69 results about "Neural system" patented technology

A building-integrated artificial intelligence system forming a continuous ambient neural field is disclosed. The system includes a distributed multimodal sensor lattice, an on-premise symbolic cognition engine, and an adaptive environmental control kernel operating entirely at the building edge without reliance on external cloud services. Sensor data from optical, thermal, acoustic, airflow, pressure, structural, electrical, and chemical modalities are transformed into non-identifying occupancy vectors, behavioral glyphs, risk indicators, and environmental state descriptors. A privacy-governed policy graph determines sensor permissions, redaction thresholds, consent conditions, emergency overrides, and jurisdiction-specific compliance parameters. The neural field predicts occupancy loads, optimizes HVAC, ventilation, and lighting, detects accidents and structural anomalies, classifies emergent risks, and generates redacted event capsules for audit and emergency dispatch. A federated topology enables multiple buildings to exchange compressed symbolic templates to improve predictive accuracy without transmitting raw data. The system provides a universal, regulation-aligned AI nervous system for autonomous building operations.

The invention relates to the technical field of nervous system dysfunction, in particular to a nerve regulation and control system for degenerative disease treatment based on multi-modal physiological feedback, which comprises a central processing unit, an optical radiation applicator, a mechanical vibration applicator, an integrated biological signal sensing array and a man-machine interaction module, cooperative stimulation is applied by using a multispectral light source and a broadband vibrator, and multi-dimensional physiological signals such as heart rate variability, myoelectricity, galvanic skin and the like are monitored in real time through a sensor array. A multi-parameter adaptive control algorithm built in the central processing unit can dynamically and intelligently adjust stimulation parameters based on the feedback signals to form an accurate personalized treatment closed loop. Meanwhile, the safety monitoring module based on the biological thermal model ensures the safety boundary of the treatment process. According to the invention, intelligent, self-adaptive and non-invasive treatment of nerve dysfunction is realized.

The invention discloses a photovoltaic buried cable fault positioning system based on a graph neural network, and the system comprises a multi-source signal collection module which is used for collecting electromagnetic response characteristics, and generating a multi-source signal sample set; the topological relation construction module is used for forming weighted directed topological relation data; the frequency domain mapping and fusion module is used for forming frequency domain-space joint feature input; the frequency domain self-evolution graph neural model module is used for executing feature propagation and aggregation of nodes and edges and outputting fault probability and uncertainty results; the space-time active detection module is used for selecting a cable section in an area with the maximum potential energy as an active detection target to form a self-tuning active detection closed loop; and the multi-source constraint correction module is used for outputting the type, the position and the confidence degree result of the buried cable fault. According to the invention, through the frequency domain self-evolution graph nervous system, accurate identification and positioning of the photovoltaic buried cable fault are realized.

The application provides a power transmission line naming recognition method based on a bidirectional enhanced nonlinear pulse nerve, and the core innovation is that a BiENSNP module is constructed to deeply simulate a dynamic information processing mechanism of a biological nerve system, the model is endowed with powerful basic representation capability, and is especially good at modeling complex nonlinear semantic patterns contained in text. The fusion architecture significantly improves the robustness and accuracy of the model in identifying entities in the power transmission line construction field (especially generative text), thereby laying a solid and good scalable technical foundation for constructing high-quality power transmission line construction knowledge base and other key application scenarios.

The invention discloses a sparse electroencephalogram signal source positioning method based on functional magnetic resonance guidance, and relates to the crossing field of electroencephalogram signal processing and neuroimaging technologies. The method mainly comprises three parts of fMRI-guided sparse source space construction, subject specificity forward model construction and multi-target regularization inverse problem solving. The method comprises the following steps: firstly, screening high-activation voxels through BOLD signal intensity of fMRI, and constructing a sparse source space through connected component analysis and representative point selection; then, a subject specific boundary element (BEM) head model is constructed based on the structural MRI, and electrode registration and lead field matrix calculation are completed; and then a multi-target regularization model fusing data fidelity, space compactness and intensity consistency is constructed, optimal estimation of source current intensity is obtained through analysis and solution, and finally a positioning result of brain power activation is output. According to the method, the advantages of high time resolution of the EEG and high spatial resolution of the fMRI are fully played, the limitations of inverse problem morbidity, low spatial resolution and insufficient multi-mode fusion in traditional brain power supply positioning are effectively solved, and brain power supply positioning with high precision, noise resistance and high interpretability is realized; and a new scheme is provided for cognitive neuroscience research, nervous system disease diagnosis and brain-computer interface development.

The application discloses a neural system disease intraoperative monitoring system based on a large model, relates to the technical field of medical information processing, and comprises a synchronous acquisition module, a mapping construction module, a consistency evaluation module, a semantic normalization module and an attribution decision module. The system synchronously acquires double-channel vocal cord electromyography signals and stimulation events, intercepts a response segment and calculates a dominant side symbol; the consistency score of normal and reverse syntax is counted in a recording window, and channel side reversal caused by tracheal intubation rotation is actively identified; accordingly, semantic normalization processing of channel exchange or retention is performed, and the underlying anatomical mapping is corrected; finally, the large model reasons the normalized signal, accurately distinguishes real physiological damage from device measurement artifacts, and maps and outputs action instructions. The application eliminates semantic pollution caused by side error to the large model from the root, and greatly improves the accuracy of intraoperative intelligent monitoring.

This is a medical device consisting of proprietary hardware integrated with sensor technology (telemetry) for head and neck stabilization and monitoring, to offer improved stabilization and comfort while collecting data on the transportation process. By providing critical insights, the efficiency and efficacy of Emergency Medical Services (EMS) and military medical personnel can be enhanced, while also mitigating the risk of neurological worsening.

Systems and methods in accordance with embodiments of the present disclosure include the creation and utilization of synthetic eye and head movement datasets that can be used as digital biomarkers for the screening, diagnosis or monitoring of neurologic diseases, including rare neurologic conditions. The synthetic datasets can be used to train deep learning models that can identify and phenotype neurologic diseases based on distinct eye movement patterns. The system and method create synthetic eye movement datasets using generative Al techniques. A pose-guided video generation framework is utilized to produce synthetic eye movement videos. A latent video diffusion mechanism translates segmented mask inputs — simplified representations of eye movements — into realistic visual sequences that mimic real eye movements associated with specific neurologic diseases.

The present invention relates to a system and method for real-time screening of Attention Deficit Hyperactivity Disorder using electroencephalographic signals processed through a GPU-accelerated time-frequency inference architecture. The invention enables continuous acquisition of multi-channel electroencephalographic data, adaptive preprocessing for artifact suppression and signal stabilization, and parallel execution of time-frequency transformations to extract neurologically relevant features in real time. Extracted features are analyzed using an inference process configured to identify neurological patterns associated with Attention Deficit Hyperactivity Disorder, while continuous validation of signal quality and temporal consistency ensures diagnostic reliability. The system dynamically adapts analytical parameters based on evolving signal characteristics and regulates computational workload to achieve energy-efficient operation during prolonged monitoring. The invention provides a technically integrated, machine-implemented screening solution capable of delivering reliable, real-time neurological assessment suitable for clinical environments.

Described herein are systems and methods used to evaluate individuals such as children for behavioral disorders, developmental delays, and neurological impairments. An exemplary method includes receiving input data of an individual related to a behavioral disorder, neurological impairment, or developmental delay, and evaluating the input data using an evaluation module comprising at least one machine learning model, thereby generating an evaluation result, where the machine learning model comprises one or more decision threshold hyperparameters that differentiate between a positive evaluation, negative evaluation, and an indeterminate evaluation with respect to a presence or an absence of the behavioral disorder, neurological impairment, or developmental delay.

The application relates to an artificial visual nervous system based on a bimodal neural device, comprising a bimodal visual information calculation array composed of a physical convolution kernel array and a synapse calculation array, the physical convolution kernel array is used for receiving an ambient light signal and converting into an electrical signal output, the synapse calculation array is used for receiving the electrical signal and converting into a visual signal, and the physical convolution kernel array and the synapse calculation array are both composed of a plurality of bimodal transistors. The application has excellent bimodal collaborative calculation capability, the receptive field mechanism (excitatory / inhibitory response) of a retinal bipolar cell is simulated through the physical convolution kernel array, image feature extraction and preprocessing are realized, the synaptic plasticity (LTP / LTD mechanism) of a central nervous system is simulated in combination with the synapse calculation array, high-level calculation of the visual signal is completed, and a complete visual nerve bionic system is formed by virtue of an array composed of single devices.

The invention discloses a micro-grid operation random optimization method based on source load probability prediction. The method comprises the following steps: firstly, performing energy-sensitive self-organizing segmentation on source-load historical time sequence data, and extracting morphological fingerprint features; and adopting an improved affinity propagation clustering algorithm fused with a power system operation constraint penalty mechanism to identify a typical operation mode. Secondly, a quantile regression model based on a gated pulse neural P system is established in each mode for probability prediction, and a probability scene library with weights is generated; and finally, constructing a two-stage stochastic optimization model with the goal of minimizing the expected operation cost, and solving by adopting a Benders decomposition algorithm to obtain a fixed equipment plan and a flexible operation strategy. According to the method, through refined mode recognition and probability modeling, on the basis of fully considering the uncertainty of the source load, robust optimization of economic operation of the micro-grid is realized, and the expected operation cost of the system is effectively reduced.

The application belongs to the field of robot control, and provides a high-risk operation robot control system and method imitating human nervous system. The high-risk operation robot control system imitating human nervous system comprises a sensing element, a lower computer, a middle computer and an upper computer; the sensing element is used for sensing robot state information and transmitting the information to the corresponding lower computer; the lower computer is used for transmitting the received robot state information to the upper computer through the middle computer; the upper computer is used for forming instruction information according to a high-risk operation preset target and the received robot state information, and issuing the instruction information to the corresponding lower computer through the middle computer; and the lower computer is also used for controlling the corresponding motion component to execute the corresponding instruction information, and feeding back the received robot state information to the upper computer through the middle computer, to form a sensing-decision-execution closed loop control.

Subject measurement systems can generate indications of neurological state, disease, dysfunction, or injury using a machine learning model. The machine learning model can include at least one encoding model and a sequential model pre-trained to perform language processing tasks. The machine learning model can further include a classifier configured to output classifications. The at least one encoding model, sequential model, and at least one decoding model can be jointly trained to predict timeseries output, thereby adapting the pre-trained sequential model for use with neurologically relevant input domains, such as medical images, EEG data, evoked response data, speech data, or the like. The at least one encoding model, sequential model, and classifier can be jointly trained to output indications of neurological state, disease, dysfunction, or injury. A subject measurement system can then generate such indications using patient data and the at least one encoding model, sequential model, and classifier.

The invention relates to the technical field of image fusion, in particular to a multi-modal image fusion method based on a roundworm spiking nervous system, which comprises the following steps of: respectively inputting registered multi-modal source images into a bionic roundworm spiking nervous system model, and calculating the pulse excitation frequency of each pixel position in a period of time; the image information is converted into a pulse counting graph; comparing the sizes of the two pulse counting diagrams pixel by pixel to generate a corresponding binary weight map; and carrying out weighted average fusion on the two source images according to the weight map to obtain a final fusion image with complementary information and enhanced features. Compared with a pulse neural network, the method is simple in structure, convenient to apply, capable of being rapidly deployed, good in interpretability and capable of flexibly adjusting related parameters according to requirements. Different from the application of pulse neural network excitation, the method achieves an expected fusion effect through weight calculation based on pulse excitation counting.

The invention discloses a nervous system disease intraoperative monitoring system based on a large model, and relates to the technical field of medical information processing, and the system comprises a synchronous acquisition module, a mapping construction module, a consistency evaluation module, a semantic normalization module and an attribution decision module. The system synchronously collects a dual-channel vocal cord electromyographic signal and a stimulation event, intercepts a response segment and calculates a dominant side symbol; the channel lateral inversion caused by trachea cannula rotation is actively identified by counting the normal and inversion grammar consistency score in the recording window; performing semantic normalization processing of channel interchange or maintenance, and correcting underlying anatomical mapping; and finally, reasoning the normalized signal by the large model, accurately distinguishing real physiological injury and equipment measurement illusion, and mapping and outputting an action instruction. According to the method, semantic pollution to a large model caused by identification errors is eliminated from the source, and the accuracy of intraoperative intelligent monitoring is greatly improved.

The invention discloses a self-adaptive regulation and control system and method simulating neuromorphic perception and action reflex. The self-adaptive regulation and control system comprises a dual synaptic transistor, a signal processing circuit and an actuator terminal, external mechanical stimulation is sensed through the double synaptic transistors, excitatory / suppressive post-synaptic current is generated and connected to the signal processing circuit, synaptic current signals are converted and amplified into voltage signals capable of driving the actuator terminal through the signal processing circuit, and the actuator terminal executes response actions after receiving the voltage signals. A closed-loop feedback process of sensing-processing-action feedback is realized; the dual synaptic transistor can continuously sense the dynamic change of external stimulation, generates the action of an excitatory / inhibitory post-synaptic current self-adaptive regulation actuator in real time, and realizes the simulation of rapid reflex and self-adaptive adjustment capability of a biological nervous system.

The invention provides an induction method for directionally differentiating neural stem cells into dopaminergic neurons. The induction method comprises the following steps: (1) electroporating the neural stem cells, and enabling a 40-80nm Fe2O3 (at) TGA (at) MIL-100 (at) PDA (at) Ag nano material to enter the neural stem cells; and (2) placing the neural stem cells in the step (1) in a 0.25-0.4 Tesla magnetic field, rotating the neural stem cells in the horizontal direction for 10-20 minutes without changing the magnetic field intensity, then culturing the neural stem cells, controlling the induced electromotive force induced by the rotating magnetic field to be 0.1-10 mV, rotating the magnetic field direction for 10-20 minutes every 23-25 hours, and culturing the neural stem cells for 72-240 hours. According to the induction method, the dopaminergic neuron differentiation rate is high, the synaptic function maturity is improved, the dopaminergic neuron differentiation rate exceeds 80%, the synaptic function maturity is improved compared with a traditional method, the induction method can be applied to Parkinson's disease treatment and nervous system injury repair, and an efficient induction technology is provided for cell treatment of nervous system degenerative diseases and injuries.

The invention provides a mouse neuron network construction method and system based on two-photon imaging, and the method comprises the steps: collecting neuron imaging data through a two-photon calcium imaging system, and extracting a fluorescence change time sequence of each neuron from the neuron imaging data; constructing a function connection matrix according to the fluorescence change time sequence, and then calculating a small world coefficient and a network core degree of the neural network based on the function connection matrix; and based on the small-world coefficient and the network core degree, using a liquid state machine to establish a brain-like neuron network. The problems that in the prior art, the network connection mode between single neurons cannot be researched at the cellular level, and effective simulation of a spatial-temporal information processing mechanism of a biological nervous system is lacked are solved.

The application discloses a small-scale Hopfield neural network circuit based on a memristor, which comprises an activation function module circuit, a memristor model circuit and a Hopfield neural network circuit; the Hopfield neural network circuit comprises three neuron networks X1, X2 and X3; and a connection weight in the Hopfeild neural network is replaced by the memristor module circuit to obtain a new neural network. By replacing the synaptic weight of the Hopfield neural network system, it is found that the originally stable system exhibits rich dynamic behaviors, including chaos, hyperchaos, quasi-periodicity, double-vortex attractor and the like. The circuit is simple in design, and can adjust the weight parameters Rr and the parameter w32 to realize various dynamic behaviors. Therefore, the circuit can be used in the field of secret communication and is helpful for the research on the nervous system.

The present application relates to the technical field of biological information, and discloses a neural system disease risk assessment method and device, an electronic device and a medical system, the method comprising: acquiring values of a plurality of biomarkers detected based on a target gingival crevicular fluid sample; inputting the values of the plurality of biomarkers as a plurality of input features into an artificial intelligence model to obtain a probability value of a neural system disease risk. The present application comprehensively analyzes a plurality of modal biological characteristics (i.e. values of a plurality of biomarkers such as neural related proteins, pathogenic bacteria load / toxicity, inflammation / barrier, etc.) in an in-vitro gingival crevicular fluid sample, realizes intelligent auxiliary assessment of a neural system disease related risk, and the assessment result can be used for early risk screening, early warning and hierarchical management of a neural system disease, and the cost is low and there is no invasiveness.

A system including a galvanic stimulator, and an eye tracking device, wherein the system is a clinical vestibular implant suitability evaluation system. The system includes a computing apparatus configured analyze eye tracking data generated by the eye tracking device and provide output indicative of the analysis. The system is configured to evoke a vestibular reflex in a human with at least a partially functioning neural system of the human's vestibular system.

This invention discloses a regional neural block path planning method based on three-dimensional medical images, relating to the field of neural block technology. The method includes the following planning steps: S1, determining the body posture of the object to be blocked according to the needs of neural block, and acquiring target area scan data through CT and MRI; S2, training the neural system images using a convolutional neural network and a loss function, performing dynamic optimization, outputting the registration results, and constructing an initial model of nerves, blood vessels, and bones. This invention constructs a relevant model of neural block under three-dimensional medical images. By calculating image loss, registration error, and individual adaptation-related data during the registration process, it helps to evaluate and optimize the registration results, reduce registration errors, construct a variable model, and observe and predict in advance, providing good preliminary preparation for regional neural block path planning, thereby improving the accuracy of regional neural block.

1. Name of the designed product: Potentiometer (medical event-related). 2. Use of the designed product: to assist in the diagnosis and evaluation of nervous system and mental diseases by recording and analyzing the electrical signals produced by the brain in response to specific stimuli. 3. Design points of the designed product: in shape. 4. Picture or photo that best indicates the design points: perspective view 1.

The application belongs to the technical field of neuromorphic computing hardware, and particularly relates to a neuromorphic cell circuit based on a two-dimensional material ferroelectric floating gate transistor and a preparation method thereof. The neuromorphic cell circuit adopts a synapse and neuron nested topological structure, that is, one ferroelectric floating gate transistor is used as an electronic synapse, and a threshold switch memristor and a capacitor are used to cooperatively constitute a biological neuron function module. The application utilizes polarization reversal of a ferroelectric insulating layer and a floating gate mechanism to realize dynamic nonvolatile adjustment of a two-dimensional semiconductor channel conductance; in combination with a volatile threshold switch characteristic of a memristor and a charging and discharging mechanism of a capacitor, the synapse plasticity and action potential leakage integral-discharge behavior of a biological nervous system are perfectly simulated. The application can realize extremely high device consistency and yield; the circuit has sub-nanowatt ultra-low power consumption and high-density integration potential, and provides a device-level solution for the next generation of underlying neuromorphic hardware.

This is a medical device consisting of proprietary hardware integrated with sensor technology (telemetry) for head and neck stabilization and monitoring, to offer improved stabilization and comfort while collecting data on the transportation process. By providing critical insights, the efficiency and efficacy of Emergency Medical Services (EMS) and military medical personnel can be enhanced, while also mitigating the risk of neurological worsening.

The present invention relates to a computer program for training a neurological disease detection algorithm to be used for neurological disease detection in an implantable neurostimulator device having a target electrode configuration, the computer program comprising: a) inputting EEG data into a computer executing the computer program, the EEG data being recorded by at least one EEG from at least one patient using an electrode system having multiple electrode channels; b) identifying neurological activity in the EEG data corresponding to the neurological disease based on a neurological disease identification tag included in the EEG data and / or input into the computer; c) selecting a subset of electrode channels from among the available electrode channels in the EEG data according to c1) the identified neurological activity and / or c2) characteristic data of the target electrode configuration; and d) training the neurological disease detection algorithm by using only the EEG data of the selected subset of electrode channels.