A method for detecting the presence of slowing patterns in an EEG sample comprising a plurality of channels of EEG signals, each channel comprising one or more segments, the method comprising: obtaining a first classifier that is trained to classify EEG samples as containing abnormal slow waves or not; performing a sequence of artifact removal processes on the EEG sample to generate a preprocessed EEG sample; extracting a first feature set from the preprocessed EEG sample; and passing the first feature set to the first classifier to predict whether the EEG sample contains abnormal slow waves or not; wherein the sequence of artifact removal processes comprises removal of one or more ocular artifacts and removal of one or more electrode artifacts.

The present disclosure describes a method for neuromodulating a subject, comprising providing (i) a cognitively engaging content, and (ii) a gamma oscillation inducing waveform, wherein said gamma oscillation inducing waveform: comprises an intensity that renders said gamma oscillation inducing waveform imperceptible to said subject; and causes a therapeutic improvement in a cognitive function, thereby neuromodulating said subject.

The present disclosure describes a method for neuromodulating a subject, comprising providing (i) a cognitively engaging content, and (ii) a gamma oscillation inducing waveform, wherein said gamma oscillation inducing waveform: comprises an intensity that renders said gamma oscillation inducing waveform imperceptible to said subject; and causes a therapeutic improvement in a cognitive function, thereby neuromodulating said subject.

A system may access electroencephalography (EEG) data of a subject, the EEG data including responses of the subject to an activation procedure. A system may analyze the EEG data including a plurality of epochs corresponding to the responses to identify first peaks, second peaks, and third peaks in one or more epochs. A system may determine values of a parameter in the plurality of epochs, the parameter being a characteristic of the first peak, the second peak, and/or the third peak. A system may generate a visual representation of the EEG data of the subject, the visual representation including an illustration of the first peak, the second peak and/or the third peak of a representative epoch or a heatmap compiled from the plurality of epochs, and the visual representation further including a graphical representation of the parameter that is presented as evidence of whether the subject is cognitively impaired.

A system may access electroencephalography (EEG) data of a subject, the EEG data including responses of the subject to an activation procedure. A system may analyze the EEG data including a plurality of epochs corresponding to the responses to identify first peaks, second peaks, and third peaks in one or more epochs. A system may determine values of a parameter in the plurality of epochs, the parameter being a characteristic of the first peak, the second peak, and/or the third peak. A system may generate a visual representation of the EEG data of the subject, the visual representation including an illustration of the first peak, the second peak and/or the third peak of a representative epoch or a heatmap compiled from the plurality of epochs, and the visual representation further including a graphical representation of the parameter that is presented as evidence of whether the subject is cognitively impaired.

One embodiment of an exemplary ambulatory seizure monitoring method calculates a phase lock value synchrony level of a neurological signal of an individual; detects an onset of a seizure event for the individual by comparing the phase lock value synchrony level with a patient threshold for the individual; and transmits a notification to a remote communication device indicating the onset of the seizure event for the individual.

One embodiment of an exemplary ambulatory seizure monitoring method calculates a phase lock value synchrony level of a neurological signal of an individual; detects an onset of a seizure event for the individual by comparing the phase lock value synchrony level with a patient threshold for the individual; and transmits a notification to a remote communication device indicating the onset of the seizure event for the individual.

A water resistant biometric data logging system for an animal including a housing containing an electrophysiological data logging device and an underwater connector configured to route and waterproof at least 10 electrode cables to the electrophysiological data logging device. Further disclosed is an apparatus (e.g., headcap) for mounting electrodes onto the animal. The apparatus includes a first layer comprising a first plurality of openings or holes, the first layer comprising a foam or a sacrificial material; a second layer comprising a second plurality of openings or holes, the second layer comprising or consisting essentially of synthetic rubber; and a potted piece containing a plurality of electrode cables, wherein the electrode cables are routed from the potted piece through the first plurality of openings and the second plurality of openings or holes.

The technology provides a multi-body earpiece suitable for use as an in-ear sensor system, which can be used for biometrics or a human-computer interface. The multi-body earpiece includes two body elements connected together by a flexure. These components provide at least 3 points of contact along different parts of the outer ear, in which the flexure is tethered to the two bodies and arranged to lock them in place during wear. In addition to having stability from moving while minimizing sound occlusion, this arrangement enables any electrodes for the on-board sensor(s) to remain in contact with the skin of the ear, and provide as many contact points in desired areas as the electronics dictate for the signals of interest.

An apparatus comprises at least one processing device comprising a processor coupled to a memory. The at least one processing device is configured to select physiologic data to be visualized, the selected physiologic data comprising a given set of one or more physiologic parameters collected from a given set of one or more subjects over a given period of time. The at least one processing device is also configured to determine a plot type for visualization of the selected physiologic data, the plot type being determined based at least in part on a repetitiveness of the selected physiologic data over a plurality of time segments in the given period of time. The at least one processing device is further configured to generate a visualization of the selected physiologic data utilizing the determined plot type, and to output the generated visualization of the selected physiologic data via an interactive graphical user interface.