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arXiv · 2509.07416

Eye Movement Feature-Guided Signal De-Drifting in Electrooculography Systems

Lianming Hu·Xiaotong Zhang·Kamal Youcef-Toumi
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Abstract

Electrooculography (EOG) is widely used for gaze tracking in Human-Robot Collaboration (HRC). However, baseline drift caused by low-frequency noise significantly impacts the accuracy of EOG signals, creating challenges for further sensor fusion. This paper presents an Eye Movement Feature-Guided De-drift (FGD) method for mitigating drift artifacts in EOG signals. The proposed approach leverages active eye-movement feature recognition to reconstruct the feature-extracted EOG baseline and adaptively correct signal drift while preserving the morphological integrity of the EOG waveform. The FGD is evaluated using both simulation data and real-world data, achieving a significant reduction in mean error. The average error is reduced to 0.896{\deg} in simulation, representing a 36.29% decrease, and to 1.033{\deg} in real-world data, corresponding to a 26.53% reduction. Despite additional and unpredictable noise in real-world data, the proposed method consistently outperforms conventional de-drifting techniques, demonstrating its effectiveness in practical applications such as enhancing human performance augmentation.

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Lianming Hu, Xiaotong Zhang, Kamal Youcef-Toumi. 2025-09-09. Eye Movement Feature-Guided Signal De-Drifting in Electrooculography Systems. https://arxiv.org/abs/2509.07416

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