Abstract: A method to measure a cognitive load based upon ocular information of a subject includes the steps of: providing a video camera configured to record a close-up view of at least one eye of the subject; providing a computing device electronically connected to the video camera and the electronic display; recording, via the video camera, the ocular information of the at least one eye of the subject; processing, via the computing device, the ocular information to identify changes in ocular signals of the subject through the use of convolutional neural networks; evaluating, via the computing device, the changes in ocular signals from the convolutional neural networks by a machine learning algorithm; determining, via the machine learning algorithm, the cognitive load for the subject; and displaying, to the subject and/or to a supervisor, the cognitive load for the subject.

Abstract: A method to optimize learning based upon ocular information of a subject includes providing a video camera for recording a close-up view of a subject's eye. A first electronic display shows a plurality of educational subject matter to the subject. A second electronic display shows an output to an instructor. Changes in ocular signals of the subject are processed through the use optimized algorithms. A cognitive state model determines a low to a high cognitive load experienced by the subject. The cognitive state model is evaluated based on the changes in the ocular signals for determining a probability of the low to the high cognitive load experienced by the subject. The probability of the low to the high cognitive load experienced by the subject is displayed to the instructor.

Abstract: A method of measuring non-invasive ocular metrics is used to diagnose a mental health state of a patient. The method includes presenting a stimuli on an electronic display screen and recording a video of at least one eye of a patient by a video camera. The stimuli is configured to elicit a change in an ocular signal of the patient's eye. Software processes image frames of the video through a series of optimized algorithms configured to isolate and quantify the at least one ocular signal by applying an image mask isolating components. An algorithm estimates a probability of a mental health state based on the change in the at least one ocular signal. The estimated mental health state can be shown to the patient or to a mental health professional.

Abstract: A method to optimize learning based upon ocular information of a subject includes providing a video camera for recording a close-up view of a subject's eye. A first electronic display shows a plurality of educational subject matter to the subject. A second electronic display shows an output to an instructor. Changes in ocular signals of the subject are processed through the use optimized algorithms. A cognitive state model determines a low to a high cognitive load experienced by the subject. The cognitive state model is evaluated based on the changes in the ocular signals for determining a probability of the low to the high cognitive load experienced by the subject. The probability of the low to the high cognitive load experienced by the subject is displayed to the instructor.