Abstract: User predictive mental response profiles updating and usage, comprising receiving a plurality of images captured by one or more imaging sensors deployed to monitor one or more eyes of a user, analyzing at least some of the plurality of images to identify one or more eye dynamics signal patterns preceding one or more abnormal events occurring in an environment of the user, updating a response profile of the user based on an association of one or more of the abnormal event and the one or more of the identified eye dynamics signal patterns, and providing information based on the updated response profile of the user. The provided information is configured to enable one or more processing units to predict an imminent abnormal event based on an eye dynamics signal of the user. An action may be initiated by the one or more processing units to affect the environment accordingly.

Abstract: A method for updating response profiles of drivers, comprising receiving a plurality of images captured by one or more imaging sensors deployed to monitor one or more eyes of a driver of a vehicle, analyzing at least some of the plurality of images to identify one or more eye dynamics signal patterns preceding one or more abnormal driving events occurring in an environment of the vehicle, updating a response profile of the driver based on an association of one or more of the abnormal driving event and the one or more of the identified eye dynamics signal patterns and providing information based on the updated response profile of the driver. The provided information is configured to enable one or more control systems of the vehicle to predict an imminent abnormal driving event based on an eye dynamics signal of the driver.

Abstract: The present invention provides methods and systems for determining mental states of a user based on mental load index, comprising: analysing image data to identify feature(s) of at least one eye while presenting user with dynamic physiological-responsive stimuli; calculating, based on the identified feature(s), spatiotemporal fluctuations over time for the feature(s); calculating, based on the spatiotemporal fluctuations, spectral pattern(s) having a first and second frequency; calculating mental load pattern(s) by bounding ratio between the spectral patterns for the first and second frequencies in time interval(s) corresponding to stimuli level changes; identifying correlation between each mental load pattern(s) and the stimuli level in the time interval(s); and determining, based on the correlation, a mental load index comprising time interval(s) corresponding to a plurality of mental states identified by comparing each mental load pattern(s) to a threshold; and classifying mental state(s) based on the ment