15 results about "Cognitive neuroscience" patented technology

The application discloses an AIGC interaction experience quality evaluation method and system based on eye movement and brain electrical dual modalities, relates to the technical field of man-machine interaction evaluation and cognitive neuroscience, and comprises the following steps: collecting brain electrical and eye movement signals of a user performing an AIGC interaction task in an entire process, and synchronously recording all key interaction events; pre-processing the signals, dividing the signals by taking the key interaction events as anchor points, and constructing event-related time sequence data segments; for each segment, carrying out dual-modality time sequence feature extraction by using an eye movement flow encoder and a brain electrical flow encoder, fusing and splicing the extracted brain electrical and eye movement features, and obtaining an interaction fluency hidden vector; based on the brain electrical and eye movement features and the interaction fluency hidden vector, combining preset indexes, and respectively calculating original scores of fluency, satisfaction and usability; constructing a scene vector according to a user interaction scene, generating weights through a dynamic adaptive weight network, dynamically weighting each original score, and obtaining a comprehensive score of the interaction experience quality.

The application discloses a multi-modal mixed teaching resource construction method and system based on cognitive neuroscience, and the method comprises the following steps: acquiring a hidden vector of static and dynamic data in a teaching environment; constructing consistent semantic information with brain data based on the brain data of students and the hidden vector of static and dynamic data; performing denoising consistency loss processing on a backbone network Unet to construct a teaching resource generation model embedded with brain science; constructing a teaching resource generation model for an actual teaching scene based on the actual teaching environment style; inputting the static data in the teaching environment and the brain data of students into the teaching resource generation model to perform load prediction, and obtaining a teaching resource-cognitive load prediction model. The application can generate teaching resources according to learning and teaching targets and teaching contents, and reduce the cognitive load of students. The application can be widely applied to the technical field of intelligent auxiliary teaching as a multi-modal mixed teaching resource construction method and system based on cognitive neuroscience.

The application discloses a kind of induced mood pulse sound feature screening method, belong to cognitive neuroscientific technology field, solve the problem of lack of effective screening in prior art mood pulse sound feature.A kind of induced mood pulse sound feature screening method, the method comprises: under the condition of subject within experiment, according to the scene of inducing mood, set multiple pulse sounds;The independent variable of the pulse sound includes sound frequency, repetition frequency and relative loudness;Different sound frequency, repetition frequency and relative loudness are cross designed, form multiple sound stimuli;Using multiple sound stimuli, a plurality of subjects are experimented respectively;And the physiological signal of each subject under each sound stimulus, emotional valence score and arousal degree score are acquired;Based on the physiological signal of all subjects under all sound stimuli, emotional valence score and arousal degree score, the feature screening result of corresponding induced mood pulse sound is obtained.

The application discloses a real-time sound feedback method and equipment based on gait event triggering and belongs to the field of walking cognitive experiment equipment. The equipment comprises a sensing unit, a lower computer, an upper computer and a sound conveying system; the sensing unit comprises a pressure sensor installed in a shoe cover and dispersedly transmitting pressure by a hard foam pad, the heel of a subject rhythmically presses the hard foam pad during walking, and the sensing area of the pressure sensor and the protective film are kept in a detachable pressure contact during the heel strike; the lower computer collects the voltage signal output by the pressure sensor in real time, maps the voltage signal into a standard pressure value and then transmits the standard pressure value to the upper computer in real time, the upper computer analyzes the standard pressure data stream, and when it is determined that the heel strike event occurs, the upper computer immediately controls the sound conveying system to emit sound stimulation with a preset frequency, amplitude and time length. The application provides a multimodal platform interconnection and closed-loop system integration scheme and can provide support for psychology and cognitive neuroscience research.

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 invention discloses a deep evaluation method and device suitable for a role-playing agent, relates to the technical field of artificial intelligence, and aims at relatively accurately evaluating the consistency between the role-playing agent and a real role in multiple scenes. The device comprises (1) a role characterization module based on cognitive neuroscience, namely extracting character attributes, memory maps and the like of roles according to a role summary described by a natural language; (2) a static benchmark question set module which comprises a general question evaluation set generated according to characters, value, emotion characteristics, cognitive styles and other characteristics; generating a customized question evaluation set according to the character attributes and the memory atlas; and generating a scene evaluation set according to the typical dialogue scene. And (3) a multi-agent iteration depth problem generation module which uses the creator agent to transform and complement problems in the static reference problem set, uses the verifier agent to verify the rationality of the problems proposed by the creator agent, and generates a depth evaluation problem. And (4) a consistency analysis module.

The invention relates to a multi-stage cognitive modeling and parameter optimization method based on a visual pairing comparison task, and aims to solve the technical problems of incomplete cognitive process modeling, weak crowd distinguishing ability, task parameter empirical and the like in the existing VPC task evaluation technology. Precise evaluation of the cognitive function and optimization design of task parameters are achieved through a five-step method, wherein VPC task parameterization design and eye movement data collection are carried out; constructing a'familiarity accumulation-memory attenuation-novelty attention and utility 'three-stage cognitive model; performing backstepping on individual free parameters based on maximum likelihood estimation; carrying out multi-population cognitive parameter distribution modeling and subtype division; and task parameter closed-loop optimization based on simulation and efficiency discrimination. According to the method, quantitative distinguishing of learning efficiency, memory stability and novel preferences is achieved, cognitive parameter recovery precision and multi-crowd distinguishing efficiency are improved, a standardized and transplantable technical path is provided for early screening of cognitive impairment and crowd typing, and the method is suitable for cognitive neuroscience research and clinical cognitive evaluation scenes.

1. The name of the design product: integrated transcranial magnetic stimulation device (DCS series). 2. The use of the design product: for the diagnosis and treatment of related diseases in neurology and rehabilitation department, psychiatry department, and the research of cognitive neuroscience and brain function. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.

This invention relates to the fields of basic research in cognitive neuroscience, large-scale artificial intelligence model architecture, brain-computer interfaces, and educational cognitive assessment. Specifically, it relates to a unified four-dimensional discrete-time spatiotemporal modeling method for basic neural information elements of the brain (neural impulses, neuronal cluster firing, and sensory neural signals). This method is based on a homologous and unified underlying theoretical framework and is an engineering embodiment of a unified four-dimensional discrete-time spatiotemporal cognitive system. It shares a completely homologous, isomorphic, and unified mathematical framework with the inventor's previously submitted "A Token Tagging Method and Cognitive System for Input Data of a Large Language Model." The only difference is that the AI ​​patent processes computational information elements (Tokens), while this patent processes biological information elements (neural impulses, sensory signals, and neuronal firing events). Together, they constitute a unified underlying architecture covering biological cognition and artificial intelligence.

The invention discloses a cross-modal sEEG-to-iEEG generation method based on a multi-scale condition regularization stream, and the method comprises the steps: obtaining sEEG and iEEG signals which are collected synchronously, and carrying out the preprocessing, and obtaining qualified condition input and target signals; then, a multi-scale conditional regularization flow model containing a multi-scale framework, multiple processes and a core reversible transformation module is constructed, and iEEG signal distribution conversion is achieved through reversible channel hybrid operation and a conditional affine coupling layer; training the model by using a maximum likelihood estimation method, and optimizing parameters until convergence; and after a new sEEG signal is preprocessed, inputting the trained model, and generating a corresponding iEEG signal through inverse transformation. According to the method, complex probability distribution is modeled through conditional regularization flow, mode collapse is avoided, nerve activity randomness is reserved, deep temporal lobe full-region iEEG generation is achieved by combining a multi-scale framework with a self-attention mechanism, high-fidelity iEEG is generated in a non-invasive mode, medical risks and cost are reduced, and the method is suitable for large-scale popularization and application. And reliable technical support is provided for epileptic focus positioning and cognitive neuroscience research.

The invention discloses a real-time sound feedback method and equipment based on gait event triggering, and belongs to the field of walking cognition experiment equipment. The equipment comprises a sensing unit, a lower computer, an upper computer and a sound transmission system, the sensing unit comprises a pressure sensor which is arranged in the shoe cover and is used for dispersing and transmitting pressure by a hard foam pad, a heel exerts rhythmic pressure on the hard foam pad in the walking process of a tested person, and a sensing area of the pressure sensor and the protective film are kept in detachable pressed contact when the heel touches the ground; the lower computer collects a voltage signal output by the pressure sensor in real time, maps the voltage signal into a standard pressure value and transmits the standard pressure value to the upper computer in real time, and the upper computer analyzes standard pressure data flow and immediately controls the sound transmission system to give out sound stimulation with preset frequency, amplitude and duration when it is judged that a heel landing event occurs currently. The invention provides a multi-modal platform interconnection and closed-loop system integration scheme, which can provide support for psychological and cognitive neuroscience research.

1. Name of the product in this design: Transcranial Magnetic Stimulation Robot Device. 2. Intended use of this design: for the diagnosis and treatment of diseases related to neurology and rehabilitation, and psychiatry, as well as for research in cognitive neuroscience and brain function. 3. The key design feature of this product is its shape. 4. Images or photos that best illustrate the design key points: composite state diagrams. 5. Component 1 is the treatment chair, component 2 is the main unit of the device, and component 3 is the display screen bracket.

1. The name of the design product: Transcranial magnetic stimulation device (S series). 2. The use of the design product: For the diagnosis and treatment of related diseases in neurology and rehabilitation department, psychiatry department, and the research of cognitive neuroscience and brain function. 3. The design points of the design product: In shape. 4. The picture or photo that best indicates the design points: Perspective view.

The application relates to the field of cognitive neuroscience and discloses a cognitive load evaluation method based on brain intelligence technology, which comprises the following steps: S1. 4-channel electroencephalogram (EEG) signal and electrooculogram (EOG) signal acquisition; S2. EEG signal and EOG signal preprocessing and feature extraction; S3. A variational autoencoding generative adversarial enhancement method for the EEG signal can expand and enrich an original data set by generating synthetic data, thereby improving the performance of a training model; S4. A dual-mode feature fusion model for the EEG and EOG based on a denoising autoencoder can obtain complementary features, enriches a feature space and is helpful to more accurately describe the cognitive state of an individual; and S5. An improved cognitive load classification method of a support vector machine (SVM) optimized by a seagull algorithm can better capture cognitive load-related features, improve the accuracy and robustness of a classifier, and the optimized model can more accurately judge the cognitive load level of an individual, thereby providing a reliable basis for cognitive load evaluation.

The invention discloses a depression brain function connection analysis method combining time sequence coding and a synaptic graph Transformer, and the method comprises the steps: based on resting state functional magnetic resonance imaging data, firstly extracting blood oxygen level dependence signals of a plurality of predefined brain regions, constructing time sequence features, then constructing a function-dynamic synaptic graph neural network model FDSyn-GNN, and finally, constructing a function-dynamic synaptic graph neural network model FDSyn-GNN and a function-dynamic synaptic graph neural network model FDSyn-GNN and a function-dynamic synaptic graph neural network model FDSyn-GNN; the method comprises the following steps: performing time sequence modeling on a brain region neural activity sequence to capture a dynamic evolution rule, modeling a dynamic connection relationship between brain regions, performing structured representation learning, and finally realizing efficient analysis on a brain function connection mode by fusing time sequence characteristics and synaptic connection characteristics. The method provided by the invention can provide support for subsequent pattern recognition, biomarker positioning or other cognitive neuroscience related tasks, has good dynamic modeling ability and structural interpretability, is helpful for improving objectivity and reliability of brain function analysis, and provides objective and valuable auxiliary information for clinical research and decision-making.