474 results about "Human brain" patented technology

The invention provides an unmanned cluster brain-like navigation map fusion construction and cooperative positioning method. The method comprises the following steps: acquiring multi-modal perception data, respectively acquiring vision, sound wave and pose information through a binocular camera, a radar and an inertial sensor, simulating a human brain nerve coding mechanism, and generating a pulse sequence and a feature vector in combination with spatio-temporal information; constructing a multi-modal coding unit into a hypergraph node, and based on a dynamic hyperedge connection topological relation, aggregating spatial-temporal characteristics through a heterogeneous hypergraph convolutional network to generate a high-order brain-like semantic map of a single agent; sharing a local brain-like map by multiple agents through distributed communication, detecting geometric and semantic conflicts of an overlapped region, performing space-time alignment based on an anchor point reference, eliminating feature contradictions by utilizing probability distribution matching, and generating a global consistent high-confidence brain-like map; and outputting the optimal position estimation. Through bionic neural coding, heterogeneous hypergraph modeling and multi-agent collaborative optimization, establishment and positioning of a high-order brain-like map in a dynamic unknown environment are realized.

The invention provides an electromagnetic photoacoustic synchronous integrated brain function imaging method and device, and relates to the field of multi-mode brain function imaging research. According to the invention, electroencephalogram, magnetoencephalogram, functional near infrared spectrum and functional ultrasonic imaging technologies are fused, important advantage complementation is formed in signal specificity and temporal-spatial resolution, and neural activity can be described from the angle of human cranial nerve electrophysiology and neural metabolism can be observed from hemodynamic information at the same time. And electrophysiology and hemodynamic activities of the brain can be synchronously recorded.

The invention relates to a multi-agent cooperation system based on a large model and a bionic human brain structure, and the method comprises the steps: an interaction layer is used for obtaining a natural language instruction inputted by a user side, and analyzing the natural language instruction into a structured task request; the application layer is used for generating a sub-task list according to the structured task request and determining a target resource demand; the support layer is used for dynamically allocating computing resources according to the sub-task list and target resource requirements and generating a resource allocation result; the cognitive layer is used for generating an environment state and a recommendation strategy according to the external environment data and the context of the natural language instruction; the decision-making layer generates a co-situation response and a task execution instruction according to the environment state, the recommendation strategy and the long-term portrait of the user; the execution layer is used for performing task execution according to the task execution instruction and generating a task execution result; and the model layer is used for determining a target model according to the task request and the model load state, and executing a task to generate a model processing result. According to the invention, the agent collaboration efficiency in a complex scene is improved.

The invention discloses a road target detection method based on brain-like dual-channel feature fusion, and belongs to the technical field of image recognition. According to the method, a target recognition and spatial positioning dual-channel mechanism based on a human brain vision system is provided, a traffic scene video stream is converted into a pulse density map, the pulse density map is input into a bionic dual-channel network, feature extraction is performed from a target recognition channel and a spatial positioning channel, then adaptive dynamic feature fusion is performed through a dynamic fusion module, and a traffic scene video stream is obtained. By strictly mapping a human brain visual path, enhancing the theoretical reliability of the technical scheme and introducing pulse convolution, high real-time performance and calculation efficiency are ensured, the precision, speed and power consumption of traffic target detection are balanced, and the method is suitable for a vehicle-mounted traffic target detection task and meets the requirement of an automatic driving system for target detection. Experiments prove that the mAP of the method for small target detection is improved by 15.8%, and the energy consumption is reduced by 40%.

The invention relates to the technical field of artificial intelligence, and discloses a brain-like multi-mode emotion recognition network, a brain-like multi-mode emotion recognition method and an emotion robot, and the brain-like multi-mode emotion recognition method comprises the steps: converting multi-mode emotion information into a pulse sequence; on the basis of the obtained pulse sequence, constructing a continuous pedigree emotion representation space, and realizing continuous representation of an emotional state; a continuous pedigree emotion representation space is utilized to generate pulse codes of mixed emotions, and a complex emotional state is represented; according to the pulse codes of the mixed emotions, establishing an emotional state conversion probability model, and describing a conversion relation between the emotions; based on an emotional state transition probability model, realizing prediction and processing of an emotional gradual change process, and capturing subtle emotional changes; according to the method, the working principle of human brain neurons can be used for reference, fine representation and accurate prediction of the human emotional state are achieved, and meanwhile high calculation efficiency and biological interpretability are achieved.

The invention discloses a brain-like small sample behavior recognition algorithm based on a motion-guided attention mechanism, and relates to the technical field of small sample behavior recognition of deep learning tasks. The brain-like induction reasoning mechanism algorithm module based on the attention mechanism simulates a human brain recognition mechanism and mainly comprises a self-attention motion feature guiding module, a video-level global perception module and a cross attention motion feature guiding module. The self-attention motion feature guiding module is used for capturing key features of a motion-related region between each video frame; the video-level sensing module continuously pays attention to key action related region features of the whole video; the cross attention motion feature guiding module compares key features of motion related areas among different videos and establishes association among the motion related areas; compared with some existing classic methods, the constructed model can significantly improve the detection precision of small sample behavior recognition, and an optimal result is achieved.

Belongs to the technical field of biomedical engineering, and discloses a method, a device, equipment, a product and a storage medium for identifying skull defects by constructing a characteristic spectrum through a nuclear magnetic image to solve the problem that a defect area is mistakenly divided into skull areas in existing automatic skull segmentation. Converting the human brain nuclear magnetic image to an anatomical space based on the plurality of anatomical points, and implementing preliminary segmentation to obtain a primary segmentation region of the skull; calculating coordinates of voxel points in the primary segmentation region in an anatomical point coordinate system, establishing a matrix, calculating a plurality of nuclear magnetic gray statistical characteristics of the primary segmentation region, and drawing a characteristic spectrum; and performing multi-condition feature threshold judgment on the feature map to obtain a logic binary map, and extracting a region of which a connected domain is greater than a specified threshold as a skull defect region. According to the method provided by the invention, a plurality of gray features are utilized, the misrecognition of the skull in the prior art is effectively corrected, the method can be further used for judging the filling tissue of the skull defect area, and the skull defect recognition capability can be improved.

The invention discloses a motion intention analysis method and device which are used for performing motion intention recognition on electroencephalogram signals. The method comprises the following steps: acquiring an electroencephalogram signal at a human brain electrode position, and performing information compression processing on the electroencephalogram signal to obtain potential state information of the electroencephalogram signal; the electroencephalogram signals are obtained based on a plurality of electrode signals; the potential state information is used for representing spatial-temporal characteristics of the electroencephalogram signals, the spatial-temporal characteristics are used for representing collection moments and collection channels of the multiple electrode signals, and each electrode signal in the multiple electrode signals corresponds to one collection channel; then, feature data of the electroencephalogram signals are extracted from the potential state information, the motion intention corresponding to the electroencephalogram signals is analyzed according to the feature data, it is not needed to judge whether cognitive distraction exists or not on the electroencephalogram signals, motion intention recognition can be directly conducted on the electroencephalogram signals, and therefore the motion intention recognition efficiency can be improved.

The invention discloses a schizophrenia early warning and evaluation system based on human brain multi-region signals, and relates to the technical field of early warning and evaluation, and the system comprises a neural connection analysis unit which is used for recognizing neural connection characteristics of a brain network based on biomarker characteristics, comparing the neural connection characteristics with a database, and determining the neural connection characteristics of the brain network; clustering individuals with similar neural connection features according to a comparison result to generate a risk group; the emotion perception analysis unit is used for acquiring facial emotion states of the risk group in a preset time period and predicting evolution trends of neural connection features in different facial emotion states; and the early warning evaluation unit is used for inputting the evolution trend of the neural connection characteristics into a pre-constructed evaluation model, outputting a risk level evaluation result and formulating an early warning measure based on the risk level evaluation result. According to the method, neural connection features and emotion perception analysis are combined, so that early-stage neural connection abnormity and emotion turning intervals of schizophrenia can be accurately identified and positioned.

The invention relates to a multi-center applicable automatic segmentation method for a brain structure of a human brain magnetic resonance image, and the segmentation method specifically comprises the steps: obtaining MRI data of different platforms, and carrying out the N4 bias field correction and image standardization processing of the MRI data; u-Net is used as a basic framework, a self-attention mechanism module is fused, and an MRI brain image structure segmentation network model suitable for multiple platforms is constructed; and inputting the processed MRI data into a segmentation network model, training the segmentation network model by using transfer learning and domain adaptation technologies, performing uncertainty estimation on an output result, and further performing model training by using screened high-quality unlabeled data so as to realize automatic segmentation of the brain structure of the human brain magnetic resonance image. Compared with the prior art, the method has the advantages that brain structures of different magnetic resonance imaging platforms can be accurately and quickly segmented, a more universal, efficient and reliable tool is provided for the field of brain image analysis, and wider application and research exploration are facilitated.

The invention discloses a depression diagnosis method based on a K-GAT network, and the method comprises the following steps: 1, obtaining the brain region thickness of each brain region based on a brain magnetic resonance image; constructing a brain topological graph by taking the brain regions as nodes, taking correlation connection between the brain regions as edges between the nodes and taking the extracted thicknesses of the brain regions as features of the nodes; the brain topological graph comprises a brain topological graph of a depression patient and a brain topological graph of a healthy person; step 2, constructing a K-GAT network for classifying the brain magnetic resonance images; screening whether there are many brain region connections, and if there are many brain region connections, determining that the brain magnetic resonance image of the patient with depression is the brain magnetic resonance image; otherwise, a healthy human brain magnetic resonance image is obtained; and step 3, classifying the brain magnetic resonance images by adopting the trained K-GAT network, and distinguishing a depression patient from a non-depression patient. The method comprises the following steps of: obtaining a brain topological graph, inputting the brain topological graph into a K-GAT network, and training the K-GAT network to obtain a trained K-GAT network.

The invention provides a voice recognition method and device based on a brain-like model, electronic equipment and a storage medium, and the method comprises the steps: obtaining a whole-brain network topological structure according to a brain function network generated by human brain image data, and carrying out the recognition of a whole-brain network through employing a multi-class neuron model as a node and a synaptic plasticity model as an edge, constructing a multi-brain-region pulse neural network as a brain-like model; constructing a speech recognition framework of the brain-like model; electromagnetic intervention is applied to different brain areas of the brain-like model, optimal electromagnetic intervention parameters are determined by analyzing the voice recognition accuracy of the brain-like model before and after electromagnetic intervention, and brain-like model voice recognition is carried out according to the optimal electromagnetic intervention parameters. According to the invention, the speech recognition performance of the brain-like model can be effectively improved, the biological interpretability and neural information processing capability of the brain-like model are further improved, and the development of brain-like intelligence in the application of a mode recognition task is promoted.

The invention belongs to the field of food quality evaluation, and provides a delicate flavor synergistic effect evaluation method based on an electroencephalogram monitoring technology. Psychological or physiological factors such as subjective judgment, forced selection or scoring, individual umami cognition and umami sensitivity difference of sensory officers may affect the accuracy of sensory experiment results; the invention discloses methods such as umami sample selection and strength evaluation, electroencephalogram signal acquisition experiment normal form, quantitative evaluation and statistical analysis, feature extraction and response difference analysis, and the like, and the response mechanism of typical umami compounds in the human brain is explored by taking the response of people to umami signals as a research object and taking electroencephalogram signal detection analysis as a main method. A new theoretical basis is provided for quantitative evaluation based on the human palatable taste intensity, and the method is worthy of wide popularization.

Techniques are described for determining cognitive impairment, an example of which includes accessing a set of epochs of magnetoencephalography (MEG) data of responses of a brain of a test patient to a plurality of auditory stimulus events; processing the set of epochs to identify parameter values one or more of which is based on information from the individual epochs without averaging or otherwise collapsing the epoch data. The parameter values are input into a model that is trained based on the parameters to determine whether the test patient is cognitively impaired.

The invention discloses a method for detecting interpersonal nerve synchronization under audio-visual stimulation, which comprises the following steps of: designing a double-person super-scanning experiment normal form, collecting double-person electroencephalogram signals, and constructing a database of visual stimulation and auditory stimulation; preprocessing the data in the database to obtain electroencephalogram signals of four different frequency bands delta, theta, alpha and beta, extracting the electroencephalogram signals of the alpha frequency band, segmenting the electroencephalogram signals, and calculating a correlation ISC value between subjects of each segment; an intra-brain network and an inter-brain network are constructed by constructing a functional connection matrix, and the similarity of the intra-brain network and the global efficiency of the inter-brain network are calculated, so that the cooperation and synchronization degree between neural activities of subjects under visual stimulation and auditory stimulation is effectively evaluated. The invention further discloses a system for detecting interpersonal nerve synchronization under audiovisual stimulation. According to the method, the difference of subjects is reduced by standardizing experimental conditions, and the influence of single sensory stimulation on an intracerebral network activation mode is studied.

The invention discloses a multi-agent reasoning framework construction method based on a meta-cognitive thought, which is characterized in that a multi-agent reasoning framework is adopted to simulate a human brain thinking model, and the method comprises the following steps: firstly, constructing a meta-cognitive agent training data set, and training an open source large language model based on a thinking injection training normal form; the method comprises the following steps of: constructing an open-source large language model to become a meta-cognitive agent with thinking ability, constructing a central agent training data set, training the open-source large language model based on a supervised training normal form to accurately evaluate the difficulty degree of a task, and finally, taking the open-source large language model which is not additionally trained as a cognitive agent to accurately evaluate the task difficulty degree. A multi-agent reasoning framework capable of effectively simulating a human meta-cognitive thinking model is constructed by combining a meta-cognitive agent and a central agent. Compared with the prior art, the method has the capability of improving the model to solve complex problems, and effectively relieves the two limitations of hindering the reasoning capability of the large language model, lacking a serial calculation mode and lacking the'thinking 'capability.

A method for delivering a therapeutic agent to a human brain by delivering systemically a therapeutic agent and applying low intensity pulsed ultrasound (“LIPU”) to the brain in the presence of circulating microbubbles. The therapeutic agent is delivered prior to applying LIPU if it has a plasma half-life of ≥1 h, and the therapeutic agent is delivered after applying LIPU if it has a plasma half-life of <1 h. In a second disclosed method, a first therapeutic agent is delivered systemically, LIPU is applied to the brain in the presence of circulating microbubbles, and a second therapeutic agent is delivered systemically. Also provided is a method for treating a brain disorder in a human by delivering a therapeutic agent systemically, administering a microbubble solution, and applying LIPU to the brain for a period of 1 min. to 30 min.

The basis of calculating memory capacity of modern computing is bit. Thus, the number of bits is the unit of information quantity of modern communication. The number of neurons (node number) in the neural networks in human brain is not the unit of the memory capacity of the human being. The complexity of neural network is much greater than the bit capacity. Hence, the current AI, which tries to imitate the human brain using computing with the basis on bits, performs inherently different processing of information from the human brain. In addition, computing based on bit number is always facing the limitation of integration. The present disclosure provides a system of information memory without relying on bits using three-dimensional neural networks. By replacing the electrical connection of non-volatile memory cells, which are distributed in a three-dimensional array, the mechanism of the information processing of the human brain can be imitated.

The invention relates to the technical field of transcranial electrical stimulation, and discloses a human brain transcranial stimulation circuit and a human brain transcranial stimulation method, and the method comprises the steps: obtaining the brain state data of a to-be-stimulated user; according to the brain state data, analyzing a connection relationship between brain regions, and constructing a brain network map; based on the relationship between the node degree value and the medium centrality value of each node in the brain network atlas and the corresponding preset value, analyzing an abnormal region in the brain network atlas in combination with a preset anomaly detection model to obtain a brain target region; generating a stimulation strategy according to the brain target area; optimizing the stimulation strategy in combination with the brain state data to realize closed-loop control of the stimulation strategy; by combining the real-time state of the user, dynamically generating the stimulation signal, accurately positioning the brain target area and optimizing the stimulation strategy, the brain target area can be stimulated more effectively, the treatment process is more scientific and reasonable, and the treatment effect and efficiency are improved.

The embodiment of the invention discloses a brain deep part electrical stimulation operation planning system and method based on a human brain network and a Lead-DBS technology, and the system and method are applied to operation planning of neuropsychiatric diseases. The data preprocessing module is used for denoising, correcting and standardizing the patient data to generate preprocessed data; the brain network model construction module is used for determining a functional connection matrix and a structural connection matrix based on the preprocessed data so as to construct an individualized brain network model; the electrode positioning module is used for generating an implantation target spot of a DBS electrode based on an individualized brain network model; the stimulation parameter optimization module optimizes stimulation parameters of the DBS based on the individualized brain network model and the electrophysiological data; the postoperative evaluation module compares the postoperative image with the preoperative plan to verify the actual implantation position and stimulation effect of the DBS electrode; the dynamic adjustment module is used for dynamically adjusting stimulation parameters.

The determination of the probability of gas hydrate formation using artificial neural network (ANN) and multilayer perceptrons (MLPs) models. ANN generally refers to a network of interconnected neurons (also referred to as “nodes”) that model the neurons in a human brain. An MLP refers to a feed-forward network having a specific arrangement of neurons and includes an input layer, one or more hidden layers, and an output layer. Input data such as temperature, pressure, gas mixture composition, and indicators of gas hydrate formation may be obtained and preprocessed for use in training and testing. The ANN and MLPs may be trained using a training set of the input to output a probability of gas hydrate formation. The trained ANN and MLP models may then be used to determine a gas hydrate formation probability for new data associated with a pipeline transporting a gas mixture.

The invention discloses a compound-target binding affinity prediction method based on multi-modal feature fusion, and relates to the technical field of compound activity prediction, and the technical key point is that the method comprises the steps of data acquisition, multi-modal feature characterization, multi-modal feature extraction and fusion, and affinity prediction to predict the compound activity. The problems of long time consumption, high cost, low efficiency and the like of compound activity prediction are solved through assistance of an artificial intelligence algorithm, and information can be processed in parallel in a self-adaptive and self-learning manner by referring to a multi-layered structure of a human brain and a layer-by-layer analysis processing mechanism of neuron information interaction. The intensity information of the interaction between the compound-target pair is provided by combining the affinity, the affinity between the compound and the target is predicted through a deep learning method, and the specific biological activity and key action target of the compound are analyzed.

The invention relates to a human brain extraction method and system for enhancing anatomical structure and surface information perception, electronic equipment and a storage medium, and belongs to the technical field of image processing. The anatomical structure sensing module aims to enhance understanding of global brain tissues, and the surface information sensing module emphasizes a local mode of a brain surface so as to solve challenges brought by low-level inter-class similarity of voxel intensity between brain tissues and non-brain tissues; furthermore, a novel local-global feature enhancement module is employed to enhance the association between local surface patterns and global brain tissue, providing a robust local-global representation that facilitates precise brain extraction. According to the method, the anatomical structure and surface information perception of the human brain is enhanced, so that accurate brain extraction is realized.

The invention discloses a large model real-time training system based on a human brain mechanism, and the system comprises an information input module which is used for receiving and processing various types of data, and carrying out the standardization processing; the temporary storage module is used for temporarily storing data for a certain time length; the importance evaluation module is used for evaluating the importance of the input data in the temporary storage module in real time; the memory classification storage module is used for classifying and storing the data into short-term, medium-term and long-term memories according to the importance of the data; the real-time fine tuning or training module is used for performing small-batch real-time training and updating model parameters by adopting a LoRA technology; and the model updating control module is used for monitoring the performance of the model in real time and automatically rolling back when abnormity occurs. According to the large model real-time training system based on the human brain mechanism, through a real-time fine tuning or training mechanism, the response speed and accuracy of the large model are remarkably improved, resource consumption is reduced, and meanwhile, the model can better adapt to a dynamic environment through an introduced memory and forgetting mechanism.

The utility model provides an intelligent head-wearable physiological signal monitoring device. The intelligent head-wearable physiological signal monitoring device comprises a mainboard structure, the two ends of the first adjusting part are adjustably connected to the main plate structure, and the first adjusting part is arranged in the first circumferential direction; the first end of the second adjusting part is connected with the main plate structure, the second end of the second adjusting part is connected with the first adjusting part, and the second adjusting part is arranged in the second circumferential direction in a length-adjustable mode; the third adjusting part is arranged on the second adjusting part, and the third adjusting part is arranged in the third circumferential direction in a length-adjustable mode; the mainboard structure, the second adjusting part and the third adjusting part are all provided with detection electrodes. According to the intelligent head-wearable physiological signal monitoring device, through cooperation of the mainboard structure, the first adjusting part, the second adjusting part, the third adjusting part and the detection electrode, the effectiveness of human brain wave detection and the wearing comfort are guaranteed, and the intelligent head-wearable physiological signal monitoring device can be applied to various use scenes; and the method has adaptability and economical efficiency.

In certain embodiments a light therapy system (e.g., phototherapy device), such as for treatment of Alzheimer's disease, depression, dementia, short-term memory, or for improved learning, improved athletic performance or improved cognitive performance, is provided where the light system comprises a blue light source that operates at a frequency in the range from 20 to 50 Hz (preferably around 40 Hz), whereby the system enables retinal ganglion cells of a human to be exposed in order to stimulate brain waves (gamma oscillations in the human's brain).

The Neuromorphic Emulation Constructor AI (NECA) is an artificial intelligence technology for building a Personal Neuromorphic Emulation (PNE), a detailed model of an individual's brain that is anatomically correct, capable of learning and plasticity, and able to emulate the person's brain electrical activity and behavior. The NECA accesses the neuroscience literature through automated search and acquisition technologies such as Google Scholar, and it assembles the knowledge of the brain's structure and function in ways that specify how to build and test the PNE.

The present invention addresses the problem of providing: a method for producing a three-dimensional cell culture including human-derived neurons, astrocytes, and microglia that can mimic the function of a human brain, and a method for culturing for said three-dimensional cell culture; a three-dimensional cell culture including human-derived neurons, astrocytes, and microglia that can mimic the function of a human brain; and a method for evaluating a test substance using the three-dimensional cell culture. The present invention provides a method for producing a three-dimensional cell culture, the method including: a step for adding human-derived astrocytes, human-derived neurons, and human-derived microglia to a culturing container prior to co-culturing of the cells; and a step for co-culturing the astrocytes, the neurons, and the microglia in the culturing container, wherein the proportion of the astrocytes to all cells added to the culturing container is 30% or more.

The invention relates to the field of computer vision and cognitive neuroscience, in particular to a human brain cognitive text description generation method and system for a natural image, and the method comprises the steps: pre-constructing an fMRI-Image mapping model which enables an fMRI signal to form mapping with image feature vectors and text feature vectors obtained through CLIP for various complex scenes; the method comprises the following steps: acquiring an fMRI signal generated by a human brain on the basis of a stimulation image, preprocessing the acquired fMRI signal to obtain a processed fMRI signal, inputting the preprocessed fMRI signal into a constructed fMRI-Image mapping model to obtain an image feature vector and a text feature vector, and establishing an image feature vector and a text feature vector; and inputting the image feature vector and the text feature vector output by the fMRI-Image mapping model into a pre-trained language mapping model to generate an image text description, so that visual semantic features can be extracted from a complex natural scene, and the text description of a natural image can be accurately generated.

The invention discloses a result verification method for a transcranial magnetic stimulator calibration device, and relates to the technical field of medical equipment detection, and the method comprises the following steps: collecting pulse magnetic field parameters outputted by a TMS therapeutic instrument in real time through constructing a multipath parallel magnetic field distribution perception array composed of a plurality of coil type magnetic field sensors; designing a three-dimensional calibration platform to perform multi-dimensional dynamic calibration on the magnetic field distribution sensing array, calibrating the relationship between the induced electromotive force and the magnetic induction intensity of the sensor, and verifying whether the measurement error is less than + / -0.1% or not; and comparing the actually measured magnetic field data with the human brain magnetic field distribution data simulated by the finite element simulation model, analyzing the difference value between the simulation data and the actually measured data, and correcting the system according to the data deviation degree. According to the transcranial magnetic stimulator calibration device and the result verification method, real-time acquisition and high-precision calibration of dynamic parameters are realized through a multi-path synchronous magnetic field sensing array, and the calibration reliability is verified based on finite element simulation and actually measured data comparison.