One embodiment provides a method, including: obtaining EEG data from one or more single channel EEG sensor worn by a user; classifying, using a processor, the EEG data as one of nominal and abnormal; and providing an indication associated with a classification of the EEG data. Other embodiments are described and claimed.

One embodiment provides a method, including: obtaining EEG data from one or more single channel EEG sensor worn by a user; classifying, using a processor, the EEG data as one of nominal and abnormal; and providing an indication associated with a classification of the EEG data. Other embodiments are described and claimed.

A neurofeedback method, including: recording electrical signals from at least one brain region of a subject, wherein changes in the recorded electrical signals over time indicate changes in an activity level of the at least one brain region; providing an audio signal having a perceived quality based on the recorded electrical signals and according to an activity level of the at least one brain region; delivering the audio signal to the subject during said recording.

A neurofeedback method, including: recording electrical signals from at least one brain region of a subject, wherein changes in the recorded electrical signals over time indicate changes in an activity level of the at least one brain region; providing an audio signal having a perceived quality based on the recorded electrical signals and according to an activity level of the at least one brain region; delivering the audio signal to the subject during said recording.

The disclosure is related configuring a vehicle according to a user profile, where an individual user profile can be selected from a registry containing one or more user profiles using electroencephalogram (EEG). An additional biometric, such as gait, can also be used. The user profiles can include a variety of the user preferences, such as the preferred climate, seat settings, mode of payment, route to take, and more. A mobile device can also be used to add a layer of security and privacy, where the mobile device has sole access to the user's user profile.

A method and system for providing a user with virtual objects within an environment, characterizing interactions with the virtual objects using a brain computer interface, and modulating features of the virtual objects based upon improved classifiers associated with the interactions. The method and system can be used to rapidly customize virtual objects to a specific user in applications related to increasing engagement with traditional and new media content, virtual and augmented reality products, streamlining interactions with input devices in digital and physical environments, providing user authentication tools, providing more secure cybersecurity features, and delivering tailored content to users.

This invention is a non-invasive Brain to Computer Interface (BCI) method for interpreting a word, phrase, or command from brain activity by identifying a pattern of electromagnetic brain activity which occurs when a person uses different action modalities to communicate a word, phrase, or command. This method can enable people with neuromuscular limitations and/or paralysis to communicate. It can also enable people to communicate and/or to control environmental devices via their thought patterns in situations where communication via touch screen, keyboard, mouse, voice command, or gesture recognition is not appropriate and/or possible.

A system for non-invasive estimation of dementia progression. The system includes a computer device and a server. The computer device obtains an image of a subject's head from at least one angle. The server and/or computer device includes a plurality of machine learning models configured to: analyze the image for patterns related to dementia symptoms; and estimate progress of said dementia symptoms of said subject based on the analysis. The server and/or computer device pre-processes the image by performing a plurality of pre-processing steps comprising: importing the image; detecting eyes and shape of the head based on a previously trained machine learning model; rotating the image based on detection of the eyes and shape of the head; normalizing the image to one standard.

A system for non-invasive estimation of dementia progression. The system includes a computer device and a server. The computer device obtains an image of a subject's head from at least one angle. The server and/or computer device includes a plurality of machine learning models configured to: analyze the image for patterns related to dementia symptoms; and estimate progress of said dementia symptoms of said subject based on the analysis. The server and/or computer device pre-processes the image by performing a plurality of pre-processing steps comprising: importing the image; detecting eyes and shape of the head based on a previously trained machine learning model; rotating the image based on detection of the eyes and shape of the head; normalizing the image to one standard.

An information processor according to an aspect of the present disclosure includes a deriving unit that derives a cognitive capacity of a user on a basis of dispersion of reaction times of the user for a plurality of requests.