Real-time continuous EOG-based gaze angle estimation with baseline drift compensation under non-stationary head conditions

This work addresses an impractical and unnatural constraint that has been generally enforced in state-of-the-art electrooculography (EOG)-based gaze estimation methods, that of maintaining a stationary head pose and position. Specifically, this work proposes an EOG-based gaze angle (GA) estimation method that accommodates natural variations in the user’s head pose and position. The EOG data collected under non-stationary head conditions, which was used in this work to validate the proposed method, is also being made publicly available. This work generalises a two-eye verging gaze geometrical model to cater for arbitrary head poses and positions, and also models the dynamics of the vestibulo-ocular reflex (VOR), which refers to the eye-head coordination that normally takes place during gaze shifts under unrestrained head conditions. These methods are validated by incorporating them within a published multiple-model framework for GA estimation. When applied to short EOG data segments, a horizontal and vertical GA estimation error of 1.85 ± 0.51° and 2.19 ± 0.62°, respectively, and an eye movement detection and labelling F-score close to 90% were obtained. These results are comparable to those reported previously under stationary head conditions. This work demonstrates that accurate GA estimation and eye movement detection and labelling can be achieved using EOG signals, even when the user’s head is not stationary. This work eliminates the need for users to maintain a stationary head pose and position, a common constraint in the field, thus introducing an EOG-based GA estimation method that allows users to move their heads naturally. •Real-time-implementable algorithm for EOG-based gaze estimation.•Modelling of the Vestibulo Ocular Reflex.•Multiple-model framework including models for fixations, saccades, blinks and VORs.•Gaze estimation while compensating for baseline drift and head movement.•Sample-by-sample labelling of EOG signals.