Real-Time EOG Signal Baseline Drift Estimation Using Passive VOG Data

One of the main challenges when it comes to electrooculography (EOG)-based eye gaze tracking for the control of human-computer interface systems is the drifting baseline. This slow wander in the signal leads to erroneous gaze angle estimates and over time, can make operating an application impossible. Baseline component estimation techniques have been proposed in the literature in order to model and remove the baseline drift component, however, most of these can only be carried out in an offline manner. In this work, we propose a novel drift mitigation technique which may be used to de-drift EOG signals in real-time without requiring users to fixate at known target locations. The proposed approach makes use of a low-sampling rate passive videooculography (VOG) source to model and remove the EOG signal baseline whilst preserving the signal's original morphology. It's performance, in terms of the horizontal and vertical gaze angle estimation error is evaluated against standard baseline estimation techniques using data from ten subjects, demonstrating improved performance.