441 results about "Visual Stimulations" patented technology

According to the multi-mode visual stimulation and intelligent self-adaption combined myopia prevention and control system based on the AR glasses, all-weather, personalized and full-scene myopia suppression is realized through multi-module cooperation of sensory visual stimulation, visual function training, AI scene dynamic recognition and directional intervention, self-adaption closed-loop feedback, cloud / offline deployment and the like; through multi-dimensional visual stimulation and real-time physiological monitoring, eye axis growth is accurately regulated and controlled, myopia prevention and control precision is improved, out-of-focus and contrast setting are automatically optimized based on immediate refraction and adjustment feedback, personalized intervention is enhanced, lightweight AI and cloud pre-calculation are introduced, the endurance of equipment is effectively prolonged, an existing AR platform is compatible, the development process is simplified, and the development efficiency is improved. And the user compliance is enhanced.

The present disclosure describes systems, devices, and methods of benefiting a cognitive state or function of a brain of a subject. The systems can include a portable system comprising a single headset device to be worn by a subject, comprising a pair of glasses configured to provide a visual stimulus to the subject, and a pair of supra-aural headphones configured to provide an auditory stimulus to the subject.

The invention relates to the technical field of medical rehabilitation, and discloses a vestibular function and cognitive function rehabilitation training system and method based on virtual reality, and the rehabilitation training system comprises a hardware platform, a software platform and a server. The hardware platform comprises VR interaction equipment, a head motion tracking sensor and calculation and display equipment; the software platform is integrated with a user management module, a visual stimulation module, a vestibular rehabilitation training module, a cognitive evaluation and training module, a dynamic visual acuity evaluation module and a data management and analysis module. By combining black and white chess grid visual stimulation, standardized vestibular rehabilitation tasks, spatial cognitive testing and DVA objective monitoring, synchronous evaluation and personalized collaborative intervention of vestibular functions and cognitive functions of dizzy patients are realized. According to the method, the problems of boring traditional rehabilitation means, subjective evaluation and lack of quantitative feedback are solved, and the scientificity, interestingness and curative effect testability of rehabilitation training are remarkably improved.

The invention provides a cognitive impairment assessment system and method based on emotion interaction multi-mode physiological signals. The cognitive impairment assessment system comprises a multi-mode physiological signal acquisition module, an emotion interaction module based on emotion recognition and a cognitive impairment assessment module. The multi-modal physiological signal acquisition module acquires multi-dimensional physiological signals such as electroencephalogram, electro-oculogram and electrocardio through a flexible sensor, and a physiological signal coupling network is constructed; the emotion interaction module based on emotion recognition adopts a heterogeneous graph neural network to analyze emotion response under visual stimulation, recognizes deviation between emotion and a preset stimulation type through a comparison model, dynamically optimizes visual stimulation parameters, and forms closed-loop emotion interaction; and the cognitive impairment evaluation module combines an SE-CNN deep learning model and fuses multi-modal physiological data and emotion interaction characteristics to realize accurate classification of cognitive impairment. According to the method, the accuracy and individualized efficiency of emotion recognition can be effectively improved, and a new normal form and a new method are provided for early cognitive impairment assessment.

The invention discloses an attention monitoring method and related equipment. Monitoring is realized through coordination of five steps of target detection, risk assessment, visual stimulation, electroencephalogram decoding and attention guidance. The method specifically solves the defects in the prior art: electroencephalogram signal decoding is adopted to replace a subjective report, so that emotion and cognition deviation is avoided, instantaneous attention monitoring is realized, and the hysteresis and non-objectivity of a subjective method are overcome; visual stimulation and electroencephalogram response association is used for replacing operation behavior inference, a mouse or touch equipment is not needed, remote monitoring is adapted, and insufficient real-time performance caused by operation delay is eliminated; the focus target is decoded through the SSVEP response frequency, the limitation of the eye tracker on the monitoring angle and the fixed posture is eliminated, and the method is suitable for the complex scene with multiple screens, multiple angles and multiple postures. Meanwhile, key target list dynamic generation and guide strategy automatic activation form closed-loop management, the key target omission risk is significantly reduced, and the man-machine cooperative monitoring efficiency and reliability are improved.

The invention discloses a cognitive disorder nostalgic audio-video intervention therapy method, which comprises the following steps of: building a scene containing nostalgic music art, classical movies and retro original sound, playing matched audio and visual materials to a patient, and guiding the patient to perform structured intervention activities such as puzzle, theme recall or emotion sharing. Therefore, memory is stimulated, cognition is exercised, and emotion expression is promoted; according to the invention, by integrating three layouts of audios of nostalgic music art, classical movies and retro original sounds with matched visual elements, a multi-scene nostalgic sensory environment is constructed, and auditory and visual channels of a patient can be synchronously stimulated; compared with single auditory or visual stimulation, the compound sensory stimulation can more effectively activate a plurality of brain areas related to memory and emotion in the brain, so that long-term memory of deep sleep is more intensively aroused, and the strength and awakening efficiency of reminiscence stimulation are greatly improved.

The invention belongs to the field of brain-computer interfaces, and particularly relates to a decision-making brain-computer method and device based on virtual reality induction, which combines two psychological decision-making tasks of auditory stimulation and visual stimulation and utilizes virtual reality equipment to create a decision-making brain-computer interface normal form of panoramic interaction, so that a subject can fit a scene facing a decision in reality to the greatest extent, and the accuracy of decision making is improved. The sensory motor cortex is effectively activated, and cooperative activation of the cognitive-motor neural network is induced; a decision interaction feedback link is added to enhance a decision stimulation effect and a cranial nerve feedback mechanism by analyzing related characteristics P300, power spectral density and brain network function connection quantity of the acquired electroencephalogram signals during normal form execution decision reaction. Compared with the prior art, the method has the advantages that immersive audio-visual stimulation and task interaction feedback are brought into a decision-making brain-computer interface for the first time, and a new scheme with neural rehabilitation and human-computer interaction functions is provided for neural feedback training of cognitive functions.

The invention provides a system for training consistent motion perception ability. The system comprises a training parameter setting module used for setting parameter information related to training; the training image generation module is used for generating a training image according to the training parameters; the man-machine interaction module is used for training information interaction and feedback between an individual and equipment; the equipment comprises display equipment, information input equipment, sound prompt equipment and monitoring equipment; the training process control module is used for controlling the sequence of different test times in the single training process; and the data recording and analyzing module is used for recording and analyzing training data so as to evaluate the overall training process. Through targeted visual stimulation design, rigorous training process control and synchronous combination of training auxiliary equipment, training of the consistent motion sensing ability is effectively realized, and the consistent motion sensing ability of a biological individual is improved.

The invention discloses a method and device for distinguishing different subtypes of attention deficit hyperactivity disorder, and the method comprises the steps: setting visual stimulation information and auditory stimulation information, the visual stimulation information comprises visual letter information and visual square position information, and the auditory stimulation information comprises auditory letter pronunciation information and auditory digital pronunciation information; generating a stimulation strategy according to the visual stimulation information and the auditory stimulation information; performing visual and auditory stimulation processing according to the stimulation strategy to obtain a correct rate, average correct response time and a comprehensive score; and according to the correct rate, the average correct reaction time and the comprehensive score, carrying out attention deficit hyperactivity disorder subtype distinguishing treatment to obtain a subtype distinguishing result. According to the method, different subtypes of attention deficit hyperactivity disorder are distinguished, and the accuracy and efficiency are improved. The method can be widely applied to the technical field of behavioral data analysis.

The invention provides a multi-source information fusion intelligent rehabilitation glove system. A main control unit of the system is used for coordinating and controlling all parts of rehabilitation gloves; the charging interface charges the battery; the control panel provides an interactive interface between a user and the system; the servo linear motor is used for controlling movement and position feedback of the rehabilitation glove; the battery supplies power to the system; the LED lamp is used for visual stimulation; the buzzer is used for audio stimulation; the low-frequency electrical stimulation interface is used for electrical stimulation; the multi-source fusion control module is used for monitoring a pressure signal and a muscle activity potential signal of the glove and controlling the movement and strength of the glove in real time; and wireless communication is used for data transmission and remote monitoring. By integrating multi-source information sensory stimulation, the rehabilitation process is enhanced, and particularly, the rehabilitation of damaged nerves is facilitated. Secondly, the multi-source information fusion control module monitors and adjusts hand movement and muscle movement of the patient in real time through a pressure sensor and a myoelectricity sensor, and personalized rehabilitation support is provided.

The invention discloses a brain-controlled mechanical arm method and device based on virtual-real fusion visual stimulation. The method comprises the steps that a state switching interface and a visual stimulation interface set are generated according to a target scene; presenting the state switching interface in front of the visual field of the operator by using a visual stimulation unit, and obtaining a state instruction selected by the operator; entering a visual stimulation interface according to the state instruction, fusing the visual stimulation interface set with an actual scene as required by using an augmented reality visual stimulation unit, presenting the fused visual stimulation interface set in front of the visual field of the operator, and obtaining an operation instruction selected by the operator according to a visual stimulation electroencephalogram signal judgment model; and the mechanical arm performs operation according to the operation instruction. Visibly, the control time and the brain burden of a brain-controlled mechanical arm operator are effectively reduced by utilizing a virtual-real fused visual stimulation interface; a brain-controlled mechanical arm device with high flexibility and practicability is built by fusing multiple technologies such as augmented reality, a brain-computer interface, a dynamic window and asynchronous control.

The invention provides a vision assessment method and system based on augmented reality, and relates to the technical field of vision assessment, and the method comprises the steps: obtaining an environment correction parameter based on an environment parameter; generating visual stimulation according to the vision evaluation task, the environment correction parameter and the initial visual stimulation parameter; s103, obtaining response feedback of the user to the current visual stimulation, and performing feature extraction on the response feedback to obtain a feature vector; s104, inputting the feature vector into a pre-trained adjustment model to obtain a visual stimulation parameter adjustment amount, and adjusting the visual stimulation parameter based on the visual stimulation parameter adjustment amount to obtain an updated visual stimulation parameter; s105, generating updated visual stimulation according to the vision evaluation task, the updated visual stimulation parameters and the environment correction parameters; the steps S103 to S105 are iteratively executed until a preset evaluation termination condition is met; and inputting the structured evaluation sequence data into a pre-trained evaluation model to generate a vision evaluation result of the user.

The invention provides a brain-controlled unmanned aerial vehicle group method based on brain-computer deep collaborative fusion. The method comprises the following steps: firstly, designing a steady-state visual evoked brain control signal acquisition normal form, and generating a visual stimulation signal through combined frequency-phase coding; secondly, the electroencephalogram signals are preprocessed through filtering, independent component analysis and the like; then, constructing an intention decoding network with deep fusion of space-time frequency features, extracting multi-scale features of the electroencephalogram signals by using a convolutional neural network, and performing deep fusion decoding through a space-time frequency Transform module to realize accurate prediction of brain control intention; thirdly, a virtual pilot algorithm and an artificial potential field algorithm are used for flight decision making of the unmanned aerial vehicle group; and finally, displaying the flight state of the unmanned aerial vehicle in real time through a visual interface constructed by PyQt5. The method has the advantages that the brain control intention of the user can be accurately decoded, the formation control and obstacle avoidance capability of the unmanned aerial vehicle group is effectively improved, and meanwhile, a visual interface and personalized setting are provided.

The invention relates to the technical field of ophthalmology informatization, in particular to an intelligent optic nerve function evaluation method and system based on visual evoked potential, and the method comprises the steps: obtaining an electroencephalogram signal, an eye movement track and an auditory reaction signal of a subject; obtaining visual stimulation parameters, wherein the visual stimulation parameters comprise stimulation frequency, stimulation intensity and stimulation duration; on the basis of the electroencephalogram signals, the eye movement tracks and the auditory reaction signals, signal preprocessing operation is executed, and preprocessed multi-modal physiological signals are obtained; extracting features by adopting an improved space-time convolutional neural network based on the preprocessed multi-modal physiological signals to obtain optic nerve function features; according to the optic nerve function features and the visual stimulation parameters, establishing an optic nerve conduction pathway model; evaluating the optic nerve function state based on the optic nerve conduction pathway model to obtain an optic nerve function evaluation result; generating an evaluation report containing the optic nerve function evaluation result; predicting the optic nerve function change trend, and generating a prediction report.

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.

A system and method is disclosed for testing and training a brain capability of planning and executing motion activity. The system comprises the following: an electroencephalographic sensor arrangement attachable to a head of said trainee; a processor configured for receiving and analyzing electroencephalographic signals obtained from said trainee in response to said visual stimulus displayed to said trainee; a memory storing instructions fori. instructing the trainee to imagine executing said motion action;ii. measuring electroencephalographic signals on the electroencephalographic sensor arrangement;iii. calculating at least one of a concentration index; a motor control index; an alertness index;iv. and iteratively providing the trainee with a feedback patternIn some embodiments a display is provided as a visual stimulus to the trainee to which the trainee responds by imagining executing a motion activity.

A Multisensory Simulation System comprises systems including: an Omni-Directional Treadmill (ODT), a Gravity Modification System (GMS), a Motion and gesture Tracking System (MTS), a Data Analytics System (DAS), a Visual Stimulation System (VSS), an Auditory Stimulation System (AUSS), an Operator Interface System (OIS), a User Harness System (UHS), a Tactile Stimulation System (TSS), an Atmospheric Simulation System (ATSS), a Neurological Stimulation System (NSS), an Olfactory Stimulation System (OSS), a Gustatory System Stimulation System (GSS), a User Monitoring System (UMS), a Controller, a Game Engine System (GES), database, a Communication Unit (CU), a User Safety System (USS), and a communication bus.

The invention relates to the technical field of exercise direction distinguishing ability training, in particular to an exercise direction distinguishing ability training system which comprises a training parameter setting module, a training image generating module, a man-machine interaction module, a training process control module and a data recording and analyzing module. The training parameter setting module sets training related parameters; the training image generation module generates a training image; the man-machine interaction module presents a training image, obtains the identification condition of a training individual on the training image, monitors a training environment and a training state, and assists training through a low-frequency modulated intermediate-frequency current; the training process control module controls a training process; the data recording and analyzing module can record and analyze training data and evaluate the overall training process. Through targeted visual stimulation design, rigorous training process control and synchronous combination of training auxiliary equipment, training of the motion direction distinguishing ability is effectively achieved, and the motion direction distinguishing ability of a biological individual is improved.

The embodiment of the invention provides a voxel-level semantic mapping method for a human visual cortex, and the method comprises the steps: building a corresponding relation between a visual stimulation feature and a human brain voxel response through a visual language model and a coding model; adopting a BLIP image description generation and word segmentation technology to extract a plurality of noun candidate tags from a human brain image sample set highly responsive to target human brain voxels; generating a plurality of image samples consistent with the plurality of noun-type candidate tags in terms of semantics in batches, and inputting the image samples into the coding model for response evaluation; and by sorting the voxel responses of the plurality of image samples, distributing semantic correlation scores for the plurality of noun type candidate tags. According to the voxel-level semantic mapping method for the human visual cortex provided by the embodiment of the invention, the label bias can be reduced by utilizing the visual language model by constructing a voxel-level semantic mapping mechanism running in an open vocabulary space. The embodiment of the invention further provides a voxel-level semantic mapping device and electronic equipment.

Systems and methods of the present disclosure are directed to systems and methods for treating cognitive dysfunction in a subject in need thereof. The system can include a light source and a speaker. A visual neural stimulation system provides, via the light source, visual stimulation having a first value of a first parameter. An auditory neural stimulation system provides, via the speaker, audio stimulation having a second value of the second parameter. A stimuli orchestration component selects, for a first time interval, one of the visual stimulation or the audio stimulation to vary based on a policy, selects, for the first time interval, the other of the visual stimulation or the audio stimulation to keep constant based on the policy, and provides causes the one of the visual neural stimulation system or the auditory neural stimulation system to vary the one of the visual stimulation or the audio stimulation.

A clustering integration-based electroencephalogram identification analysis method under complex visual stimulation comprises the following steps of designing an experimental normal form of electroencephalogram signal acquisition under a complex visual stimulation condition, selecting N subjects, and respectively operating according to the experimental normal form, synchronously collecting and recording multi-channel electroencephalogram signal data of N subjects under complex visual stimulation in the experimental normal form; starting an experiment, preprocessing electroencephalogram signals of the N subjects, obtaining preprocessed electroencephalogram signal data corresponding to the N subjects, and forming a rapid sequence to present an electroencephalogram signal data set; carrying out clustering analysis on the electroencephalogram signal data of the N subjects by utilizing a spectral clustering algorithm to obtain N sub-clusters; fusing the N tested sub-clusters by using a spectral clustering integrated optimization strategy based on hypergraph division; and testing and evaluating the fusion model to obtain a performance attribute value of the fusion model. According to the method, decision-making layer integration of a plurality of tested brain visual cognition image classification models is realized, so that a cross-individual electroencephalogram signal analysis method under a visual stimulation target with relatively high generalization, accuracy and robustness is constructed.

A vision test can be implemented in a virtual reality (VR) environment using a computer device that includes a head-mounted display (HMD). The computer device can obtain historical vision data of a patient user. Based on the historical vision data, the computer device determines an ordered sequence of vision tests including a first vision test for the patient user. The first vision test is followed by a set of one or more subsequent vision tests of the ordered sequence of vision tests. The computer device executes a user application configured to enable the ordered sequence of vision tests by rendering a user interface on the display. A first visual stimulus of the first vision test is displayed on the user interface. The computer device obtains a user response to the first visual stimulus, and dynamically adjusts one or more subsequent visual stimuli based on the user response.

A system and method relating to a brain-computer interface in which a visual stimulus overlaying one or more objects is provided, at least a portion of the visual stimulus having a characteristic modulation. The brain computer interface measures neural response to objects viewed by a user. The neural response to the visual stimulus is correlated to the modulation, the correlation being stronger when attention is concentrated upon the visual stimulus. The visual stimulus includes a feedback element that varies according to a measure of attention on the or each overlaid object.

The invention discloses a method and device for evaluating speech expression ability of neurological and mental diseases. According to the method, speech is induced through a visual stimulation material, speech data are collected, and after speech recognition, semantic calibration and text error correction, evaluation indexes are calculated from multiple dimensions of fluency, microcosmic linguistics and macroscopic linguistics in combination with a natural language processing technology, a large language model and a vector embedding technology, so that the evaluation accuracy is improved. And the result credibility is guaranteed through a robustness verification mechanism of iteration reflection of a double-big language model. According to the method, the problems of large subjective deviation, low efficiency and shallow analysis dimension of traditional evaluation are solved, and objective, accurate, efficient and comprehensive evaluation is realized.

The invention discloses a high-dimensional electroencephalogram clustering decoding method under complex visual stimulation. The method comprises the following steps: designing an electroencephalogram signal acquisition experiment normal form under rapid sequence visual presentation; electroencephalogram signals are collected and processed; the electroencephalogram signals are further processed, high-quality electroencephalogram signals from sufficient subjects are obtained, and an electroencephalogram signal data set under rapid sequence visual presentation is formed; performing clustering analysis on the high-dimensional electroencephalogram signal data; the electroencephalogram signals are accurately identified; and the identification effect is evaluated. According to the method, potential characteristics of electroencephalogram signals under different visual stimuli are mined, and effective identification and analysis of electroencephalogram modes under different stimuli are achieved.

Brain activity can be monitored to facilitate a vision test in a virtual reality (VR) environment using an electronic device that includes a head-mounted display (HMD) and a plurality of electrodes. The electronic device can execute a user application configured to enable a virtual vision test, and generates a VR user interface corresponding to a three-dimensional (3D) virtual environment. The electronic device renders, on the HMD, a user interface including a first visual stimulus corresponding to the virtual vision test. While displaying the visual stimulus, in real time, the electronic device collects a plurality of electrical signals by the plurality of electrodes that contact a head of a user, and determines information of at least one of a second visual stimulus following the first visual stimulus and a user response to the first visual stimulus based on the plurality of electrical signals.

A computer-implemented method for determining a refractive error of an eye of a person includes the following steps: a) generating input data containing a distance between the eye of the person and a visual stimulus displayed to the eye of the person and b) generating outcome data containing a refractive error of the eye of the person determined by evaluating the input data. A position of the entrance pupil of the eye of the person is considered when the refractive error of the eye of the person is determined. A computer program, a field device, a remote device, a determining device, a method for producing a geometrical model of a spectacle lens, and a method for producing a spectacle lens are also disclosed. The present disclosure provides a fast, easy, versatile, reliable, and accurate approach for determining a refractive error of an eye of a person.

A dichoptic visual stimulus is presented to a user's eyes by presenting a first visual stimulus to a first eye of the user, the first visual stimulus being presented with a first modulation, and presenting a second visual stimulus to a second eye of the user, the second visual stimulus being presented without the first modulation. The first visual stimulus and second visual stimulus are configured to cause the user to perceive the first visual stimulus and the second visual stimulus as a single visual element, and to perceive the visual element as being unmodulated by the first modulation. Neural signals are obtained from a neural signal capture device configured to detect neural activity of the user. In response to determining, based on the neural signals, that the first eye is focused on the first visual stimulus, a computing system executes a command associated with the dichoptic visual stimulus.

The invention discloses an SSVEP (Steady-State Visual Evoked Potential) recognition system and method based on linear frequency modulation visual evoked potential migration. The visual stimulation generation and presentation module generates and presents a visual stimulation signal; the electroencephalogram signal acquisition module is used for acquiring Chirp-VEP and SSVEP electroencephalogram signals; the electroencephalogram signal preprocessing module is used for preprocessing the collected electroencephalogram signals; the Chirp pulse matrix construction module is used for converting a Chirp signal to obtain a Chirp pulse matrix, the Chirp-VEP decomposition module is used for constructing a least square optimization problem to solve common model parameters of Chirp-VEP, and the common model parameters are migrated to SSVEP through the SSVEP identification module to carry out feature extraction and classification. According to the method, the system overhead is reduced, the calibration time is shortened, the training cost is minimized while the optimal performance is kept, and the practicability of the brain-computer interface based on SSVEP is improved.

The invention discloses a cross-subject brain decoding system based on a functional magnetic resonance image and feature decoupling. The cross-subject brain decoding system comprises a functional magnetic resonance image feature extraction module, an image generation module and an image output module, the functional magnetic resonance image feature extraction module extracts neural activity features related to visual stimulation from functional magnetic resonance imaging fMRI data, the image generation module comprises a feature decoupling module and a diffusion transformer module, and the feature decoupling module extracts common features among subjects from the neural activity features; the diffusion transformer module generates a high-accuracy image by using the common features as conditions, and the output module processes and outputs the generated image. According to the method, the features of the brain activity signals are extracted through the mask auto-encoder, the common features are extracted through the decoupling module and combined with the diffusion transformer, and external visual images can be efficiently and accurately reconstructed from functional magnetic resonance imaging (fMRI) data of different subjects.