Abstract: Method of analyzing eye tracking data for estimating user's cognitive and emotional level of consumption of visual information. A training machine learning model is trained using a data set containing gaze information of known training users, their known cognitive levels and their EEG signal measurements. A calibrating machine learning model is trained using a data set of calibrating visual information displayed to a user, calibrating gaze tracks of that user, calibrating actions data of that user, and calibrating session data related to the session environment. The device displays to that user a target visual information and records target eye tracking data of that user in response to consuming the target information. The recorded target eye tracking data is calibrated via the calibrating machine learning model. The calibrated target eye tracking data is fed into the training machine learning model, which estimates the cognitive levels of consumption of the target visual information of that user.

Abstract: Method of analyzing eye tracking data for estimating user's cognitive and emotional level of consumption of visual information. A training machine learning model is trained using a data set containing gaze information of known training users, their known cognitive levels and their EEG signal measurements. A calibrating machine learning model is trained using a data set of calibrating visual information displayed to a user, calibrating gaze tracks of that user, calibrating actions data of that user, and calibrating session data related to the session environment. The device displays to that user a target visual information and records target eye tracking data of that user in response to consuming the target information. The recorded target eye tracking data is calibrated via the calibrating machine learning model. The calibrated target eye tracking data is fed into the training machine learning model, which estimates the cognitive levels of consumption of the target visual information of that user.