Abstract
The present study evaluates the quality of gaze data produced by a low-cost eye tracker (The Eye Tribe©, The Eye Tribe, Copenhagen, Denmark) in order to verify its suitability for the performance of scientific research. An integrated methodological framework, based on artificial eye measurements and human eye tracking data, is proposed towards the implementation of the experimental process. The obtained results are used to remove the modeled noise through manual filtering and when detecting samples (fixations). The outcomes aim to serve as a robust reference for the verification of the validity of low-cost solutions, as well as a guide for the selection of appropriate fixation parameters towards the analysis of experimental data based on the used low-cost device. The results show higher deviation values for the real test persons in comparison to the artificial eyes, but these are still acceptable to be used in a scientific setting.
Highlights
Eye movement analysis constitutes one of the most popular ways to examine visual perception and cognition
It is worth mentioning that the last decades several toolboxes, libraries, and/or standalone, such as ILAB [14], OGAMA [15], GazeAlyze [16], EyeMMV [12], ETRAN [17], and ASTEF [18,19], have been proposed and distributed as open-source projects
Except from that the present study reports critical values of spatial noise produced in several set-ups, it indicates the way to effectively use these values as an input in EyeMMV’s fixation identification algorithm
Summary
Eye movement analysis constitutes one of the most popular ways to examine visual perception and cognition. Eye tracking techniques allow capturing the visual reaction in an objective way. Considering the complexity of the spatiotemporal distribution of eye tracking data, the need for robust analysis techniques to efficiently process the recorded signals is well-known from the early eye movement surveys [8,9]. The analysis of eye tracking protocols is connected with the implementation of fixation identification algorithms, which constitute the basis for the development of other analysis software tools. Different fixation identification algorithms have been proposed by the eye tracking community (e.g., [10,11,12,13]), while at the same time several eye movement analysis tools have been developed. It is worth mentioning that the last decades several toolboxes, libraries, and/or standalone, such as ILAB [14], OGAMA [15], GazeAlyze [16], EyeMMV [12], ETRAN [17], and ASTEF [18,19], have been proposed and distributed as open-source projects
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