Combining automation and expertise: A semi-automated approach to correcting eye-tracking data in reading tasks.

In reading tasks, drift can move fixations from one word to another or even another line, invalidating the eye-tracking recording. Manual correction is time-consuming and subjective, while automated correction is fast - yet limited in accuracy. In this paper, we present Fix8 (Fixate), an open-source GUI tool that offers a novel semi-automated correction approach for eye-tracking data in reading tasks.

Advancing Dynamic-Time Warp Techniques for Correcting Eye Tracking Data in Reading Source Code.

Automated eye tracking data correction algorithms such as Dynamic-Time Warp always made a trade-off between the ability to handle regressions (jumps back) and distortions (fixation drift). At the same time, eye movement in code reading is characterized by non-linearity and regressions. In this paper, we present a family of hybrid algorithms that aim to handle both regressions and distortions with high accuracy.