Gaze Analytics Pipeline for Unity 3D Integration - Signal Filtering and Analysis

Analysis of human spatial cognition when navigating within physical or virtual environments can be bolstered by collecting eye movements with the help of an eye tracker. In physical reality, a head-mounted tracker can be worn during navigation with recorded gaze data often mapped to the screen coordinates of a forward-facing camera. In virtual reality, gaze data can be recorded with a so-called remote, or table-mounted eye tracker placed in front of the display screen on which the virtual environment is presented. In both cases gaze data will eventually need to be processed in order to infer insights about human visual attention to elements in the environment. Application of inferential statistics to collected gaze data often relies on characterization of the raw data into fixations, usually derived from some form of filtering, e.g., dispersion-based or velocity-based. Unfortunately, most commercial software packages provide only a limited choice of fixation detection algorithms (i.e., filters), often hiding implementation details or filter parameters from the user. Some systems (e.g., Ogama) still rely on the dispersion-based “fixation pickers” [7] which have been shown to be less than reliable, particularly when evaluating data captured on di↵erent platforms, at di↵erent sampling rates [10]. Velocity-based filters, or “saccade pickers”, while perhaps more di cult to tune, o↵er a more reliable alternative. Beyond the lack of su cient control over filtering parameters, commercial packages often do not include flexible means for statistical analysis. This is hardly surprising, however, since eye tracking vendors can hardly be expected to anticipate all possible experimental designs for which their devices are used. In this paper we describe a gaze analytics pipeline through which raw gaze data is processed. The pipeline consists of the following steps: