Noise estimation for head-mounted 3D binocular eye tracking using Pupil Core eye-tracking goggles.

Head-mounted, video-based eye tracking is becoming increasingly common and has promise in a range of applications. Here, we provide a practical and systematic assessment of the sources of measurement uncertainty for one such device - the Pupil Core - in three eye-tracking domains: (1) the 2D scene camera image; (2) the physical rotation of the eye relative to the scene camera 3D space; and (3) the external projection of the estimated gaze point location onto the target plane or in relation to world coordinates. We also assess eye camera motion during active tasks relative to the eye and the scene camera, an important consideration as the rigid arrangement of eye and scene camera is essential for proper alignment of the detected gaze. We find that eye camera motion, improper gaze point depth estimation, and erroneous eye models can all lead to added noise that must be considered in the experimental design. Further, while calibration accuracy and precision estimates can help assess data quality in the scene camera image, they may not be reflective of errors and variability in gaze point estimation. These findings support the importance of eye model constancy for comparisons across experimental conditions and suggest additional assessments of data reliability may be warranted for experiments that require the gaze point or measure eye movements relative to the external world.