A wearable apparatus for display glasses is provided. According to certain embodiments, the apparatus includes a display configured to provide a display of information that includes at least two options for selection. The apparatus further includes an electromyograph device and a processor. The electromyograph device is configured to track muscle activity of a wearer of the display glasses. The processor is configured to determine a plurality of events based on the muscle activity. The plurality of events are associated with at least one of types of the muscle activity, occurring numbers of the types of the muscle activity, or occurring time of the types of the muscle activity. One of the at least two options is identified based on the plurality of events.

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.

The present invention is a computer-implemented method comprising: receiving, by one or more processors, data from an electroencephalogram device worn by a user, wherein data is collected related to at least one brainwave; separating the collected data into individual data streams related to the one or more brainwaves; performing at least one manipulation to each of the individual data streams, wherein each of the data streams are manipulated to produce a sound applying at least one filter to each of the sounds; and generating each of the sounds, wherein a musical composition is formed.

The present invention is a computer-implemented method comprising: receiving, by one or more processors, data from an electroencephalogram device worn by a user, wherein data is collected related to at least one brainwave; separating the collected data into individual data streams related to the one or more brainwaves; performing at least one manipulation to each of the individual data streams, wherein each of the data streams are manipulated to produce a sound applying at least one filter to each of the sounds; and generating each of the sounds, wherein a musical composition is formed.

An example system disclosed herein for transforming neuro-response data into media ratings includes a data collector to obtain first neuro-response from a first subject exposed to a first media and second neuro-response data from a second subject exposed to a second media. The first media broadcast is before a time of the second media. The example system includes an analyzer to integrate the first neuro-response data with ratings data for the first media to generate a first rating for the first media. The ratings data is based on set-top box data associated with a media presentation device presenting the first media. The analyzer is to transform the second neuro-response data into a second rating for the second media based on the first rating.

An example system disclosed herein for transforming neuro-response data into media ratings includes a data collector to obtain first neuro-response from a first subject exposed to a first media and second neuro-response data from a second subject exposed to a second media. The first media broadcast is before a time of the second media. The example system includes an analyzer to integrate the first neuro-response data with ratings data for the first media to generate a first rating for the first media. The ratings data is based on set-top box data associated with a media presentation device presenting the first media. The analyzer is to transform the second neuro-response data into a second rating for the second media based on the first rating.

Novel tools and techniques for assessing, predicting and/or estimating effectiveness of hydration of a patient and/or an amount of fluid needed for effective hydration of the patient, in some cases, noninvasively.

A method for classification processing of an electrophysiological signal, including acquiring an electrophysiological signal collected by an acquisition device, and acquiring a channel association feature corresponding to the acquisition device. The channel association feature indicates spatial locations of multiple acquisition channels of the acquisition device, each of the multiple acquisition channels collecting the electrophysiological signal at a respective spatial location. The method further includes extracting a time feature corresponding to the electrophysiological signal, and generating an embedded feature based on the channel association feature and the time feature, and extracting a spatial feature corresponding to the embedded feature, and obtaining a classification result corresponding to the electrophysiological signal based on the spatial feature.

Provided are a method and device for predicting a user state according to an embodiment of the present invention. The method for predicting a user state according to an embodiment of the present invention comprises the steps of: acquiring first biometric data for a plurality of users; fine-tuning a prediction model on the basis of the first acquired biometric data and a fixed learning parameter; outputting a predicted user state using a fine-tuned prediction model by inputting a second biometric data for predicting the user state for predicting the user state for at least one user, wherein the fixed learning parameter is extracted on the basis of a first model that is different from the prediction model and trained to predict a user state for the plurality of users by inputting the first biometric data for the plurality of users.