The invention discloses a novel porous microgel cell delivery system with high-throughput preparation capability, the system is composed of three parts, namely an aqueous two-phase system, a gas-assisted microfluidic device and a microporous microgel support, and can realize rapid and large-scale preparation of homogenized microgel, and the natural microporous structure in the microgel can be used for preparing a cell with high throughput. The survival and proliferation of various stem cells and somatic cells are facilitated, and high cell loading capacity and long-term activity maintenance are realized; the composite hydrogel between the microgels provides support for migration of endothelial cells and lumen formation, has good injectability and biocompatibility, and is matched with various tissue repair applications, and animal experiments find that in an ischemic stroke rat model, the composite hydrogel has a good application prospect. The microporous microgel loaded with the primary rat neural progenitor cells is injected, so that long-term neural function recovery of stroke rats can be effectively improved; the invention establishes a porous microgel delivery system with high throughput, high cell loading capacity and multiple vascularization functions, and the porous microgel delivery system is suitable for various tissue engineering applications.

The application discloses a kind of based on proprioception guide's brain-computer interface closed loop rehabilitation training system and method, it is related to brain-computer interface technical field, to solve the problem that existing fusion stimulation's brain-computer interface system lacks the individualized rehabilitation training method of stage, guiding, and with promoting central nervous remodeling as basic goal in rehabilitation training, system includes stimulation module and the central control module being connected with stimulation module, the stimulation module applies external stimulation to target muscle group;The central control module is respectively connected with intention decoding module and multi-modal feedback module, the intention decoding module judges whether motion intention characteristic meets standard according to built-in algorithm model;The intention decoding module is connected with electroencephalogram acquisition and processing module, and the electroencephalogram acquisition and processing module real-time acquisition and pre-processes electroencephalogram signal;The technical scheme of the application can enhance the authenticity and effectiveness of motor imagination, promote neural function remodeling.

The embodiment of the invention discloses an electroencephalogram feature extraction method combining ABR and ERP waveform, comprising: acquiring left and right brain wave shapes from the same test, the brain wave shapes including ABR waveform and ERP waveform; for the left and right ABR waveforms and the left and right ERP waveforms, time features, amplitude features, density features and fatigue features between single-side waveforms and double-side waveforms are extracted; and fusing time features, amplitude features, density features and fatigue features of the left and right ABR waveforms and the left and right ERP waveforms as electroencephalogram features for reflecting neural activity. According to the embodiment of the invention, multi-dimensional effective signal features can be extracted from the ABR and ERP brain wave shapes to comprehensively reflect the neural function states of the brain and the brainstem.

The invention discloses a clinical application-oriented cloud edge hybrid AI artificial spinal cord system and a control method, and relates to the technical field of neural engineering and brain-computer interfaces. The system comprises a neural signal acquisition module, a multi-mode perception fusion module, an edge real-time control unit, a cloud intelligent training unit, a nerve stimulation output module, a somatosensory feedback module, a clinical-level safety fusion module and a wireless communication module. According to the invention, a core architecture of low-delay real-time control at an edge end less than or equal to 15ms, cloud individualized AI model training, rehabilitation process dynamic adaptation and networking non-inductive upgrading is adopted, the motion intention decoding accuracy is improved through multi-modal sensing fusion, and the use security and data privacy are guaranteed through a clinical-level security compliance system; the motion function of the patient with the spinal cord transection injury can be reconstructed, the control strategy can be dynamically optimized according to the rehabilitation process of the patient, and the lifelong iteration upgrading of the system is realized. The problems that in the prior art, pure edge computing power is insufficient, pure cloud delay is too high, individual adaptation and clinical compliance core pain points are lacked are solved, the motion intention recognition accuracy is larger than or equal to 98%, clinical conversion can be directly achieved, the method is suitable for rehabilitation of diseases such as spinal cord injury, cerebral apoplexy and cerebral palsy and nerve function replacement, and the commercialized application prospect is wide.

The invention discloses a pre-hospital stroke face recognition method and system based on a hybrid deep learning model, and belongs to the field of medical artificial intelligence. The method comprises the following steps: processing multi-modal baseline data when a user executes a standardized challenge in a healthy state by adopting a hybrid deep learning model so as to construct a personalized baseline response model; when the suspected stroke occurs, processing multi-modal instantaneous data when the user executes the same challenge by adopting a hybrid deep learning model to generate an instantaneous response trajectory; the hybrid deep learning model calculates a potential function failure potential representing a neural function instability degree based on a dynamic difference between the transient response trajectory and the baseline response model, and determines a time change trend of the potential function failure potential to obtain a failure potential accumulation rate; and finally, the model generates a face recognition result representing the stroke risk level based on the potential function failure potential and the failure potential accumulation rate. According to the invention, early and fine stroke-related face and speech abnormalities can be recognized with high sensitivity.

The invention provides a heat stroke early warning device and method based on zero heat flux and multi-parameter perception, and relates to the technical field of military operation medical protection. A zero-heat-flux core body temperature monitoring technology, a high-precision heart rate sensor and a three-axis acceleration sensor are integrated, key indexes such as core body temperature, heart rate and gait stability of heat stroke forebodes are monitored in real time, the neural function state is accurately judged in combination with an intelligent algorithm, and a multi-stage early warning system is constructed; early warning of labor heat stroke can be effectively achieved, and reliable protection is provided for people who work and exercise in the high-temperature environment.

This invention relates to the field of data analysis technology, specifically to a method and system for analyzing neural functional characteristics based on multimodal data. The method includes the following steps: acquiring multimodal neural data, analyzing rhythmic and reactive and motor characteristics, calculating coupling and blockage parameters, and normalizing them into feature vectors to determine abnormalities. In this invention, a unified time axis coordinate system is constructed to precisely align cortical neural potentials with limb movement trajectories. Hilbert transform is used to analyze signal phase, and neural pathway blockage mapping parameters are calculated using response dispersion to quantify conduction stability. For the phenomenon of phase inversion in precursor potential segments during motor fluctuations, precursor neural coupling parameters are established to characterize the motor readiness state. Based on the comparison of multidimensional feature vectors with a standard baseline, automated and accurate classification of neural functional abnormalities is achieved, effectively solving the pain points of being unable to quantitatively analyze neural behavioral coupling mechanisms and the difficulty in distinguishing between conduction blockage and readiness defects.

The invention relates to an anxiety symptom new target targeted localization and aiTBS precise regulation and control system based on a causal network atlas, and the system comprises an image obtaining module which is used for obtaining the head MRI data of a user; the data processing and target spot positioning module is used for calculating individualized nerve regulation target spot coordinates based on the brain network causal atlas model and MRI (Magnetic Resonance Imaging) data; the neural navigation module is used for registering the target point coordinate to the physical space of the head of the user and guiding the coil to align; and the stimulation control and application module is used for applying magnetic pulses to the target spot according to a preset accelerated intermittent theta pulse stimulation (aiTBS) parameter set. The invention further relates to a corresponding method and device, a processor and a storage medium thereof. By adopting the system, the method, the device, the processor and the storage medium, accurate and efficient regulation and control of a specific neural circuit are realized by integrating causal atlas positioning and efficient aiTBS stimulation, and an advanced tool and scheme are provided for neuroscience research and neural function optimization.

The invention relates to the technical field of neuroinformatics, in particular to a dyskinesia treatment analysis method and system based on neural circuit feedback, and the method comprises the following steps: dividing functional partitions based on neural circuit activity, extracting signal fluctuation and discharge trajectory to recognize an abnormal interval, analyzing the consistency of signal gradient and direction according to a candidate set, and obtaining a dyskinesia treatment result. And extracting a distribution matrix, analyzing uniformity, screening abnormal regions, evaluating a deviation calibration grade, screening partitions needing to be adjusted, and outputting a neuromodulation linkage adjustment data table. According to the method, the signal fluctuation and the discharge track are extracted based on neural circuit activity division, and time synchronization analysis is carried out, so that the abnormal interval can be accurately identified, local uniformity evaluation is enhanced by using a signal distribution matrix and functional feature analysis, and neural circuit response coordination and signal transduction stability are optimized; accurate positioning and dynamic regulation and control of dyskinesia causes are realized, and pertinence and consistency of nerve function evaluation and regulation are ensured.

The application discloses a neuron action potential electric-thermal coupling simulation method based on temperature effect, relates to the technical field of neuron electric activity simulation methods, and comprises the following steps: S1, geometric modeling and mesh division; S2, temperature-dependent improvement is carried out on a Hodgkin-Huxley model; S3, material assignment: material properties are assigned to each structure of the model in Abaqus; S4, a user-defined subroutine is called to realize the regulation of temperature as a continuous variable parameter on the dynamics of a Hodgkin-Huxley ion channel; the user-defined subroutine comprises a USDFLD subroutine, a UMATHT subroutine and a HETVAL subroutine; a neuron axon three-dimensional finite element model is established through the Hodgkin-Huxley model, the temperature-dependent Hodgkin-Huxley dynamics equation is directly embedded through the user-defined subroutine, and electric-thermal-mechanical three-field full coupling is realized; meanwhile, the application also provides an effective simulation tool and a theoretical basis for the research on the influence of a subsequent thermal environment on neural functions.

The application discloses a kind of emotion recognition, consciousness evaluation and auxiliary rehabilitation method, equipment and medium based on closed loop olfactory stimulation, it is related to neural function diagnosis and artificial intelligence technical field, comprising: obtaining the first facial image sequence of patient under the stimulation of smell;First facial image is input into facial action unit intensity estimation model, and the multiple facial action unit recognition results of model output are obtained online;Determine the facial expression change intensity of patient based on multiple facial action unit recognition results;In the case where facial expression change is lower than preset threshold, the concentration and type of smell stimulation are adjusted in real time based on preset concentration gradient, and image is recollected for evaluation.Through multiple expression intensity results, determine the level of consciousness of patient, judge the residual situation of olfactory consciousness brain network, formulate rehabilitation training scheme and implement longitudinal tracking.The application can evaluate the emotion and consciousness level of patient in real time, accurately, and be applied to olfactory stimulation assisted rehabilitation.

The invention discloses a neural function feature recognition method and system based on multi-modal signal processing, and relates to the technical field of multi-modal signal processing, and the method comprises the following steps: carrying out the preprocessing of an original behavior data stream and electroencephalogram data of a user, obtaining multi-modal data segments, extracting behavioral features and electroencephalogram micro-state features, and obtaining a multi-modal data segment; performing adaptive weighted fusion to obtain neural function feature vectors, associating the neural function feature vectors with corresponding metadata, adding labels, storing the neural function feature vectors in a database, establishing an index structure to obtain a neural function feature database, and when the feature vectors to be stored in the database are obtained, judging a new different mode through a preset similarity threshold value. And automatically storing the new different patterns into a database and updating an index structure, performing similarity retrieval on the to-be-recognized neural function feature vector and the database, and outputting a recognition result if the recognition confidence reaches a preset recognition confidence threshold according to the recognition confidence of label distribution in a retrieval result. Otherwise, transferring the query feature vector into a dynamic updating process.

This invention discloses a novel optical device belonging to the field of sensory intelligence. Its structure and formation method are as follows: it is composed of a large number of precisely arranged optical fibers, with a hammer-shaped form. Its large end face, i.e., the light-incident end face, is a convex or concave curved surface structure, while its small end face, i.e., the light-outceasing end face, is a planar microarray structure of optical fibers or a curved microarray structure of optical fibers. Its main functions are: mimicking the retinal nerve function of human and animal eyes, it can collect light signals in space and transmit them in parallel to a photoelectric conversion array for visual perception signals, forming high-dimensional voxel signals; and it can amplify and reduce these high-dimensional voxel signals for further signal processing by brain-like sensory integrated circuits. This invention also discloses the fabrication methods of this novel optical device, including optical fiber drawing, micro-nano fabrication, and 3D printing.

The application discloses a kind of neural disease detection analysis method and system, are acquired bracelet collection signal, sphygmomanometer collection signal, motion behavior image and behavior test data, are extracted tremor intensity characteristics, gait symmetry characteristics and autonomic nervous rhythm characteristics to bracelet collection signal by multi-band decomposition;Analysis motion behavior image and standardized action test obtains motor function score;Carry out heart rate variability analysis and identify abnormal mode of neural function;Neural function state atlas is constructed and feature weight is calculated;Combine historical monitoring data to predict disease progression trend;Through subsequent feedback correction information dynamic adjustment prediction result.The application realizes long-term trend prediction and dynamic correction based on initial data by the way that short-time intensive monitoring is combined with long-period intermittent collection, significantly improves the reliability and practicality of neural disease risk assessment in home scene.

The invention provides an attention deficit hyperactivity disorder (ADHD) subtype recognition and evolution analysis method based on comparative learning, which comprises the following steps: acquiring brain functional magnetic resonance imaging data of an ADHD patient and a healthy person, and extracting low-frequency amplitude and functional connection multi-modal data from the brain functional magnetic resonance imaging data; inputting the multi-modal data into a feature coding module to generate potential feature representation of each modal; inputting the potential feature representation into a multi-view learning module, generating a projection semantic feature and a shared semantic feature, and minimizing the difference between the projection semantic feature and the shared semantic feature; performing hierarchical multi-agent regularization constraint on the shared semantic features; inputting the shared semantic features into a classifier for classification and recognition; and carrying out dimension reduction on the shared semantic features to obtain feature evolution trajectories from healthy people to different subtypes. According to the method, healthy people and disease subtypes can be identified, the characteristic dynamic evolution process from the healthy people to the subtypes is modeled, the neurological function heterogeneity of the disease is revealed, and a basis is provided for personalized precise diagnosis and treatment.

The present application relates to the technical field of neural information, in particular to a movement disorder treatment analysis method and system based on neural loop feedback, comprising the following steps: dividing functional partitions based on neural loop activity, extracting signal fluctuation and discharge trajectory to identify abnormal intervals, analyzing signal gradient and direction consistency according to candidate set, extracting distribution matrix to analyze uniformity and screen abnormal areas, evaluating deviation calibration level, screening partition adjustment, and outputting neural regulation linkage adjustment data table. In the present application, signal fluctuation and discharge trajectory are extracted based on neural loop activity division and time synchronization analysis, which can accurately identify abnormal intervals, and signal distribution matrix and functional feature analysis are used to strengthen local uniformity evaluation, optimize neural loop response coordination and signal conduction stability, realize accurate positioning and dynamic regulation of movement disorder causes, and ensure the pertinence and consistency of neural function evaluation and regulation.

The invention relates to a method and device for analyzing visual and neural function changes after visual training, and relates to the technical field of visual neural function evaluation.The method comprises the steps that in the visual training process of a target object, visual brain physiological data of different training groups and trained binocular visual functions are synchronously collected through EEG-fNIRS, and the visual brain physiological data are obtained; the method comprises the following steps of: acquiring physiological data signals, performing preprocessing and feature extraction on the acquired physiological data signals to obtain corresponding signal features, performing analysis based on the extracted signal features to determine a first analysis result and a second analysis result, and finally analyzing dynamic interaction of electroencephalogram and brain oxygen by utilizing EEG-fNIRS in combination with the two analysis results and binocular visual functions. According to the method, the change analysis result of the visual and neurological functions after visual training is obtained, the examination of organically combining the traditional visual function with the neurological function is realized, the improvement of the visual function is analyzed, and the influence of the visual function on the brain function can be revealed from the neurophysiology level.