One of the most important tests available to assess human vision is the visual field test, or perimetry. While imaging of the fundus and standard tests such as Snellen charts can assess factors such as acuity, and identify damaged parts of the retina, only the visual field test can measure the subjective field of view of perception from the patient side. We introduce a Virtual Reality (VR)-based perimetry test. A systematic test for measuring the visual field function conducted in VR. The VR-based test is precise enough to supersede the standard perimetry test carried out on either a Goldmann or Octopus test machine. Using VR allows for a more comfortable experience and opens up for further development that can potentially be used to map out other deficiencies of the visual field function. It also provides the ability to gamify the test allowing it to be carried out on children. Here we present some of the considerations and implementation details which ensure that our software has the necessary capabilities of producing correct measurements and valid results.

Characterizing noise in eye movement data is important for data analysis, as well as for the comparison of research results across systems. We present a method that characterizes and reconstructs the noise in eye movement data from video-oculography (VOG) systems taking into account the uneven sampling in real recordings due to track loss and inherent system features. The proposed method extends the Lomb-Scargle periodogram, which is used for the estimation of the power spectral density (PSD) of unevenly sampled data [Hocke and Kämpfer 2009]. We estimate the PSD of fixational eye movement data and reconstruct the noise by applying a random phase to the inverse Fourier transform so that the reconstructed signal retains the amplitude of the original noise at each frequency. We apply this method to the EMRA/COGAIN Eye Data Quality Standardization project's dataset, which includes recordings from 11 commercially available VOG systems and a Dual Pukinje Image (DPI) eye tracker. The reconstructed noise from each VOG system was superimposed onto the DPI data and the resulting eye movement measures from the same original behaviors were compared.

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.