A display device and a method of manufacturing the same are disclosed. In one aspect, the display device includes a substrate including a separation area and a plurality of pixel formed over the substrate. The separation area is formed between adjacent pixels, and a plurality of through holes are respectively defined by a plurality of surrounding inner surfaces of the separation area, and wherein each of the inner surfaces passes through the substrate. The display device also includes an encapsulation layer formed over the substrate and covering the inner surfaces of the separation area.

The invention discloses an objective and quantitative detection method for amblyopia by electroencephalogram (EEG). The method comprises the following steps: firstly, carry out binocular dichoptic viewing display, then design a visual evoked stimulation paradigm, establish a brain-computer interface platform, build a test interaction interface, next determine an amblyopia EEG quantitative index. By using a suppression coefficient (SI) to describe the binocular suppression relationship, quantify the degree of amblyopia, and finally obtain amblyopia detection result feedback, where the computer interaction interface module presents a final amblyopia detection result to realize feedback of a user. The operation is simple and rapid, the applicability is high, and the indexes are objective and quantitative.

Disclosed are a testing method, a system and a device for flicker fusion frequency range. The testing method comprises: taking end point values of a self-preset range as starting judgment values; acquiring a flicker judgment result of a subject; if the flicker judgment result is flickering, continuing to execute the acquisition step by increasing a judgement value until the flicker judgment result turns into non-flickering, and defining a judgement value at the moment of turning as an judgment value.sub.up; if the flicker judgment result is non-flickering, continuing to execute the acquisition step by decreasing a judgement value until the flicker judgment result turns into flickering, and defining a judgement value at the moment of turning as an judgment value.sub.down; and acquiring a flicker fusion frequency range which takes a judgment value.sub.up and a judgment value.sub.down as end point values.

According to various aspects, a new concept of Steady-State Visually Evoked Potential (SSVEP) based Brain-Computer Interface (BCI) is described where brain-computer communication occurs by capturing SSVEP induced by consciously imperceptible visual stimuli integrated into, for example, a virtual scene. These consciously imperceptible visual stimuli are able to convey subliminal information to a computer. In various embodiments, computer based operations can be mapped to visual elements with associated flickering stimuli, and induced SSVEP can be detected when the user focused upon them. In various embodiments, these visual elements can be introduced into existing display without any perceivable change to content being displayed.

A display device and a method of manufacturing the same are disclosed. In one aspect, the display device includes a substrate including a separation area and a plurality of pixel formed over the substrate. The separation area is formed between adjacent pixels, and a plurality of through holes are respectively defined by a plurality of surrounding inner surfaces of the separation area, and wherein each of the inner surfaces passes through the substrate. The display device also includes an encapsulation layer formed over the substrate and covering the inner surfaces of the separation area.

Traditional cognitive load estimation techniques rely on raw pupil size alone which is often prone to confound with changes in illumination, errors associated with sensor devices and irregular oscillations of pupil under constant light conditions. Estimation of cognitive load finds application in many domains including optimum work allocation, assessing a work environment and medical diagnosis. The present disclosure employs frequency domain analysis of pupil size variations to estimate load imposed by a cognitive task. A cognitive load metric based on power and frequency relations at mean frequency of the variation in pupil size addresses cognitive load estimation based on pupil dilation, wherein the pupil dilation is captured by employing low cost non-intrusive nearables.

A method of deception detection based upon ocular information of a subject provides a video camera configured to record a close-up view of a subject's eye. A cognitive state model is configured to determine a high to a low cognitive load experienced by the subject. An emotional state model is configured to determine a high to a low state of arousal experienced by the subject. After asking a question, the ocular information is processed to identify changes in ocular signals of the subject. The cognitive state and emotional state models are evaluated based solely on the changes in ocular signals where a probability of the subject being either truthful or deceptive is estimated for a binary output.

A method of assessing operational risk based upon ocular information of a subject includes providing a video camera recording a close-up view of a subject's eye. The ocular information is processed to identify changes in ocular signals of the subject through the use of convolutional neural networks. Changes in ocular signals are evaluated from the convolutional neural networks by a machine learning algorithm. A duty fitness result is determined for the subject where the duty fitness result is either fit for duty, unfit for duty or more information needed. The results can then be displayed to the subject and/or to a supervisor.

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.

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.