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.

A pixel light source apparatus, system and method are disclosed for a stimulus source for visual pathway testing. A representative system includes a plurality of pixel light sources and a control and driver circuit. The pixel light sources are coupled to each other to form a partially-spherical dome, with each pixel light source arranged to emit light directed to a convergent location of the partially-spherical dome. The control and driver circuit implements a selected stimulus protocol to selectively energize each pixel light source to generate light stimulation to any arbitrary or selected portion of the retina of an eye of a human subject. A representative pixel light source comprises at least one elongated and optically opaque side wall; a rear wall; an illumination source; a first optical element defining a first light chamber; and a second optical element defining a second light chamber.

A system for assessing a mental health disorder in a human subject, the system comprising: a display configured to display a series of natural test images to the subject; an input by which the subject can input a response, following the display of each test image, as to whether or not the test image satisfies a predetermined categorization criterion; a control processor configured to control the display of the test images by the display, to measure the duration of time from when each test image is initially displayed to when the corresponding response is input by the subject, and to generate a set of response data including the response times in respect of each of the test images; and a data processor configured to process the set of response data and to compare the processed response data with reference data to assess whether or not the subject has, or is likely to develop, the mental health disorder.

The present invention is directed to an apparatus for identifying the rate of frequency at which a person perceives distinct visual images or sounds. The device, product and method comprise means for presenting the image(s) or sounds, means for selecting the distinct image(s) or sounds, means for selecting an instance number, presenting the image or sound in an amount equal to the instance number, the distinct image being displayed on a screen, the sound being presented via a speaker, at a first frequency rate at which frequency the person in unable to perceive the correct number of images or sounds, means for decreasing the frequency rate presentation of the distinct image or sound to a second frequency rate, the second frequency rate being the frequency at which the person is able to perceive individual occurrences of the image or sound in an amount equal to the instance number, and capturing the value of the second display frequency. The captured display frequencies are instrumental in assessing and determining the rate or speed at which the tested person's brain perceives images or sound.

In described embodiments, a device and method for diagnosing brain and neurological issues is provided. The device measures the performance of Convergence, Divergence, and binocular tracking capabilities of a subject's eyes, which can be used to determine whether a subject has experienced a brain or other neurological event.

A system includes (100) includes a display (112) that sequentially displays images from an image data set at a predetermined rate, an optical attention monitoring device (120) that senses a characteristic indicative of a clinician's attention to each displayed image of the image data set, and a processor (106) that executes an attention detection module (118) that detects a lapse in attention with respect to one or more of the displayed images based on the sensed characteristic indicative of the clinician's attention and generates a signal indicating the one or more of the displayed images.

A control system for controlling an accommodation demand detection in an ophthalmic technical system includes at least one sensor configured to detect a signal sequence from a body itself or an environmental signal sequence and to convert the detected signal sequence into measurement signals, and at least one detector configured to convert the measurement signals into a control signal that influences the accommodation demand detection. The detector includes a signal processor configured to compare the measurement signals with a reference signal bandwidth and to generate the control signal When the measurement signals are covered by a predetermined reference signal bandwidth.

Methods and systems for assessing a health state of a person via eye movement-driven biometric systems are provided. Examples of the health states that it would be possible to detect with such a system are but not limited to brain injuries (e.g., concussions), dementia, Parkinson's disease, post-traumatic stress syndrome, schizophrenia, fatigue, cybersickness, autism, Bipolar Disorder and other health conditions that manifest themselves in abnormal behavior of the human visual system. Described methods and systems can also detect influence of alcohol and/or drugs. The system extracts biometric template of a person by deriving features from the captured eye movement signal. The system may compare the difference between previous healthy state of a tested person and newly captured template or an averaged biometric template created from the records of multiple healthy people state of multiple people and a newly captured template from a person who needs to be tested. Based on the difference between the templates, a decision of a health state of a person is made. Described methods and systems may work on any device that has eye tracking capabilities including but not limited to desktop mounted eye tracking systems, head mounted eye tracking systems such as Virtual Reality and Augmented Reality or stand-alone mounted eye tracking systems.

A fatigue-degree monitoring device includes a first-fatigue-indicator calculating section configured to calculate a first indicator concerning fatigue of a user on the basis of operation from an operation section, a second-fatigue-indicator calculating section configured to calculate a second indicator concerning the fatigue of the user on the basis of biological information of the user, and a fatigue determining section configured to determine a fatigue degree of the user on the basis of the first indicator and the second indicator.

A method of discovering relationships between iris physiology and cognitive states and/or emotional states of a subject includes providing a computing device and a video camera to record a close-up view of the subject's eye. A first light is held to the lower eyelid skin and a second light a distance away illuminating the stroma of the eye. The first and second light are electronically synced together and configured to flash alternatively. The user engages in a plurality of tasks while recording ocular information which is processed to identify correlations between the responses in the iris musculature and the distortions in the stroma through the use optimized algorithms. One can then identifying at least one predictive distortion is identified in the stroma capturable solely with a visible-spectrum camera correlating to a predicted responses in the iris musculature.