Method to Detect Eye Position Noise from Video-Oculography when Detection of Pupil or Corneal Reflection Position Fails

TL;DR

This paper introduces a software method to automatically detect noise caused by detection failures in video-oculography eye-tracking data, improving the reliability of gaze position signals.

Contribution

The novel contribution is an automated detection technique for transient noise in eye position signals due to detection failures, applicable offline and potentially in real-time.

Findings

Effective detection of noise periods in eye-tracking signals

Applicable to horizontal and vertical gaze traces

Suitable for offline and real-time analysis

Abstract

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.