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Eye tracking is prevalent in scientific and commercial applications. Recent computer vision and deep learning methods enable eye tracking with off-the-shelf webcams and reduce dependence on expensive, restrictive hardware. However, such deep learning methods have not yet been applied and evaluated for remote, online psychological experiments. In this study, we tackle critical challenges faced in remote eye tracking setups and systematically evaluate appearance-based deep learning methods of gaze tracking and blink detection. From their own homes and laptops, 65 participants performed a battery of eye tracking tasks including (i) fixation, (ii) zone classification, (iii) free viewing, (iv) smooth pursuit, and (v) blink detection. Webcam recordings of the participants performing these tasks were processed offline through appearance-based models of gaze and blink detection. The task battery required different eye movements that characterized gaze and blink prediction accuracy over a comprehensive list of measures. We find the best gaze accuracy to be 2.4° and precision of 0.47°, which outperforms previous online eye tracking studies and reduces the gap between laboratory-based and online eye tracking performance. We release the experiment template, recorded data, and analysis code with the motivation to escalate affordable, accessible, and scalable eye tracking that has the potential to accelerate research in the fields of psychological science, cognitive neuroscience, user experience design, and human–computer interfaces. [ABSTRACT FROM AUTHOR]

Eye behaviour provides valuable information revealing one’s higher cognitive functions and state of affect. Although eye tracking is gaining ground in the research community, it is not yet a popular approach for the detection of emotional and cognitive states. In this paper, we present a review of eye and pupil tracking related metrics (such as gaze, fixations, saccades, blinks, pupil size variation, etc.) utilized towards the detection of emotional and cognitive processes, focusing on visual attention, emotional arousal and cognitive workload. Besides, we investigate their involvement as well as the computational recognition methods employed for the reliable emotional and cognitive assessment. The publicly available datasets employed in relevant research efforts were collected and their specifications and other pertinent details are described. The multimodal approaches which combine eye-tracking features with other modalities (e.g. biosignals), along with artificial intelligence and machine learning techniques were also surveyed in terms of their recognition/classification accuracy. The limitations, current open research problems and prospective future research directions were discussed for the usage of eye-tracking as the primary sensor modality. This study aims to comprehensively present the most robust and significant eye/pupil metrics based on available literature towards the development of a robust emotional or cognitive computational model. [ABSTRACT FROM AUTHOR]

Eye-tracking experiments rely heavily on good data quality of eye-trackers. Unfortunately, it is often the case that only the spatial accuracy and precision values are available from the manufacturers. These two values alone are not sufficient to serve as a benchmark for an eye-tracker: Eye-tracking quality deteriorates during an experimental session due to head movements, changing illumination or calibration decay. Additionally, different experimental paradigms require the analysis of different types of eye movements; for instance, smooth pursuit movements, blinks or microsaccades, which themselves cannot readily be evaluated by using spatial accuracy or precision alone. To obtain a more comprehensive description of properties, we developed an extensive eye-tracking test battery. In 10 different tasks, we evaluated eye-tracking related measures such as: the decay of accuracy, fixation durations, pupil dilation, smooth pursuit movement, microsaccade classification, blink classification, or the influence of head motion. For some measures, true theoretical values exist. For others, a relative comparison to a reference eye-tracker is needed. Therefore, we collected our gaze data simultaneously from a remote EyeLink 1000 eye-tracker as the reference and compared it with the mobile Pupil Labs glasses. As expected, the average spatial accuracy of 0.57° for the EyeLink 1000 eye-tracker was better than the 0.82° for the Pupil Labs glasses (N = 15). Furthermore, we classified less fixations and shorter saccade durations for the Pupil Labs glasses. Similarly, we found fewer microsaccades using the Pupil Labs glasses. The accuracy over time decayed only slightly for the EyeLink 1000, but strongly for the Pupil Labs glasses. Finally, we observed that the measured pupil diameters differed between eye-trackers on the individual subject level but not on the group level. To conclude, our eye-tracking test battery offers 10 tasks that allow us to benchmark the many parameters of interest in stereotypical eye-tracking situations and addresses a common source of confounds in measurement errors (e.g., yaw and roll head movements). All recorded eye-tracking data (including Pupil Labs’ eye videos), the stimulus code for the test battery, and the modular analysis pipeline are freely available (https://github.com/behinger/etcomp).

The effect of blinks on the saccade frequency before the onset of step ramp smooth pursuit was assessed in healthy controls. Blinks elicited before smooth pursuit onset suppressed saccades in contrast to a control and a gap paradigm. The source of blink associated suppression of saccades remains unclear but is probably not caused by visual signals or the omnipause neurons. [ABSTRACT FROM AUTHOR]

Blinks are known to affect eye movements, e.g., saccades, slow and fast vergence, and saccade-vergence interaction, in two ways: by superimposition of blink-associated eye movements and changes of the central premotor activity in the brainstem. The goal of this study was to determine, for the first time, the effects of trigeminal evoked blinks on ongoing smooth pursuit eye movements which could be related to visual sensory or premotor neuronal changes. This was compared to the effect of a target disappearing for 100-300 ms duration during ongoing smooth pursuit (blank paradigm) in order to control for the visual sensory effects of a blink. Eye and blink movements were recorded in eight healthy subjects with the scleral search coil technique. Blink-associated eye movements during the first 50% of the blink duration were non-linearly superimposed on the smooth pursuit eye movements. Immediately after the blink-associated eye movements, the pursuit velocity slowly decreased by an average of 3.2±2.1°/s. This decrease was not dependent on the stimulus direction. The pursuit velocity decrease caused by blinks which occluded the pupil more than 50% could be explained mostly by blanking the visual target. However, small blinks that did not occlude the pupil (<10% of lid closure) also decreased smooth pursuit velocity. Thus, this blink effect on pursuit velocity cannot be explained by blink-associated eye movements or by the blink having blanked the visual input. We propose that part of this effect might either be caused by incomplete visual suppression during blinks and/or a change in the activity of omnipause neurons. [ABSTRACT FROM AUTHOR]

Blinks executed during eye movements affect kinetic eye movement parameters, e.show $132#g., peak velocity of saccades is decreased, their duration is increased, but their amplitude is not altered. This effect is mainly explained by the decreased activity of premotor neurons in the brainstem: omni-pause neurons (OPN) in the nucleus raphe interpositus. Previous studies examined the immediate effect of blinks directly on eye movements but not their effect when they are elicited several hundred milliseconds before the eye movements. In order to address this question we tested blinks elicited before the target onset of saccades and pursuit and compared the results to the gap effect: if a fixation light is extinguished for several hundred milliseconds, the reaction time (latency) for subsequent saccades or smooth pursuit eye movements is decreased. Monocular eye and lid movements were recorded in nine healthy subjects using the scleral search-coil system. Laser stimuli were front-projected onto a tangent screen in front of the subjects. Horizontal step-ramp smooth pursuit of 20 deg/s was elicited in one session, or 5 deg horizontal visually guided saccades in another experimental session. In one-third of the trials (smooth pursuit or saccades) the fixation light was extinguished for 200 ms before stimulus onset (gap condition), and in another third of the trials reflexive blinks were elicited by a short airpuff before the stimulus onset (blink condition). The last third of the trials served as controls (control condition). Stimulus direction and the three conditions were randomized for saccades and smooth pursuit separately. The latency of the step-ramp smooth pursuit in the blink condition was found to be decreased by 10 ms, which was less than in the gap condition (38 ms). However, the initial acceleration and steady-state velocity of smooth pursuit did not differ in the three conditions. In contrast, the latency of the saccades in the gap condition was decreased by 39 ms, but not in the blink condition. Saccade amplitude, peak velocity, and duration were not different in the three conditions. There was also no difference in blink amplitude and duration of pupil occlusion in the blink condition, neither in saccades nor in smooth pursuit. The latency reduction of smooth pursuit, but not of saccades, may neither be explained by the brief pupil occlusion nor by visual suppression, warning signals, or the startle response. Whether the effects are caused by the influence of blinks on OPNs or other premotor structures remains to be tested. [ABSTRACT FROM AUTHOR]