24 results about "Electro-oculography" patented technology

The invention relates to the technical field of sleep electroencephalogram analysis, in particular to a sleep electroencephalogram analysis method and device, which more comprehensively assesses the sleep state and improves the reliability and credibility of the result through double verification of frequency domain analysis and image analysis. The method comprises the following steps: acquiring sleep electroencephalogram time domain information of a to-be-analyzed user; the sleep electroencephalogram time domain information at least comprises one physiological signal including an electrooculogram, an electroencephalogram, an electrocardiogram and a mandibular electromyogram; inputting the sleep electroencephalogram time domain information into an electroencephalogram time-frequency domain conversion model to obtain a sleep electroencephalogram frequency domain feature set; performing classified statistics on the sleep electroencephalogram frequency domain feature set by using a preset sleep activity feature reference to obtain a sleep state staging frequency domain analysis result of the to-be-analyzed user; the sleep states comprise sleep states in different time periods; and inputting the sleep electroencephalogram time domain information of the user to be analyzed into the electroencephalogram activity image recognition model.

The invention relates to a multimodal sleep staging method based on a bidirectional interactive RWKV network, and belongs to the technical field of data processing, and the method comprises the following steps: obtaining an electroencephalogram signal, an electrooculogram signal and an electromyogram signal; carrying out depth feature extraction on the electroencephalogram signal, the electrooculogram signal and the electromyogram signal; the first RWKV module is used for realizing context modeling of information in the modals and fusion of information between the modals by taking the first RWKV module as a main part and the second RWKV module as an auxiliary part; the first RWKV module and the second RWKV module are used as a main part and an auxiliary part, and context modeling of information in modals and fusion of information between the modals are achieved through the second RWKV module parallel to the first RWKV module; the outputs of the two RWKV modules are integrated through element-by-element addition to form a fusion feature; and generating a category with the highest probability for the posterior probability distribution of each sleep stage as a sleep stage prediction result. The method is high in staging efficiency and high in accuracy.

The invention discloses a neonatal precise music treatment system based on amplitude integrated electroencephalogram awakening signals and a use method thereof. The neonatal precise music treatment system comprises a collection module, a customized music treatment content design module, an automatic closed-loop module and a safety guarantee module. The collection module is used for collecting the electrooculogram, the heart rate, the pulse oxyhemoglobin saturation and the amplitude integrated electroencephalogram, screening out an amplitude integrated electroencephalogram awakening signal according to the electrooculogram, the heart rate and preset three-level grabbing logic, and then obtaining the level of the amplitude integrated electroencephalogram awakening signal and the level of the brain function state; the customized music treatment content design module is used for generating music according to a preset source material library, the level of the amplitude integrated electroencephalogram awakening signal and the brain function state level; the automatic closed-loop module is used for controlling playing and stopping of music; and the safety guarantee module is used for controlling the volume and stopping working when the amplitude integrated electroencephalogram awakening signal is interrupted. According to the method and the device, the accuracy of awakening signal grabbing is improved.

Methods and systems for tracking vision performance use various techniques such as and not limited to electrooculography. A modular device is integrated with frames of eyeglasses that can be worn by a user. The device incorporates one or more biosensors such as EOG sensors. EOG recording may be used to estimate eyelid and eye motion, and eye gaze direction. The modular device may be fitted into new eyewear or retrofitted into existing eyewear for tracking eyes of the wearer. Systems and methods enable seamless integration of eye tracking into everyday life to maximize productivity and performance.

A system for determining a direction of gaze of a user, comprising a set of electrodes arranged on earpieces, each electrode comprising a patch of compressible and electrically conducting foam material. The system further includes circuitry connected to the electrodes and configured to receive a set of voltage signals from a set of electrodes arranged on an audio endpoint worn by a user, multiplex said voltage signals into an input signal, remove a predicted central voltage from said input signal, to provide a detrended signal, and determine said gaze direction based on said detrended signal. Such conducting foam materials provide satisfactory bio-sensing performance for a wide range of compression levels and over time. In the case of on-ear headphones, the foam electrodes may be integrated in the cuffs with little or no effect on the comfort level.

The invention discloses a non-contact sleep eye movement detection method and system based on a biological radar, and the method comprises the steps: transmitting a radar echo signal through a transmitter, receiving the radar echo signal through a receiver, and selecting two paths of time sequences in a target distance in the received radar echo signal through a computer, carrying out autocorrelation processing on the two paths of time sequences to obtain an eye movement periodic characteristic enhanced time sequence, solving a phase in the eye movement periodic characteristic enhanced time sequence to obtain a path of real number time sequence, carrying out fast Fourier transform on the real number time sequence to obtain a power spectrum of the time sequence, and meanwhile, carrying out time spectrum analysis on the power spectrum of the time sequence. The electrooculogram measuring platform is used for synchronous detection, the non-contact detection result is compared with the synchronous detection result of the electrooculogram measuring platform, and the correctness and feasibility of the non-contact detection result are further verified on the basis of achieving non-contact detection of the eye movement of the human body.

The invention discloses an electroencephalogram signal denoising method and system combining SSA and JADE, and the method comprises the following steps: carrying out the preprocessing of an original electroencephalogram signal, extracting a signal mode of oscillation change related to artifacts, combining a preset threshold value with an extreme point of the signal mode, screening and extracting a segment only containing an electrooculogram signal, and carrying out the denoising of the electroencephalogram signal. The rest part is zeroed to serve as an input signal for blind source separation; the method comprises the following steps: decomposing a single-channel electroencephalogram signal into a plurality of components, constructing a trajectory matrix and performing SSA, preliminarily separating main components and noise components of the signal, and further processing the components obtained by singular spectrum analysis and decomposition by using a JADE algorithm; separating a signal and noise by using high-order statistics, processing a source signal of an artifact related source, and suppressing a noise component; and performing inverse JADE on the processed source signal matrix and the mixed matrix, and re-synthesizing the de-noised single-channel electroencephalogram signal. Compared with the prior art, under the condition that the noise level is high, noise and useful signals can still be effectively distinguished, and therefore the signal processing effect is remarkably improved.

A method of classifying, by an apparatus including a processor and a memory, a subject-independent driver state according to an embodiment of the present disclosure may include (a) learning a state classification model that receives one or more drivers' bio-signal data from a database to output the drivers' state classification (b) receiving a new driver's bio-signal, (c) preprocessing the received new driver's bio-signal, and (d) inputting the preprocessed new driver's bio-signal to the state classification model to output the driver's state classification result, wherein the bio-signal is at least one of an electroencephalogram (EEG), an electrooculogram (EOG), an electrocardiogram (ECG), and a photoplethysmogram (PPG).

This invention belongs to the field of umami perception evaluation and provides a method for recognizing umami-induced EEG signals in the taste perception process. The method includes the following steps: acquiring taste EEG data induced by umami stimulation using an EEG signal acquisition system; establishing a umami EEG dataset using the acquired data; then cleaning the raw data using an independent component analysis algorithm, including filtering, removing electrooculograms, artifacts, and rereferences, and calculating the frequency (Hz) and power spectral density; vectorizing and regularizing the 0–13 Hz signals according to brain regions; subsequently, augmenting the dataset using minority class oversampling (MCB) and dividing it into training and test sets; constructing an ensemble model for signal classification; and finally, using the trained model for umami recognition. The ensemble model method proposed in this invention significantly improves the accuracy and stability of the umami EEG recognition model, effectively achieving umami recognition.

Methods and systems for tracking vision performance use various techniques such as and not limited to electrooculography. A modular device is integrated with frames of eyeglasses that can be worn by a user. The device incorporates one or more biosensors such as EOG sensors. EOG recording may be used to estimate eyelid and eye motion, and eye gaze direction. The modular device may be fitted into new eyewear or retrofitted into existing eyewear for tracking eyes of the wearer. Systems and methods enable seamless integration of eye tracking into everyday life to maximize productivity and performance.

The present invention relates to the technical field of sleep electroencephalogram analysis, and particularly to a sleep electroencephalogram analysis method and device, which comprehensively evaluate the sleep state through double verification of frequency domain analysis and image analysis, and improve the reliability and credibility of the results. The method includes: obtaining the sleep electroencephalogram time-domain information of the user to be analyzed; the sleep electroencephalogram time-domain information includes at least one physiological signal such as electrooculogram, electroencephalogram, electrocardiogram, and electromyogram of the masseter muscle; inputting the sleep electroencephalogram time-domain information into an electroencephalogram time-frequency domain conversion model to obtain a sleep electroencephalogram frequency-domain feature set; using a pre-set sleep activity feature benchmark to classify and statistically analyze the sleep electroencephalogram frequency-domain feature set to obtain a sleep state staging frequency domain analysis result of the user to be analyzed; which includes sleep states in different time periods; inputting the sleep electroencephalogram time-domain information of the user to be analyzed into an electroencephalogram activity image recognition model.

Novel auricular sleep monitoring systems and ear-canal pulse oximeter may be configured to be coupled to a wearer's ear or peri-auricular area. The sleep monitoring systems may include one or more of an auricular electrooculogram (EOG) system; an auricular electroencephalogram (EEG) system; an auricular electrocardiogram (ECG) system; and an ancillary sleep surveillance system, comprising a conventional pulse oximeter or a novel ear-canal pulse oximeter and other sleep-relevant sensors. A processing unit may be configured to analyze data from EOG system, EEG system, ECG system, pulse oximeter and other sleep-relevant sensors to assess the wearer's sleep profile. When obstructive sleep apnea is detected, a notification may be automatically generated immediately to warn the wearer to take appropriate actions. These wearable sleep monitoring systems may be housed in earbud-style or hearing-aid-style structures, suitable for snugly pre-mounted electrodes, easy self-installation or self-removal and nightly use at home.

This disclosure relates to a multimodal sleep staging method based on a bidirectional interactive RWKV network, belonging to the field of data processing technology. The method includes: acquiring electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG) signals; performing deep feature extraction on the EEG, EOG, and EMG signals to obtain [specific features]; using a first RWKV module as the primary and secondary module as the secondary module to achieve contextual modeling of intramodal information and fusion of intermodal information; using a second RWKV module parallel to the first RWKV module as the primary and secondary module to achieve contextual modeling of intramodal information and fusion of intermodal information; integrating the outputs of the two RWKV modules through element-wise addition to form a fusion feature; generating a posterior probability distribution for each sleep stage and selecting the category with the highest probability as the sleep staging prediction result. This invention offers high staging efficiency and high accuracy.

A method for recognizing blink and eye movement based on electroencephalogram (EEG) and a system thereof are disclosed. The method includes measuring a subject's EEG-based electrooculogram (EOG) signal (hereinafter referred to as “EOG signal”) using three electrodes connected to an EEG head cap. The method further includes performing potential blink detection and zero crossing detection using the EOG signal, and generating a plurality of parameters used for blink and eye movement classification using the EOG signal depending on presence or absence of the potential blink and the zero crossing. In addition, the method further includes classifying the blink and up / down / left / right eye movements of using the plurality of parameters.

The product is used to collect bioelectric signals such as electrocardiogram, electroencephalography, electromyography, and electrooculography; the broken lines show portions of the medical instrument for collecting physiological electrical signal that form no part of the claimed design.

The invention belongs to the field of freshness perception evaluation, and provides a method for identifying a delicate flavor induced electroencephalogram signal in a taste perception link, which comprises the following steps of: acquiring taste electroencephalogram data induced under the stimulation of delicate flavor by using an electroencephalogram signal acquisition system, establishing a delicate flavor electroencephalogram data set by using the acquired data, cleaning the original data by adopting an independent component analysis algorithm in sequence, including filtering, electrooculogram removal, artifact removal and re-reference, calculating frequency (Hz) and energy spectrum density, and performing vectorization and regularization arrangement on 0-13Hz signals according to a brain region; and then a minority class oversampling technology is used to amplify a data set and divide a training set and a test set, an integrated model is constructed for signal classification, and the finally trained model is used for delicate flavor identification. According to the integrated model method provided by the invention, the accuracy and the stability of the delicate flavor electroencephalogram recognition model are remarkably improved, and the delicate flavor recognition is effectively realized.

A mouthguard for measuring eye movements of a wearer, has: a body adapted to engage teeth of the wearer and extending laterally along a frontal axis between a left side and a right side and extending vertically along a vertical axis, between a top and a bottom; a power source; electrooculogram (EOG) electrodes secured to the body and operatively connected to the power source, the EOG electrodes including a top right electrode, a top left electrode, at least one bottom electrode, and a bias electrode being substantially centered on the body; and a controller operatively connected to the EOG electrodes and to the power source, the controller having a processing unit and a computer-readable medium having instructions stored thereon executable by the processing unit to: receive electrooculogram signals from the EOG electrodes; and analyse the electrooculogram signals to determine movements of one or more eye of the wearer.

The product is used to collect bioelectric signals such as electrocardiogram, electroencephalography, electromyography, and electrooculography; the dashed lines show portions of the medical instrument for collecting physiological electrical signal are transparent material and that form no part of the claimed design.

Novel auricular health monitoring systems, preferably including one or more of the following: auricular electroencephalogram (EEG), electrooculogram (EOG), electrocardiogram (ECG) and ear-canal pulse oximeter, which may be useful in health monitoring or in brain-computer interface. The EEG electrodes, EOG electrodes, ECG electrodes, light generator and light detecting sensor may be housed on a surface of an earbud-style structure or other in-ear structures made of flexible elastic and adaptable material such that these pre-mounted EEG electrodes, EOG electrodes, ECG electrodes and the pulse oximeter will be naturally and snugly contacting the skin of the wearer's external ear canal. A processing unit may be configured to analyze data from EEG system, EOG system, ECG system and ear-canal pulse oximeter to assess the wearer's health profile. These wearable monitoring systems are suitable for easy self-installation or self-removal and daily ambulatory use.

Abstract A wearable apparatus and method for monitoring physiological parameters of an individual during sleep, the apparatus comprising: (a) a head-applied sensor assembly configured to be worn on the head of an individual, the sensor assembly comprising: (i) at least one electroencephalography (EEG) sensor; (ii) at least one electrooculography (EOG) sensor; (iii) at least one electromyography (EMG) sensor; and (iv) at least one photoplethysmography (PPG) oximetry sensor; and (b) an electronics module operatively coupled to the head-applied sensor assembly and configured to acquire signals from the EEG, EOG, EMG, and PPG oximetry sensors during a common monitoring period. Abstract

Aspects of the technology relate to using a wearable computing device 100 to determine and utilize head and eye poses. The wearable computing device can include an electrooculogram electrode 250 configured to provide first feedback to one or more processors 220, and an inertial measurement unit 260 configured to provide second feedback to the one or more processors. The first feedback can correspond to eye movement, and the second feedback can correspond to head movement. The wearable computing device can also include one or more processors that can be configured to use the first feedback and the second feedback to determine whether the head movement and the eye movement correspond to a trigger for an action, and cause the wearable computing device to perform the action based on the determination.

A lightweight, cost-effective electrooculography (EOG)-based signal analysis device designed for non-diagnostic, commercial communication and control of external devices. The device includes ocular electrodes (102a, 102b, 102c) for capturing vertical eye movement signals and an auricular reference electrode (300) to improve signal stability and comfort. These signals are processed by a microcontroller (202) and transmitted to an external signal interpreter (202), facilitating interaction with devices such as computers, robotic systems, and prosthetics. The invention addresses the challenge of providing accessible, portable communication solutions for individuals with neuro-motor impairments, ensuring practical use with minimal setup requirements. The primary applications include communication support for conditions like ALS, Locked-in syndrome, and cerebral palsy, enabling users to control external systems efficiently.

The invention is suitable for the technical field of eye movement training, and provides an eye movement training evaluation method and an eye movement training evaluation device.The method comprises the steps that in the eye movement training process, an electro-oculogram signal of a user is obtained; based on the electrooculogram signal, determining a target eye movement frequency band of the user, the target eye movement frequency band being a rapid eye movement frequency band of the user; determining a first power spectral density of the electrooculogram signal in the target eye movement frequency band; based on the first power spectral density, the extraocular muscle movement intensity of the user is determined, the extraocular muscle movement intensity refers to the contraction intensity of the extraocular muscle of the user, and the extraocular muscle movement intensity reflects the extraocular muscle state of the user. According to the invention, the problems of poor real-time performance, insufficient objectivity and the like of eye movement training effect evaluation can be solved.