An example system disclosed herein includes an analyzer to analyze first neuro-response data and second neuro-response data and a selector to identify a candidate location in source material for introduction of an advertisement or entertainment based on first neuro-response data and second neuro-response data. The analyzer is to detect a first pattern of oscillation in a first frequency band of third neuro-response data; detect a second pattern of oscillation in a second frequency band of the third neuro-response data; determine a degree of phase synchrony or amplitude synchrony based on the first pattern of oscillation and the second pattern of oscillation; and determine an effectiveness of the advertisement or entertainment based on the degree of phase synchrony or amplitude synchrony.

An example system disclosed herein includes an analyzer to analyze first neuro-response data and second neuro-response data and a selector to identify a candidate location in source material for introduction of an advertisement or entertainment based on first neuro-response data and second neuro-response data. The analyzer is to detect a first pattern of oscillation in a first frequency band of third neuro-response data; detect a second pattern of oscillation in a second frequency band of the third neuro-response data; determine a degree of phase synchrony or amplitude synchrony based on the first pattern of oscillation and the second pattern of oscillation; and determine an effectiveness of the advertisement or entertainment based on the degree of phase synchrony or amplitude synchrony.

Typically, high NREM stage N3 sleep detection accuracy is achieved using a frontal electrode referenced to an electrode at a distant location on the head (e.g., the mastoid, or the earlobe). For comfort and design considerations it is more convenient to have active and reference electrodes closely positioned on the frontal region of the head. This configuration, however, significantly attenuates the signal, which degrades sleep stage detection (e.g., N3) performance. The present disclosure describes a deep neural network (DNN) based solution developed to detect sleep using frontal electrodes only. N3 detection is enhanced through post-processing of the soft DNN outputs. Detection of slow-waves and sleep micro-arousals is accomplished using frequency domain thresholds. Volume modulation uses a high-frequency/low-frequency spectral ratio extracted from the frontal signal.

A biosignal-based avatar control system according to an embodiment of the present disclosure includes an avatar generating unit that generates a user's avatar in a virtual reality environment, a biosignal measuring unit that measures the user's biosignal using a sensor, a command determining unit that determines the user's command based on the measured biosignal, an avatar control unit that controls the avatar to perform the command, an output unit that outputs an image of the avatar in real-time, and a protocol generating unit for generating a protocol that provides predetermined tasks, and determines if the avatar performed the predetermined tasks. According to an embodiment of the present disclosure, it is possible to provide feedback in real-time by understanding the user's intention through analysis of biosignals and controlling the user's avatar in a virtual reality environment, thereby improving the user's brain function and motor function.

A biosignal-based avatar control system according to an embodiment of the present disclosure includes an avatar generating unit that generates a user's avatar in a virtual reality environment, a biosignal measuring unit that measures the user's biosignal using a sensor, a command determining unit that determines the user's command based on the measured biosignal, an avatar control unit that controls the avatar to perform the command, an output unit that outputs an image of the avatar in real-time, and a protocol generating unit for generating a protocol that provides predetermined tasks, and determines if the avatar performed the predetermined tasks. According to an embodiment of the present disclosure, it is possible to provide feedback in real-time by understanding the user's intention through analysis of biosignals and controlling the user's avatar in a virtual reality environment, thereby improving the user's brain function and motor function.

Various embodiments of an apparatus, methods, systems and computer program products described herein are directed to an Analytics Engine that receives one more signal files that include neural signal data of a user based on voltages detected by one or more electrodes on a set of headphones worn by a user. The Analytics Engine preprocesses the data, extracts features from the received data, and feeds the extracted features into one or more machine learning models to generate determined output that corresponds to at least one of a current mental state of the user and a type of facial gesture performed by the user. The Analytics Engine sends the determined output to a computing device to perform an action based on the determined output.

Various embodiments of an apparatus, methods, systems and computer program products described herein are directed to an Analytics Engine that receives one more signal files that include neural signal data of a user based on voltages detected by one or more electrodes on a set of headphones worn by a user. The Analytics Engine preprocesses the data, extracts features from the received data, and feeds the extracted features into one or more machine learning models to generate determined output that corresponds to at least one of a current mental state of the user and a type of facial gesture performed by the user. The Analytics Engine sends the determined output to a computing device to perform an action based on the determined output.

Exosuits that use core grip members are described herein. Core grip members apply forces in a radially inward manner from the exterior of the body to the interior of the body to provide support to the user and to serve as a platform for mounting power layer segments.

The present method and system provides a neuromodulation therapy including receiving a plurality of input data relating to a patient, the input data including brain value measurements. The method and system includes analyzing the input data in reference to reference data generated based on machine learning operations associated with existing patient data and reference database data. Based thereon, the method and system includes electronically determining, a brain malady and a severity value for the patient and electronically generating a treatment protocol for the patient, the treatment protocol includes transcranial stimulation parameters. Therein, the method and system includes applying a transcranial stimulation using the transcranial stimulation parameters based on the treatment protocol.

The present method and system provides a neuromodulation therapy including receiving a plurality of input data relating to a patient, the input data including brain value measurements. The method and system includes analyzing the input data in reference to reference data generated based on machine learning operations associated with existing patient data and reference database data. Based thereon, the method and system includes electronically determining, a brain malady and a severity value for the patient and electronically generating a treatment protocol for the patient, the treatment protocol includes transcranial stimulation parameters. Therein, the method and system includes applying a transcranial stimulation using the transcranial stimulation parameters based on the treatment protocol.