Disclosed and contemplated herein is a bispectral EEG (BSEEG) method which can detect patients with delirium and can detect delirium with systemic inflammation. In various implementations, a mouse model is used to detect delirium with systemic inflammation induced by lipopolysaccharide (LPS) injection.

Systems and methods for providing a computer-generated visualization of EEG data are disclosed. Raw EEG data generated from a multi-channel EEG headset (or other device) may be received. The EEG data may be run through a fast Fourier transform (FFT) to separate out various frequency components in each channel, isolating the brain wave components for each channel. A visual display may be generated based on the isolated components comprising a first display portion and a second display portion. The first display portion may comprise a geometrical mesh with predefined parameters representing the portions of a crystal. The second display portion may comprise a time-varying color visualization based on the variance of the brain waves. A composite computer display in which the first display portion is overlaid over the second display portion may be generated and provided via a display device.

Systems and methods for providing a computer-generated visualization of EEG data are disclosed. Raw EEG data generated from a multi-channel EEG headset (or other device) may be received. The EEG data may be run through a fast Fourier transform (FFT) to separate out various frequency components in each channel, isolating the brain wave components for each channel. A visual display may be generated based on the isolated components comprising a first display portion and a second display portion. The first display portion may comprise a geometrical mesh with predefined parameters representing the portions of a crystal. The second display portion may comprise a time-varying color visualization based on the variance of the brain waves. A composite computer display in which the first display portion is overlaid over the second display portion may be generated and provided via a display device.

The technology involves scaffold structures used for in-ear sensor systems. Such systems that can perform biometric signal detection or act as a human-computer interface. Scaffolding arrangements minimize the amount of material placed in the ear while providing a secure fitting device that can be worn for hours, days or longer in order to provide maximal benefit to the wearer. The scaffolding includes a C-shaped arcuate curvature for at least part of the housing. This configuration can act as a natural leaf spring to help maintain the housing in contact with different points along the ear. Sensors are located along various points of the scaffolding for use in different diagnostic situations. Different components of an on-board sensor input and processing system can be distributed along different parts of the scaffolding. Such structures beneficially minimize ambient sound occlusion and avoid the need of an exterior strap or clip worn around the ear.

The technology involves scaffold structures used for in-ear sensor systems. Such systems that can perform biometric signal detection or act as a human-computer interface. Scaffolding arrangements minimize the amount of material placed in the ear while providing a secure fitting device that can be worn for hours, days or longer in order to provide maximal benefit to the wearer. The scaffolding includes a C-shaped arcuate curvature for at least part of the housing. This configuration can act as a natural leaf spring to help maintain the housing in contact with different points along the ear. Sensors are located along various points of the scaffolding for use in different diagnostic situations. Different components of an on-board sensor input and processing system can be distributed along different parts of the scaffolding. Such structures beneficially minimize ambient sound occlusion and avoid the need of an exterior strap or clip worn around the ear.

Example portable devices, systems, and methods for monitoring hearing sensitivity are described herein. An example device includes a wearable member, a stimulator, and a sensor that includes a plurality of electrodes. The stimulator and the sensor are integrated into the wearable member. Additionally, the device includes a controller operably coupled to the stimulator and the sensor, and the controller is configured to deliver a stimulation signal to a subject's ear using the stimulator, and receive an electrophysiological response signal of the subject in response to the stimulation signal. The electrophysiological response signal is recorded by the sensor. Optionally, the controller is integrated into the wearable member.

Example portable devices, systems, and methods for monitoring hearing sensitivity are described herein. An example device includes a wearable member, a stimulator, and a sensor that includes a plurality of electrodes. The stimulator and the sensor are integrated into the wearable member. Additionally, the device includes a controller operably coupled to the stimulator and the sensor, and the controller is configured to deliver a stimulation signal to a subject's ear using the stimulator, and receive an electrophysiological response signal of the subject in response to the stimulation signal. The electrophysiological response signal is recorded by the sensor. Optionally, the controller is integrated into the wearable member.

Electroencephalography (EEG) data may be analyzed to calculate various metrics such as maximum amplitude projection, node visit frequency, node transition frequency, and/or node transition polarity. The calculated metrics may be provided graphically in some examples. In some examples, the metrics may be provided graphically in combination with other data such as raw EEG traces.

Electroencephalography (EEG) data may be analyzed to calculate various metrics such as maximum amplitude projection, node visit frequency, node transition frequency, and/or node transition polarity. The calculated metrics may be provided graphically in some examples. In some examples, the metrics may be provided graphically in combination with other data such as raw EEG traces.

Disclosed is an elastic electroencephalography dry electrode, comprising an electrode base and a plurality of dry electrode units, which are fixedly arranged on the electrode base, wherein each dry electrode unit comprises an elastic supporting element and at least one electrode probe element; one end of the elastic supporting element is fixed to the electrode base, and the other end thereof is connected to the electrode probe element; the electrode probe element is used for being in contact with a scalp to collect an electroencephalography signal; the elastic supporting element is used for transmitting the electroencephalography signal; and the electrode base comprises an electrode substrate element, an electrode unit fixing element and an electrode displacement limiting element. Further disclosed are an electroencephalography device and an application system.