A comparison of EOG baseline drift mitigation techniques

• Published in: Biomedical signal processing and control Vol. 57; p. 101738
• Main Authors: Barbara, Nathaniel, Camilleri, Tracey A., Camilleri, Kenneth P.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2020
• Subjects: Baseline drift; Baseline drift mitigation; Electrooculography; EOG; Eye gaze tracking; Electrooculography; Eye gaze tracking; EOG; Baseline drift mitigation; Baseline drift
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2019.101738

•The first literature review of different EOG baseline drift mitigation techniques.•Qualitative and quantitative comparison of techniques on real-recorded EOG data.•An open-access EOG signal database being made publicly available. Electrooculography (EOG) is an eye movement recording technique based on the electrical activity due to the eyes, which may be used to develop human computer interfaces. The EOG signal baseline is subject to drifting and, although several baseline drift mitigation techniques have been proposed in the literature, the specific technique and the corresponding parameters are generally arbitrarily chosen. Furthermore, the literature does not establish which is the most suitable technique. Hence, this work aims to review these different techniques, and qualitatively and quantitatively compare their performance in mitigating the baseline drift using the same EOG data. This dataset is also being made publicly available to serve as a benchmark for future work. The state-of-the-art baseline drift mitigation techniques, namely, frequent DC reference resetting, signal differencing, high-pass filtering, wavelet decomposition and polynomial fitting, were implemented and statistically compared. Generally, frequent resetting and signal differencing were statistically significantly better than the other techniques. Furthermore, high-pass filtering and wavelet decomposition had statistically similar performance, while the polynomial fitting technique was never superior to the other techniques. While frequent resetting and signal differencing gave the best performance, the former disrupts the user's interaction with the system whereas the latter undesirably changes the EOG signal morphology. From the remaining techniques, high-pass filtering and wavelet decomposition would be the most suitable, but only the former would be applicable to real-time applications. This work compares five state-of-the-art EOG baseline drift mitigation techniques and provides a guideline for future work.