The present disclosure relates to an EEG measuring device which is in close contact with a head of a subject while having an adjustable size to fit the head of the subject.

A system may obtain a set of features characterizing a segment of inertial measurement unit (IMU) data generated by an IMU of an ear-wearable device. The system may apply a machine learning model (MLM) that takes the features characterizing the segment of the IMU data as input. The system may determine, based on output values produced by the MLM, whether a user of the ear-wearable device has potentially been subject to physical abuse. The system may then perform an action in response to determining that the user of the ear-wearable device has potentially been subject to physical abuse.

One variation of a method for hosting mobile access to dense electroencephalography data includes: receiving a set of signals, in a raw resolution, recorded by a set of channels in an electroencephalography headset during an electroencephalography test; receiving, from a client computing device, a view parameters for viewing the set of signals on a display; calculating a quantity of raw signal points per pixel column of the display based on the view parameters and a length of a segment of the electroencephalography test; for each signal in the set of signals, for each discrete contiguous sequence of the quantity of raw signal points within the segment of the signal, calculating a value set characterizing the discrete contiguous sequence of the quantity of raw signal points in the signal; and generating a static image representing value sets for each channel, in the set of channels, across the segment of the electroencephalography test.

One variation of a method for hosting mobile access to dense electroencephalography data includes: receiving a set of signals, in a raw resolution, recorded by a set of channels in an electroencephalography headset during an electroencephalography test; receiving, from a client computing device, a view parameters for viewing the set of signals on a display; calculating a quantity of raw signal points per pixel column of the display based on the view parameters and a length of a segment of the electroencephalography test; for each signal in the set of signals, for each discrete contiguous sequence of the quantity of raw signal points within the segment of the signal, calculating a value set characterizing the discrete contiguous sequence of the quantity of raw signal points in the signal; and generating a static image representing value sets for each channel, in the set of channels, across the segment of the electroencephalography test.

Provided are a method, server, and computer program for designing a customized headgear for transcranial direct current stimulation. According to one embodiment, there is provided a method for designing a customized headgear for transcranial direct current stimulation performed by a computing device and applying electrical stimulation to a preset target point in a brain of a subject, the method including: acquiring a head image of the subject; generating a headgear mask by using the acquired head image; generating a stimulator mask by using an optimal stimulation position combination for applying the electrical stimulation to the preset target point; and generating a customized headgear mask for the subject by performing a subtraction operation between the generated headgear mask and the generated stimulator mask.

Systems, devices, and methods described herein relate to multi-sensory presentation devices, including virtual reality (VR) devices, visual display devices, sound devices, haptic devices, and other forms of presentation devices, that are configured to present sensory elements, including visual and/or audio scenes, to a user. In some embodiments, one or more sensors including electroencephalography (EEG) sensors and a photoplethysmography (PPG) sensors, e.g., included in a brain-computer interface, can measure physiological data of a user to monitor a state of the user during the presentation of the visual and/or audio scenes. Such systems, devices, and methods can adapt one or more visual and/or audio scenes based on user physiological data, e.g., to control or manage the state of the user.

A computer network implemented system for improving the operation of one or more biofeedback computer systems is provided. The system includes an intelligent bio-signal processing system that is operable to: capture bio-signal data and in addition optionally non-bio-signal data; and analyze the bio-signal data and non-bio-signal data, if any, so as to: extract one or more features related to at least one individual interacting with the biofeedback computer system; classify the individual based on the features by establishing one or more brain wave interaction profiles for the individual for improving the interaction of the individual with the one or more biofeedback computer systems, and initiate the storage of the brain waive interaction profiles to a database; and access one or more machine learning components or processes for further improving the interaction of the individual with the one or more biofeedback computer systems by updating automatically the brain wave interaction profiles based on detecting one or more defined interactions between the individual and the one or more of the biofeedback computer systems. A number of additional system and computer implemented method features are also provided.

A computer network implemented system for improving the operation of one or more biofeedback computer systems is provided. The system includes an intelligent bio-signal processing system that is operable to: capture bio-signal data and in addition optionally non-bio-signal data; and analyze the bio-signal data and non-bio-signal data, if any, so as to: extract one or more features related to at least one individual interacting with the biofeedback computer system; classify the individual based on the features by establishing one or more brain wave interaction profiles for the individual for improving the interaction of the individual with the one or more biofeedback computer systems, and initiate the storage of the brain waive interaction profiles to a database; and access one or more machine learning components or processes for further improving the interaction of the individual with the one or more biofeedback computer systems by updating automatically the brain wave interaction profiles based on detecting one or more defined interactions between the individual and the one or more of the biofeedback computer systems. A number of additional system and computer implemented method features are also provided.

The present invention relates to a quantitative electroencephalogram (QEEG) monitor and system capable of monitoring and displaying simultaneously neuropathological characteristic and activity of both sides of a subject's brain. The methods include various indices and examination of differences in these indices by which neurophysiological conditions or problems can be identified and treated. These methods, and the systems and devices using these methods preferably can be used for identifying these neurophysiological conditions or brain dysfunction with monitors and methods for seizure detection, for sedation monitoring, for anesthesia monitoring, and the like. These bilateral brain monitoring methods and systems, and the devices using these methods can be used by individuals or clinicians with little or no training in signal analysis or processing. These bilateral monitoring methods can also be used in a range of applications.

The present invention relates to a quantitative electroencephalogram (QEEG) monitor and system capable of monitoring and displaying simultaneously neuropathological characteristic and activity of both sides of a subject's brain. The methods include various indices and examination of differences in these indices by which neurophysiological conditions or problems can be identified and treated. These methods, and the systems and devices using these methods preferably can be used for identifying these neurophysiological conditions or brain dysfunction with monitors and methods for seizure detection, for sedation monitoring, for anesthesia monitoring, and the like. These bilateral brain monitoring methods and systems, and the devices using these methods can be used by individuals or clinicians with little or no training in signal analysis or processing. These bilateral monitoring methods can also be used in a range of applications.