42 results about "EEG device" patented technology

The invention discloses an EEG signal quality real-time evaluation method and device, and the method comprises the following steps: 1, building an EEG signal quality evaluation database based on an EEG public database, marking the EEG signal quality based on a signal quality grade standard, and dividing EEG signal quality grades according to score scores; 2, performing feature extraction on the EEG signal quality evaluation database, and constructing an EEG signal quality time-frequency-space feature set; 3, taking the time-frequency-space feature fusion feature map as input, and constructing an EEG signal quality real-time evaluation model based on a deep learning algorithm; 4, performing model deployment and real-time evaluation; and 5, outputting and feeding back the model. According to the method, the signal quality in the EEG equipment acquisition process can be evaluated in real time, a signal quality real-time display interface is constructed for real-time visualization, a user feedback mechanism is established, and a user can be prompted to adjust the equipment or operate.

The embodiment of the invention provides an electroencephalogram experiment data processing method and device and electroencephalogram equipment, and relates to the technical field of electroencephalogram equipment. According to the specific implementation scheme, in the process of continuously displaying multiple experimental stimulation contents, electroencephalogram experimental data of an experimented object are collected; acquiring experimental stimulation content to obtain acquired content; determining a content switching time point in the collected content, and generating content identification data based on the content switching time point, the content switching time point representing a time point between two adjacent experimental stimulation contents corresponding to the collected content; and fusing the content identification data with the electroencephalogram experiment data to obtain fused electroencephalogram experiment data. In the scheme, the electroencephalogram equipment can automatically generate the content identification data, and the dependency on the equipment is reduced. Compared with wireless connection, the accuracy of the content identification data can be improved. Besides, stimulation in a specified environment such as a natural environment can be directly collected for experiment without display equipment, so that the application range of the scheme is expanded.

The invention provides an automatic safe driving cross-scene alertness detection method and device based on EEG analysis, and relates to the technical field of intelligent driving. The embodiment of the invention provides an automatic safe driving cross-scene alertness detection method based on EEG analysis, and the method solves the problem that the number of channels among different EEG devices is inconsistent by utilizing the capability of reconstructing the EEG information of a plurality of real channels of a pre-trained alertness detection network. The alertness detection network after fine tuning can reconstruct missing EEG information according to input EEG information of a small number of channels and corresponding brain region information, and a classification task is completed based on the reconstructed information.

The invention discloses a psychological health screening method based on immersive game tasks and EEG (electroencephalogram) analysis, and relates to the technical field of psychological health screening, comprising the following steps: determining a psychological health dimension to be evaluated, and integrating VR (virtual reality) and EEG equipment; extracting corresponding data from different psychological dimension game data of VR to obtain emotion stability, cognitive ability and social skill evaluation values; and constructing a psychological health evaluation model based on linear regression through the EEG feature data, outputting score results on different psychological dimensions, and automatically generating a psychological health evaluation report in combination with corresponding evaluation values. The method solves the problems that it is difficult to establish a player attention state classification model to determine the attention concentration duration of players in different game stages, it is difficult to quantify evaluation values constructed by feature data in different psychological dimensions, and it is lack of automatically generating a psychological health evaluation report by combining VR games and EEG electroencephalogram signals.

Embodiments of a system for controlling an object using brainwaves are disclosed. The system includes a set of EEG electrodes configured to be positioned on a head of a user and to collect EEG signals. The system further includes one or more computer readable storage mediums storing a framework configured to execute an extensible architecture through which EEG signals are interpreted for control of the object. The framework includes an EEG device plugin associated with the set of EEG electrodes and configured to extract the EEG signals from the set of EEG electrodes. The framework also includes an interpreter plugin configured to convert the EEG signals extracted by the EEG device plugin into a command. Further, the framework includes an object control plugin configured to access the command through an extension point of the interpreter plugin and to execute the command to control the object.

The invention provides an electroencephalogram signal baseline interval determination method and device and electroencephalogram equipment, and relates to the technical field of electroencephalograms, and the method comprises the following steps: determining a first effective electroencephalogram signal in a first electroencephalogram signal through the first electroencephalogram signal in a current detection time period; and calculating the power spectral density of each frequency band in the preset number of frequency bands in the first effective electroencephalogram signals by using the first effective electroencephalogram signals. And performing multi-fractal denoising transformation on the effective electroencephalogram signals of each frequency band to obtain a multi-fractal spectrum of each frequency band. And calculating a first stability index of the first effective electroencephalogram signal by using the power spectrum density and the multi-fractal spectrums of all frequency bands in the first effective electroencephalogram signal. And if the first stability index is smaller than or equal to the historical stability index, determining the first continuous interval as a target baseline interval. Wherein the historical stability index is the stability index of the historical baseline interval in the previous historical detection time period of the current detection time period.

Ventilator (1) with at least a sensor for detecting at least one parameter of a breathing gas, a control device for specifying settings, a storage unit a control unit connected to the storage unit, a first computer program product for implementing the settings taking into account a specification and a second computer program product that regulates data transfer to and from the control unit via at least one interface (8); wherein the ventilator (1) is designed such that at least one interface allows for the mutual exchange of data between the ventilator (1) and external storage media and / or devices (12), the exchange of parameters, and the loading of parameters and / or a configuration of the ventilator from external storage media and / or devices, wherein the ventilator has at least two interfaces that support different standards and communication protocols, wherein one of the interfaces is designed as a radio interface and another of the interfaces is designed as a memory card interface, wherein the memory card interface is suitable for a Secure Digital card, a Multimedia card, a Memory Stick PRO card, an xD-Picture card, a SmartMedia card or a CompactFlash card, where data from the ventilator can be saved to an inserted memory card via the memory card interface, and data from an inserted memory card can be loaded into the ventilator, where, in the area of ​​the ventilator, a firmware or software is installed as the second computer program product, which runs as a client and enables a connection of the ventilator to the Internet, where the client is responsible for initiating contact during data transmission and is configured to determine the timing of the transmission, where reading or saving ventilation and usage information to the respective storage medium must be initiated by an event, and the event is the expiration of a specific period of time since the start or end of ventilation therapy, where, when reading usage data from the ventilator, an assignment to the correct patients is automatically performed via a stored database. wherein the ventilator contains relevant information about its setting parameters and their setting limits as well as its stored therapy information and is designed to make the relevant information available to a PC, wherein the ventilator is configured to provide, via the respective interface or storage medium, an application directly executable by a PC operating system, wherein the ventilator contains executable logic in non-volatile memory, wherein the executable logic is not executable by the ventilator itself but by the PC, thus enabling the installation of software on the PC without requiring a user to obtain the software directly from the ventilator manufacturer or a service provider via CD, download, or storage medium. wherein the ventilator further comprises a data cable as a data exchange means inserted into the respective interface, wherein the data cable is connected to an interface converter outside the ventilator, wherein the interface converter is designed to enable communication between the ventilator and another device via at least one transmission standard not provided in the ventilator itself, wherein at least one additional sensor in the form of a pulse oximeter and / or a sensor for CO2 measurement and / or a sensor for measuring respiratory effort and / or an ECG device and / or an EEG device is additionally connected to the ventilator via the interface converter.

An electroencephalography (EEG) system comprises: an EEG device; a display; one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for: receiving measurement data; extracting, from the measurement data, a first set of features and a second set of features; inputting the first set of features and the second set of features into a first treatment-specific machine-learning model and a second treatment-specific machine-learning model, respectively; generating a data structure based on the predicted treatment responses; and rendering, on the display, the generated data structure to provide the predicted treatment responses to the first candidate treatment of the CNS disease and the second candidate treatment of the CNS disease.

The invention discloses a bicycle feature optimization design method based on eye movement and electroencephalogram. The method comprises the steps that S1, a bicycle target sample picture is selected to be preprocessed, and bicycle morphological feature areas are divided; s2, a subject wears an electroencephalogram cap, the position and the sitting posture of the subject are adjusted, the head position of the subject is corrected, the sight of the subject moves along with a red dot in a screen, and an experiment task is started after the error reaches the standard; s3, collecting eye movement data of the subject in the bicycle morphological feature observation area through an eye movement tracker to obtain eye movement evaluation indexes; electroencephalogram data of the subject in the bicycle morphological characteristic observation area is collected through electroencephalogram equipment to obtain electroencephalogram indexes; s4, obtaining a bicycle feature design optimization process through the combination of the eye movement index and the electroencephalogram index; according to the method, direct data of selective attention of the user in the cognitive recognition process can be obtained through the eye movement tracking technology and the electroencephalogram technology to be applied to bicycle design, the bicycle design efficiency and accuracy are improved, the requirement of the market for high-quality bicycle design is met, and the wide market prospect is achieved.

A neurofeedback system includes an EEG apparatus, a presentation apparatus and a controller. The controller is configured to: (i) cause the presentation apparatus to display an overlaid image to the user that comprises a first image flickering at a first frequency and a second image flickering at a second frequency different than the first frequency, the first image being an affective distractor stimulus image and the second image being a task-relevant stimulus image, (ii) receive from the EEG apparatus a number of first steady-state visual evoked potential (SSVEP) signals generated in response the first image of the overlaid image and a number of second SSVEP signals generated in response the second image of the overlaid image, and (iii) calculate feedback indicative of how much attention of the was user allocated to the task-relevant stimulus image versus how much attention of the user was allocated to the affective distractor stimulus image.

A multi-use electroencephalogram (EEG) system and method consists of a strobe where the strobe is adjustable to produce single and multiple strobe flashes. An EEG device connected with the strobe configured for time-locking an EEG recording with the strobe where the EEG device measures patient flash visual evoked potential (FVEP) response to the strobe flashes and a controller connected with the strobe and the EEG device where the controller operates the strobe to produce one single strobe flash condition followed by a double strobe flash condition employing various interstimulus intervals in a complete counterbalanced order.

The present invention discloses a method for decoding imagined movements based on tactile sensation calibration, comprising: (1) setting up a tactile stimulation device and applying tactile stimulation to the dorsal side of the left or right wrist of the subject; (2) setting up an EEG device to record EEG signals, with the original signals being digitally sampled at a frequency of 500 Hz and using a filter to filter the original signals; (3) using the tactile stimulation device and the EEG device to collect the pure imagined movement data of the subject, as well as the imagined movement data assisted by tactile sensation; (4) using the common spatial pattern algorithm to extract the EEG data features and training a classifier for classifying the EEG data features; wherein, the imagined movement data assisted by tactile sensation is used as the training set; (5) applying the trained classifier to pure imagined movement decoding. By using the present invention, EEG data with a higher signal-to-noise ratio can be obtained, and then a more robust and accurate model can be trained for movement decoding.

The present invention comprises an arrangement with an EEG device, in particular a QEEG device and a head part which has at least two applicators which generate a low-frequency electromagnetic field of an application frequency in a frequency range of 1 Hz to 100 Hz and with a field strength below 20 mT, for use in the treatment of dementia, depression, HRV regulation, Raynaud's disease, Asperger's syndrome, migraine, dizziness, sleep disorders, burnout, ADHD, muscular and / or joint pain or autism.

An electroencephalography (EEG) system comprises: an EEG device; a display; one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for: receiving measurement data; extracting, from the measurement data, a first set of features and a second set of features; inputting the first set of features and the second set of features into a first treatment-specific machine-learning model and a second treatment-specific machine-learning model, respectively; generating a data structure based on the predicted treatment responses; and rendering, on the display, the generated data structure to provide the predicted treatment responses to the first candidate treatment of the CNS disease and the second candidate treatment of the CNS disease.

Glucose forecasting systems and methods that include a minimally invasive scalp-worn behind-the-ear EEG device that includes first and second sensors. With an application on a personal device, analyzing the processed EEG signals with a trained forecasting model and forecasting future glucose levels, of the subject; causing the personal device to visually present on a display information that is indicative of the forecasted future glucose levels.

The application discloses a rehabilitation lower limb exoskeleton EEG decoding method based on ensemble learning, relates to the technical field of EEG decoding, and comprises the following steps: acquiring an EEG signal of a user's motor cortex through an EEG device; performing shallow layer processing on the EEG signal to obtain a first type of feature; constructing a deep layer processing network model of the EEG signal, performing deep layer processing on the EEG signal, and obtaining a second type of feature; combining the first type of feature and the second type of feature, training a classifier, and decoding the motor intention of the EEG signal; performing visual feedback on the user according to the decoded motor intention, then executing the decoded movement on a lower limb exoskeleton device, and obtaining proprioceptive feedback. The application combines shallow layer and deep layer neural networks to improve the performance of lower limb motor intention, performs continuous motor imagination experiments on the lower limb exoskeleton device, collects continuous EEG signals, and designs an experimental paradigm of online visual and proprioceptive feedback.

A long-term intracranial stereoelectroencephalogram (stereoEEG) system for determining an epileptic region of a patient includes an implantable long-term stereoEEG device connected to an intracranial stereoEEG deep electrode operably implantable within brain parenchyma. The implantable device is designed to perform conventional EEG context recording and preferential seizure recording in real time, and to transmit the records in an asynchronous manner. Through the asynchronous transmission mode, overheating of the device is avoided, battery consumption is reduced, and the long-term stereoscopic EEG process is safe and feasible. The implantable long-term stereoscopic EEG device may also include a temperature monitoring module for avoiding overheating of the device due to data processing or transmission loads and any manufacturing failure. The device of the invention may include an external stereoscopic EEG data storage device that enables long-term intracranial stereoscopic EEG without imparting specific restrictions to the patient's daily life.

In at least some embodiments, the present disclosure describes an electroencephalography (EEG) device having a main band and at least one moveable band. The main band may include fixed EEG sensors positioned on the main band so as to contact a scalp of the wearer in at least two scalp zones. The at least one moveable band may include at least one moveable EEG sensor. The moveable band may move relative to the main band to adjust to a position of the moveable EEG sensor(s) on the scalp of the wearer in at least one adjustable zone. The EEG device may include a processing device to measure EEG measurements using the fixed and / or movable EEG sensors, and transmit the EEG measurements to an external computing device to determine a mental condition, a mental state or both of the wearer based on the EEG measurements.

The invention discloses a nuclear magnetic photoelectric multi-mode synchronous brain imaging system, and relates to the technical field of brain imaging, the system comprises PET-MR equipment, EEG equipment, a detector head sleeve and an eye tracker, the PET-MR equipment is integrated equipment of the PET equipment and the MR equipment, the EEG equipment comprises EEG receiving equipment and an EEG electrode, the detector head sleeve comprises an NIRS detector and the EEG electrode, and the NIRS detector is connected with the EEG electrode. The PET-MR equipment is used for positron emission tomography and magnetic resonance imaging, the detector head sleeve is used for collecting electroencephalogram signals and blood flow infrared signals, and the eye tracker is used for collecting eye movement tracks; and the PET equipment, the eye tracker, the NIRS detector and the EEG equipment carry out multi-modal data synchronous acquisition according to a synchronous signal provided by a clock source in the MR equipment. According to the invention, the comprehensiveness and accuracy of brain imaging are improved.

In at least some embodiments, the present disclosure describes an electroencephalography (EEG) device having a main band and at least one moveable band. The main band may include fixed EEG sensors positioned on the main band so as to contact a scalp of the wearer in at least two scalp zones. The at least one moveable band may include at least one moveable EEG sensor. The moveable band may move relative to the main band to adjust to a position of the moveable EEG sensor(s) on the scalp of the wearer in at least one adjustable zone. The EEG device may include a processing device to measure EEG measurements using the fixed and / or movable EEG sensors, and transmit the EEG measurements to an external computing device to determine a mental condition, a mental state or both of the wearer based on the EEG measurements.

The embodiment of the invention provides an electroencephalogram experiment data transmission method and device, electroencephalogram equipment and an electroencephalogram experiment system, and relates to the technical field of electroencephalogram equipment. According to the specific implementation scheme, the method comprises the steps that in the process that a display device displays experiment stimulation content, electroencephalogram experiment data of an experimented object is collected; based on wired connection between the electroencephalogram device and the display device, receiving content identification data sent by the display device; fusing the content identification data with the electroencephalogram experiment data to obtain fused electroencephalogram experiment data; and based on wireless connection between the electroencephalogram equipment and the server, sending the fused electroencephalogram experimental data to the server. According to the scheme, non-delay transmission of the content identification data can be achieved based on wired connection, the content identification data and the electroencephalogram experiment data are conveniently fused, the fused experiment data are conveniently sent to the server for experiment analysis, therefore, effective transmission of the electroencephalogram experiment data is achieved, high real-time performance and high stability are guaranteed, and user experience is improved. And the electroencephalogram experiment can be stably carried out.

According to one aspect, the present description relates to a portable device (10) for acquiring electroencephalographic (EEG) signals emitted by a user. The portable device comprises a flexible support (11) intended to fit a localized region of the skull of the user and a set of sensors (13) of electrical signals generated by the neuronal activity of the user, arranged on said support (11) so as to form contacts with the scalp when the device is worn by the user. For each sensor, an electronic circuit for filtering and amplifying electrical signals detected by said sensor is incorporated in the flexible support (11) and forms, with said sensor, an active electrode. The portable device also comprises a housing (12) comprising an electronic system for processing signals from said electronic filtering and amplification circuits, said housing being linked mechanically to the flexible support to form, with said support, a means of attachment to a garment or accessory intended to be worn by the user.

This disclosure relates to an emotion recognition and neural modulation system and method based on bilateral amygdala phase difference, comprising: a physiological signal acquisition device for acquiring a user's physiological signals; a facial expression acquisition device for acquiring the user's facial expressions and eye movement information; an electroencephalogram (EEG) device for acquiring the user's electroencephalogram (EEG) signals; a magnetic resonance imaging (MRI) device for acquiring the user's brain activity map; and a data processing module for preprocessing the physiological signals, facial expressions, and eye movement information before inputting them into an emotion recognition model to obtain an emotion category, determining neural activity patterns based on the EEG signals and brain activity map using a neural network model, and adjusting the operating parameters of the emotion recognition model, the EEG device, and the MRI device based on the comparison results of the neural activity patterns and the emotion category. Using the above technical solution, high-precision monitoring and regulation of emotional states and neural activity are achieved.

A method and system for tracking visual attention are disclosed. By generating at least one visual stimulus with a characteristic modulation, the characteristic modulation being applied to high spatial frequency, HSF, components of the visual stimulus and displaying the or each visual stimulus via a graphical user interface, GUI, of a display, a neural response may be induced in the user's brain. By receiving neural signals of a user from a neural signal capture device, such as an EEG device, a point of focus of the user (when the user views the visual stimulus) may be determined based on the neural signals, since the neural signals include information associated with the characteristic modulation of a visual stimulus to which the user's visual attention is directed.

An electroencephalography (EEG) system comprises: an EEG device; a display; one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for: receiving measurement data; extracting, from the measurement data, a first set of features and a second set of features; inputting the first set of features and the second set of features into a first treatment-specific machine-learning model and a second treatment-specific machine-learning model, respectively; generating a data structure based on the predicted treatment responses; and rendering, on the display, the generated data structure to provide the predicted treatment responses to the first candidate treatment of the CNS disease and the second candidate treatment of the CNS disease.

This application discloses a multimodal synchronous brain imaging system based on nuclear magnetic resonance-optical-electrical signals, relating to the field of brain imaging technology. The system includes: a PET-MR device, an EEG device, a detector headgear, and an eye tracker. The PET-MR device is an integrated device of a PET device and an MR device. The EEG device includes an EEG receiver and EEG electrodes. The detector headgear includes an NIRS detector and EEG electrodes. The PET-MR device is used for positron emission tomography (PET) and magnetic resonance imaging (MRI). The detector headgear is used to acquire EEG signals and blood flow infrared signals. The eye tracker is used to acquire eye movement trajectories. The PET device, eye tracker, NIRS detector, and EEG device synchronously acquire multimodal data based on a synchronization signal provided by a clock source within the MR device. This application improves the comprehensiveness and accuracy of brain imaging.

The invention relates to the technical field of man-machine interaction and multimedia control, in particular to an emotion dynamic adjustment algorithm and system based on EEG signal driving, and the algorithm comprises the following steps: S1, collecting an EEG signal of a player in real time through EEG equipment, and carrying out the preprocessing of the EEG signal, including filtering, denoising and artifact elimination; s2, inputting the preprocessed EEG signals into a convolutional neural network + BiLSTM model, extracting deep emotional features, and outputting an emotional state value; and S3, according to the current emotional state vector and the historical emotional data, judging whether the emotion of the player is in a target interval or not, if the emotional state deviates from the target interval, generating an adjusting instruction, and dynamically adjusting game content parameters. According to the emotion dynamic adjustment algorithm and system based on EEG signal driving, closed-loop control over player state perception and game content is achieved, the immersion feeling of players is improved, the game duration is prolonged, and personalized dynamic adjustment is provided for musical play type sound games.

Embodiments of a system for controlling an object using brainwaves are disclosed. The system includes a set of EEG electrodes configured to be positioned on a head of a user and to collect EEG signals. The system further includes one or more computer readable storage mediums storing a framework configured to execute an extensible architecture through which EEG signals are interpreted for control of the object. The framework includes an EEG device plugin associated with the set of EEG electrodes and configured to extract the EEG signals from the set of EEG electrodes. The framework also includes an interpreter plugin configured to convert the EEG signals extracted by the EEG device plugin into a command. Further, the framework includes an object control plugin configured to access the command through an extension point of the interpreter plugin and to execute the command to control the object.

The invention discloses a dance intervention training method for improving the function of a human body. The method comprises the following steps of 1, data collection, wherein electroencephalogram signals of the human body during different dance actions are recorded through electroencephalogram equipment; step 2, stretching limbs; in the step of stretching the limbs, the limbs are stretched, and the actions are large in motion range, smooth and high in ductility; step 3, mode identification: analyzing the electroencephalogram data obtained in the step 1, finding out a most stable electroencephalogram characteristic mode which is most related to a specific intention or behavior of'body stretching action ', and identifying the action in the mode as a training action; and 4, dance intervention training: combining a plurality of training actions to form a series of dance actions for training. Neurophysiological association between dance actions is monitored in real time through an electroencephalogram, and human muscle training is performed in combination with the dance actions, so that recovery of human body functions is driven.