12 results about "Brain right" patented technology

A method of brain imaging detection includes: dividing a brain image into a left brain medical image and a right brain medical image and obtaining the grayscale values of multiple pixels thereof, respectively; mirror-flipping one of the left and right brain medical images; performing a grayscale value subtraction with the mirror-flipped medical image and the other medical image to obtain a plurality of grayscale differences of the pixels; determining whether the grayscale difference of each pixel exceeds a grayscale difference threshold and whether a number of the pixels having the grayscale different exceeding the grayscale difference threshold is greater than or equal to a pixel number threshold; and when the number of the pixels having the grayscale difference exceeding the grayscale difference threshold is greater than or equal to the pixel number threshold, determining that the brain image is abnormal.

The invention discloses a dynamic space-time CNN-Transform emotion brain-computer interface decoding method, and relates to the technical field of brain-computer interfaces, a dynamic time feature extraction module is designed according to the sensitivity of multi-scale convolution to an electroencephalogram sequence along a time dimension, and electroencephalogram sequence time feature information is mined by using convolution kernels of different sizes; constructing a local-global spatial feature extraction module by referring to close correlation between asymmetry of left and right brain regions of the brain and an emotional state, and sequentially extracting spatial feature information of the left brain, the right brain and the whole brain of the electroencephalogram sequence; a spatial-temporal feature fusion module is designed, and spatial-temporal feature relations among the left brain, the right brain and the whole brain are mined; the long-time dependency relationship in the electroencephalogram sequence is captured through an attention mechanism by referring to the advantage of Transform on long-time sequence processing, so that the emotion electroencephalogram decoding precision is effectively improved; and finally, performing emotion recognition on the feature sequence subjected to Transform coding by using a multi-layer perceptron to realize end-to-end emotion electroencephalogram decoding.

This invention relates to the field of EEG emotion recognition technology, specifically to an emotion recognition method and system based on two-level KL divergence. The method includes: acquiring EEG data and calculating the first KL divergence between the left and right hemispheres in each region before and after external sensory stimulation, at each set frequency band; averaging the first KL divergence of each segment in each hemisphere as the feature before and after external sensory stimulation; calculating the second KL divergence based on this feature; and concatenating the feature before and after external sensory stimulation with the second KL divergence as input to a classification model. This invention deeply mines the emotionally sensitive features in EEG signals through two-level progressive distribution difference calculation and combines it with a classification network to achieve model training and accurate classification, thereby achieving precise recognition of subtle emotional changes.

The invention specifically discloses a novel neural network method based on double-brain fusion, computer equipment and a medium, and relates to the technical field of artificial intelligence. The method comprises the steps that firstly, multi-modal data are collected and preprocessed, and time sequence data and spatial data are generated; secondly, simulating human left and right brain division cooperation, and designing a left brain (logic brain) module and a right brain (pattern brain) module which are respectively responsible for sequence data processing and logic reasoning, feature extraction and pattern recognition; a dynamic fusion mechanism is realized through bidirectional attention gating and a resource allocation controller, and output weights of left and right brains are adjusted and resource allocation is optimized according to task requirements; and finally, constructing an adaptive learning framework by means of a meta-learning drive and feedback enhancement module, improving the adaptive capacity of the network to a new task, and adjusting the fusion weight. The method is excellent in key indexes such as accuracy, processing delay and error rate, and has the advantages of efficient multi-modal cooperation, optimized resource utilization rate and strong generalization ability.

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.

The invention provides an obstructive sleep apnea disease recognition system and device based on lead electroencephalogram signals. The system and device can be applied to the technical field of biomedicine and the technical field of artificial intelligence. The recognition system comprises a signal acquisition device used for acquiring a first electric signal in a first target area of the left brain of a user and acquiring a second electric signal in a second target area of the right brain of the user to obtain a lead electroencephalogram signal set; the processor is used for extracting a plurality of initial brain function connection features from the lead electroencephalogram signal set, and updating the plurality of initial brain function connection features according to feature reference values of the initial brain function connection features to obtain a plurality of target brain function connection features; inputting the multiple target brain function connection features into a disease recognition model to obtain a recognition result; and under the condition that the identification result represents the obstructive sleep apnea disease, identifying the disease degree of the obstructive sleep apnea disease according to the Pearson's correlation coefficient and the phase locking value.

The application discloses a dynamic space-time CNN-Transformer emotion brain-computer interface decoding method, relates to the technical field of brain-computer interface, and is characterized in that a dynamic time feature extraction module is designed according to the sensitivity of multi-scale convolution to the time dimension of electroencephalogram sequences, and different size convolution kernels are used to mine time feature information of the electroencephalogram sequences; the asymmetry of the left and right brain regions of the brain is closely related to the emotional state, a local-global space feature extraction module is constructed, and the spatial feature information of the left brain, the right brain and the whole brain of the electroencephalogram sequences is extracted in sequence; a space-time feature fusion module is designed to mine the space-time feature relationship among the left brain, the right brain and the whole brain; the advantage of the long-time sequence processing of the Transformer is used for reference, the long-time dependence relationship in the electroencephalogram sequences is captured through an attention mechanism, and the decoding precision of the emotional electroencephalogram is effectively improved; finally, a multilayer perception machine is used for emotion recognition on the feature sequence coded by the Transformer, and end-to-end emotion electroencephalogram decoding is realized.

Optimized artificial intelligence search and display systems generate and display content on the left or right side of a user to comport to and enhance right brain / left brain cognitive interactions with the search results. Search results are parsed and displayed on the most useful side of viewer. The search and display systems reduce cognitive load of the user as information is presented to the side of the user's brain that is most suited for the type of information presented. This approach not only leverages innate cognitive strengths but also promotes a more balanced and effective interaction with technology, bridging the gap between raw data and meaningful insight.

The invention discloses a brain function activation level bias detection method based on near-infrared image data. The method comprises the following steps: acquiring the near-infrared image data and extracting at least one group of brain function activation indexes of left and right brain regions from the near-infrared image data; and obtaining optimal solutions of a, b and c calculated according to the bias calculation model: respectively comparing the extracted brain function activation indexes with 0, a, b and c according to a preset bias detection process, and outputting a bias detection result. Based on the near-infrared image data, by analyzing the near-infrared blood oxygen data characteristics of the time sequences of the left brain and the right brain, the size difference of the function activation level between the left brain and the right brain is accurately recognized, and help is provided for clinical departments to timely and effectively recognize the brain function activation characteristics of mental patients; meanwhile, a feasible nerve biomarker is provided for evaluating the treatment effect and tracking the prognosis of a patient.

The application belongs to the technical field of emotion recognition, and in particular to an emotion recognition method based on left and right brain emotion lateralization; the method specifically comprises the following steps: S1, electroencephalogram signal acquisition electrode and processing: obtaining standard electroencephalogram signals; S2, time domain feature and frequency domain feature extraction of electroencephalogram signals of left and right brains: extracting Hjorth and differential entropy features; S3, calculating edge weights of a dynamic graph convolutional neural network based on feature differences of left and right brains: calculating weights by using a CRITIC weight method; S4, calculating an adjacency matrix of the dynamic graph convolutional neural network based on inter-channel correlation of electroencephalogram signals: calculating phase locking values to construct the adjacency matrix; S5, emotion recognition by using an improved dynamic graph convolutional neural network: iteration and training; the application extracts the same features from the time domain and the frequency domain of the left and right brains respectively, thereby better recognizing that the left brain is more sensitive to positive emotions and the right brain is more sensitive to negative emotions, and solving the problem of low accuracy of emotion recognition in the two dimensions AV of valence and arousal.

The application discloses a brain function activation level lateralization detection method based on near-infrared image data, comprising the following steps: acquiring near-infrared image data and extracting at least one group of brain function activation indexes of left and right brain areas from the near-infrared image data; acquiring optimal solutions of a, b and c calculated according to a lateralization calculation model; comparing the extracted brain function activation indexes with 0, a, b and c respectively according to a preset lateralization detection process, and outputting a lateralization detection result. The application is based on near-infrared image data, analyzes the near-infrared blood oxygen data characteristics of the left brain and the right brain time sequence, accurately identifies the size difference of the function activation levels between the left brain and the right brain, provides help for clinical departments to timely and effectively identify the brain function activation characteristics of mental disease patients, and provides a feasible neurobiological marker for evaluating the treatment effect and tracking the prognosis of patients.

The application provides a right brain development kit based on the whole brain development cycle, which combines fetal period neural plasticity intervention and early childhood spatial cognition training. The kit includes a building block base, an instruction building block, a mobile terminal, an image recognition server and a development board. Pregnant mothers activate the prefrontal lobe cognitive brain area through visual-spatial geometry training (such as angle trajectory operation), and the neurotransmitter is transmitted through the placenta to promote fetal right brain development; in the early childhood, the teaching medium (such as the conductive drawing device) is controlled through the building block instruction to continue the spatial thinking training. The image recognition server automatically analyzes the building block instruction and compiles the control code to realize seamless connection of the fetal-early childhood right brain development, and significantly improve the spatial cognition, creativity and learning efficiency.