Eye Movement Feature-Guided Signal De-Drifting in Electrooculography Systems
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911144278491136 |
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| author | Hu, Lianming Zhang, Xiaotong Youcef-Toumi, Kamal |
| author_facet | Hu, Lianming Zhang, Xiaotong Youcef-Toumi, Kamal |
| contents | 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° in simulation, representing a 36.29% decrease, and to 1.033° 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_07416 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Eye Movement Feature-Guided Signal De-Drifting in Electrooculography Systems Hu, Lianming Zhang, Xiaotong Youcef-Toumi, Kamal Signal Processing 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° in simulation, representing a 36.29% decrease, and to 1.033° 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. |
| title | Eye Movement Feature-Guided Signal De-Drifting in Electrooculography Systems |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2509.07416 |