Data quality is essential to the validity of research results and to the quality of gaze interaction. We argue that the lack of standard measures for eye data quality makes several aspects of manufacturing and using eye trackers, as well as researching eye movements and vision, more difficult than necessary. Uncertainty regarding the comparability of research results is a considerable impediment to progress in the field. In this paper, we illustrate why data quality matters and review previous work on how eye data quality has been measured and reported. The goal is to achieve a common understanding of what data quality is and how it can be defined, measured, evaluated, and reported. 1 1 We thank the members of the COGAIN Technical Committee (see www.cogain.org/EyeDataQualityTC) for the standardisation of eye data quality for their ongoing participation and comments to this text.

The precision of an eye-tracker is critical to the correct identification of eye movements and their properties. To measure a system's precision, artificial eyes (AEs) are often used, to exclude eye movements influencing the measurements. A possible issue, however, is that it is virtually impossible to construct AEs with sufficient complexity to fully represent the human eye. To examine the consequences of this limitation, we tested currently used AEs from three manufacturers of eye-trackers and compared them to a more complex model, using 12 commercial eye-trackers. Because precision can be measured in various ways, we compared different metrics in the spatial domain and analyzed the power-spectral densities in the frequency domain. To assess how precision measurements compare in artificial and human eyes, we also measured precision using human recordings on the same eyetrackers. Our results show that the modified eye model presented can cope with all eye-trackers tested and acts as a promising candidate for further development of a set of AEs with varying pupil size and pupil-iris contrast. The spectral analysis of both the AE and human data revealed that human eye data have different frequencies that likely reflect the physiological characteristics of human eye movements. We also report the effects of sample selection methods for precision calculations. This study is part of the EMRA/COGAIN Eye Data Quality Standardization Project.Keywords Eye movements . Artificial eye . Precision . Data quality . Eye-tracker noise . Power-spectral density High-quality eye movement data are a prerequisite for the valid measurement of fixation durations, saccade amplitudes and velocities, and many other behavioral measures in eye movement research. Spatial accuracy and precision are two of the most important aspects of eye data quality. Accuracy is defined as the difference between the tracker-estimated gaze position and the actual gaze position, whereas precision is defined as the ability to reliably reproduce a measurement, given a fixating eye (ideally, a stable eye)-see Fig. 1. Accuracy and precision are two independent measurements of eye-tracking data quality-that is, they can be both good or poor, or one good and the other poor. The most commonly used measures of precision in eye-trackers are the sample-tosample root mean square angular displacement [RMS(S2S)] and the standard deviation (SD) of samples in a given time window. These values will change not only dependent on the actual precision level of the tracker but also on the calculation used, and the samples or time period included in that calculation. Longer periods increase the probability of fixational eye movements (tremor, microsaccades, and drift), which in turn will increase imprecision. Holmqvist, Nyström, and Mulvey (2012) showed that artificially increasing imprecision via the addition of Gaussian noise from 0.03°to 0.30°results in an increase of up to 200 ms in calculated fixation durations. If this range of noise, as measured by RMS(S2S) precis...

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Abstract-This paper introduces the concept of using gaze as a sole modality for fully controlling player characters of fastpaced action computer games. A user experiment is devised to collect gaze and gameplay data from subjects playing a version of the popular Super Mario Bros platform game. The initial analysis shows that there is a rather limited grid around Mario where the efficient player focuses her attention the most while playing the game. The useful grid as we name it, projects the amount of meaningful visual information a designer should use towards creating successful player character controllers with the use of artificial intelligence for a platform game like Super Mario.Information about the eyes' position on the screen and the state of the game are utilized as inputs of an artificial neural network, which is trained to approximate which keyboard action is to be performed at each game step. Results yield a prediction accuracy of over 83% on unseen data samples and show promise towards the development of eye-controlled fast-paced platform games. Derived neural network players are intended to be used as assistive technology tools for the digital entertainment of people with motor disabilities.

Infrared light is the most common choice for illumination of the eye in current eye trackers, usually produced via IR light-emitting diodes (LEDs). This chapter provides an overview of the potential hazards of over-exposure to infrared light, the safety standards currently in place, configurations and lighting conditions employed by various eye tracking systems, the basics of measurement of IR light sources in eye trackers, and special considerations associated with continuous exposure in the case of gaze control for communication and disabled users. It should be emphasised that any eye tracker intended for production should undergo testing by qualified professionals at a recognised test house, in a controlled laboratory setting. However, some knowledge of the measurement procedures and issues involved should be useful to designers and users of eye tracking systems.