Method to Detect Eye Position Noise from Video-Oculography when Detection of Pupil or Corneal Reflection Position Fails
This work addresses noise detection in eye-tracking systems, which is crucial for researchers and practitioners in fields like psychology and human-computer interaction, but it is incremental as it builds on existing signal analysis techniques.
The authors tackled the problem of detecting noise in eye position signals from video-based eye trackers when pupil or corneal reflection detection fails, resulting in rapid irregular oscillations called RIONEPS; they developed a software method based on signal inefficiency estimation to automatically identify these periods, with potential for real-time adaptation.
We present software to detect noise in eye position signals from video-based eye-tracking systems that depend on accurate pupil and corneal reflection position estimation. When such systems transiently fail to properly detect the pupil or the corneal reflection due to occlusion from eyelids, eye lashes or various shadows, the estimated gaze position is false. This produces an artifactual signal in the position trace that is rapidly, irregularly oscillating between true and false gaze positions. We refer to this noise as RIONEPS (Rapid Irregularly Oscillating Noise of the Eye Position Signal). Our method for detecting these periods automatically is based on an estimate of the relative inefficiency of the eye position signal. We look for RIONEPS in the horizontal and vertical traces separately, and although we typically use it offline, it is suitable to adaptation for real time use. This method requires a threshold to be set, and although we provide some guidance, thresholds will have to be estimated empirically.