Electro-Oculography and Proprioceptive Calibration Enable Horizontal and Vertical Gaze Estimation, Even with Eyes Closed

TL;DR

This study shows that eye movement can be estimated using EOG even with closed eyes, opening new possibilities for assistive technology and medical research.

Contribution

The novel use of EOG and proprioceptive calibration enables reliable gaze estimation with closed eyes.

Findings

EOG tracking was statistically equivalent to camera-based eye tracking when eyes were open.

EOG signals with closed eyes followed instructed paths more accurately than chance-level performance.

Proprioceptive calibration enabled reliable gaze estimation even when eyes were not visible.

Abstract

Eye movement is an important tool used to investigate cognition. It also serves as input in human–computer interfaces for assistive technology. It can be measured with camera-based eye tracking and electro-oculography (EOG). EOG does not rely on eye visibility and can be measured even when the eyes are closed. We investigated the feasibility of detecting the gaze direction using EOG while having the eyes closed. A total of 15 participants performed a proprioceptive calibration task with open and closed eyes, while their eye movement was recorded with a camera-based eye tracker and with EOG. The calibration was guided by the participants’ hand motions following a pattern of felt dots on cardboard. Our cross-correlation analysis revealed reliable temporal synchronization between gaze-related signals and the instructed trajectory across all conditions. Statistical comparison tests and equivalence tests demonstrated that EOG tracking was statistically equivalent to the camera-based eye tracker gaze direction during the eyes-open condition. The camera-based eye-tracking glasses do not support tracking with closed eyes. Therefore, we evaluated the EOG-based gaze estimates during the eyes-closed trials by comparing them to the instructed trajectory. The results showed that EOG signals, guided by proprioceptive cues, followed the instructed path and achieved a significantly greater accuracy than shuffled control data, which represented a chance-level performance. This demonstrates the advantage of EOG when camera-based eye tracking is infeasible, and it paves the way for the development of eye-movement input interfaces for blind people, research on eye movement direction when the eyes are closed, and the early detection of diseases.

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