11 results about "Brain model" patented technology

The application relates to a control method for dynamic walking of a biped robot and the biped robot, and belongs to the technical field of robot control, which comprises the following steps: 1, a self-recurrent cerebellar model neural network is used to establish a dynamic model of the biped robot with a disturbance term, and dynamic robust walking of the biped robot is converted into a problem of realizing stability of a multi-input multi-output nonlinear system with a bounded uncertain term; 2, an adaptive self-recurrent cerebellar model neural network error observer is designed to estimate an error upper limit; 3, an adaptive law of network weight is designed to realize real-time updating of the network weight space and to adjust parameters of each learning factor; and 4, a boundary value estimation algorithm is used to compensate for an estimation error and feedback to a robot walking system, so that the biped robot can realize asymptotic stable walking. The application enables the control system to adapt to time-varying characteristics of the biped walking system on line, and has continuous learning and adaptive capacity for unknown dynamics.

This application discloses a method and apparatus for processing brain electric fields. The method includes: acquiring a three-dimensional model of the brain; the three-dimensional model includes M voxels and brain region labels for each voxel in the M voxels; sequentially inputting current to each pair of electrodes in N pairs of electrodes deployed in the three-dimensional model; determining a guiding field matrix based on the conductivity corresponding to the brain region label of each voxel in the three-dimensional model; the guiding field matrix is ​​used to characterize the electric field distribution at each voxel location under stimulation by each pair of electrodes; and determining the envelope of the electric field distribution of the three-dimensional model based on the guiding field matrix. This method can determine the envelope of the electric field distribution of the three-dimensional brain model with high accuracy.

The VEM-robot emotional right-brain model construction method decomposes the robot's brain into an emotional right brain, an intellectual left brain, and a motor cerebellum, forming the embodied or humanoid robot operating system VEM-ROS. This enables emotional communication between the robot and humans or other robots. Human output is used as the perceptual spectrum, and robot output as the deductive spectrum. Both the perceptual and deductive spectra are segmented, synthesized, and aligned using the emotional rhythm of the multimodal VEM-Token. LLM-Token decomposition of the large language model calculates lexicalized logical perception, and VEM-Token decomposition calculates the micro-expressions of multimodal emotional components. VEM-ROS also includes priority and interruption mechanisms, VEM memory mechanisms, dialogue relationships, craniofacial and limb sensors and actuators and interfaces, robot cloning, and operating system encapsulation. The right-brain model supports emotional micro-expression communication between the robot and humans, similar to the Turing test, and is expected to distinguish whether the emotional test subject is a robot or a human.

This application discloses a magnetic resonance-compatible time-interference neuromodulation system, comprising: an image acquisition module for acquiring structural magnetic resonance (SMR) images and functional magnetic resonance (fMRI) images; a localization simulation module for analyzing SMR images to determine the target brain region and construct a brain model, calculating an initial stimulation parameter set based on the brain model and initial stimulation commands, and updating the stimulation parameter set based on stimulation commands; an analysis module for analyzing fMRI images to obtain functional state determination results and spatial consistency determination results; an electrical stimulation module for receiving the initial stimulation parameter set and initial stimulation commands to output a time-interference electrical stimulation signal, and receiving stimulation commands to adjust the time-interference electrical stimulation signal; and a control module for generating the initial stimulation commands and generating stimulation commands based on the functional state determination results and spatial consistency determination results. The system provides highly real-time, multi-index closed-loop modulation of time-interference electrical stimulation, highly adaptable to clinical intervention and laboratory research tasks related to brain region modulation.

A biomechanically realistic brain model for impact testing comprises white matter simulant materials and gray matter simulant materials positioned to correspond with anatomical brain structure. The white matter simulant comprises anisotropic hydrogels with embedded magnetically-responsive, electrically-responsive, thermally-responsive, and / or mechanically-responsive particles that exhibit directionally-dependent stress-strain responses. The gray matter simulant comprises isotropic hydrogels or silicones, particularly siloxanes, that exhibit uniform stress-strain responses to applied forces. The materials are cast, injected, printed, or formed in anatomically correct positions and share realistic interfaces. The brain model accurately imitates physical brain responses during impact tests, particularly angular impacts, providing realistic testing results for biomechanical analysis.

The present disclosure provides systems and methods for deep learning super-resolution training and / or image generation for low and ultra-low field magnetic resonance imaging. In some aspects, a method includes obtaining a first image of a brain with a low field strength magnetic resonance imaging system. The first image has a first resolution. The method further includes obtaining a deep learning brain model based on a high field strength image. The deep learning brain model can be configured to be applied by a neural network comprising a plurality of layers. The method further includes applying the deep learning brain model to the first image to generate a second image of the brain. The second image has a second resolution, and the second resolution is greater than the first resolution.

The application provides a method for establishing a surgical virtual training system based on robot-assisted intracranial fine operation, comprising the following steps: establishing a surgical virtual training system through a Qt module, an OpenGL module and an external library Chai3d; establishing a brain model and a surgical instrument model in the surgical virtual training system, and integrating a force feedback haptic device Sigma.7 in the surgical virtual training system; establishing a brain tissue model based on the brain model and in combination with an LHDM model; training the brain tissue model based on a progressive layered constraint-driven cutting method; obtaining performance data by performing interactive operation on the brain tissue model through the force feedback haptic device Sigma.7, and optimizing the performance of the surgical virtual training system based on the performance data. The application can ensure high authenticity and clinical relevance of the surgical virtual training system in the surgical training process.

The present disclosure discloses a model construction method and device, a storage medium and an electronic device, which are used for constructing a brain-like model of a pulse neural network based on biological brain topology constraints, and relate to the technical field of computational neuroscience. The present disclosure solves the problem of lack of biological rationality of the brain-like model of the pulse neural network. The model construction method comprises: dividing brain regions of to-be-processed functional magnetic resonance imaging data to obtain M brain region image data; generating M model nodes based on the M brain region image data; generating N model edges based on a correlation coefficient matrix between the M model nodes; screening the N model edges based on a preset network topology threshold to obtain S model edges meeting a preset condition; generating a topology constraint of a brain-like model based on a biological brain function network based on the M model nodes and the S model edges; and constructing the brain-like model based on the topology constraint. The present disclosure can improve the biological rationality of the brain-like model constructed based on the pulse neural network.

The application discloses a method and system for locating and delimiting a cortical pathological focus based on an intracranial electroencephalogram signal. The method comprises the following steps: constructing an individualized three-dimensional navigation model; obtaining an original intracranial electroencephalogram signal; generating a plurality of representative segments according to the original intracranial electroencephalogram signal; analyzing the original intracranial electroencephalogram signal and the plurality of representative segments to obtain network analysis core indexes; generating a fusion three-dimensional brain model according to the network analysis core indexes and the individualized three-dimensional navigation model; generating a space-time three-dimensional electroencephalogram model and an electrophysiological functional area distribution map according to the fusion three-dimensional brain model and the network analysis core indexes; and generating accurate electrophysiological locating and delimiting information of the cortical pathological focus according to the space-time three-dimensional electroencephalogram model and the electrophysiological functional area distribution map. The application realizes accurate electrophysiological delimiting of an intraoperative cortical pathological focus (glioma, hemangioma, focal cortical dysplasia, etc.).

This invention belongs to the field of obstacle avoidance technology for underwater autonomous vehicles (AUVs). First, it acquires the original observation state of the AUV; then, it constructs a dynamic threshold adaptive module to rationally adjust the pulse trigger rate based on dynamic threshold changes; next, it constructs a pulse encoder and a pulse decoder to achieve mutual conversion between continuous information and pulse sequence information; finally, it constructs a threshold adaptive brain-like model, including a pulse dynamic threshold Actor network and a deep Critic network, deploying the dynamic threshold adaptive module in each spiking neuron of the Actor network; and finally, it inputs the original observation state into the trained model and outputs action commands to control the vehicle's obstacle avoidance and navigation. This invention utilizes the dynamic threshold changes of spiking neurons to rationally adjust the trigger rate, maintaining model equilibrium and ensuring that the AUV possesses safe, stable, and efficient dynamic obstacle avoidance capabilities. It has significant application value for achieving dynamic obstacle avoidance tasks for AUVs in complex and unknown environments.

The application provides a multi-frequency time-domain interference stimulation simulation optimization method and device, which comprises the following steps: constructing a corresponding Gaussian function according to the time-domain interference stimulation parameters for a target clinical user and obtaining the Fourier series of each channel of a transcranial electrical stimulation instrument; generating a brain model based on brain T1-weighted imaging data and setting corresponding target point position information to optimize the target point electric field direction in any direction; determining the effective electrode arrangement area of each axial section of the brain model and arranging electrodes; optimizing the electrodes arranged in each axial section to determine each grid of the brain model and the time-domain interference stimulation electric field intensity amplitude, and optimizing the input current of each channel. The application can realize multi-frequency stimulation simulation optimization for each channel of the transcranial electrical stimulation instrument, can improve the focusing of the time-domain interference stimulation, can reduce the calculation complexity of the time-domain interference stimulation parameter configuration, and can effectively and quickly realize personalized time-domain interference stimulation simulation combined with clinical data.