TEMoD: Target-Enabled Model-Based De-Drifting of the EOG Signal Baseline using a Battery Model of the Eye

Barbara, Nathaniel; Camilleri, Tracey A.; Camilleri, Kenneth P.

doi:10.1109/embc46164.2021.9629973

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…It can be caused by electrode movement, variations in skin impedance, or environmental factors. Baseline drift can obscure the underlying EOG signal and hinder the accurate detection of eye movement-related information [14]. Power line Interference: Power line interference, also known as mains hum, arises from the coupling of the EOG electrodes with the electrical power supply network.…”

Section: Various Types Of Noise In Eog Signalmentioning

confidence: 99%

A review for filtering techniques of the Electrooculography (EOG) signals

Aws M Abdullah,

Ali R Ibrahim,

Ammar A Al-Hamadani

et al. 2023

Global J. Eng. Technol. Adv.

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Electrooculography (EOG) is a non-invasive method employed for the measurement of the electrical potential produced by ocular movements. EOG signals frequently encounter contamination from diverse forms of interference, thereby impeding precise analysis and interpretation. In order to address these obstacles, numerous filtering methodologies have been devised to ameliorate the quality of EOG signals. The objective of this paper is to examine the filtering techniques commonly employed for EOG signals, elucidating their respective benefits and limitations.

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Section: Various Types Of Noise In Eog Signalmentioning

confidence: 99%

A review for filtering techniques of the Electrooculography (EOG) signals

Aws M Abdullah,

Ali R Ibrahim,

Ammar A Al-Hamadani

et al. 2023

Global J. Eng. Technol. Adv.

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“…In VOG systems, KF fusion-based methodologies have been employed to denoise ocular motion signals derived from pupil reflections [ 33 ]. Nevertheless, these model-based denoising approaches [ 34 , 35 ], which utilize acceleration-based fusion algorithms [ 25 ], exhibit a notable disparity in accuracy when compared to ground truth measurements.…”

Section: Introductionmentioning

confidence: 99%

A Fusion Algorithm Based on a Constant Velocity Model for Improving the Measurement of Saccade Parameters with Electrooculography

Gunawardane,

MacNeil,

Zhao

et al. 2024

Sensors

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Electrooculography (EOG) serves as a widely employed technique for tracking saccadic eye movements in a diverse array of applications. These encompass the identification of various medical conditions and the development of interfaces facilitating human–computer interaction. Nonetheless, EOG signals are often met with skepticism due to the presence of multiple sources of noise interference. These sources include electroencephalography, electromyography linked to facial and extraocular muscle activity, electrical noise, signal artifacts, skin-electrode drifts, impedance fluctuations over time, and a host of associated challenges. Traditional methods of addressing these issues, such as bandpass filtering, have been frequently utilized to overcome these challenges but have the associated drawback of altering the inherent characteristics of EOG signals, encompassing their shape, magnitude, peak velocity, and duration, all of which are pivotal parameters in research studies. In prior work, several model-based adaptive denoising strategies have been introduced, incorporating mechanical and electrical model-based state estimators. However, these approaches are really complex and rely on brain and neural control models that have difficulty processing EOG signals in real time. In this present investigation, we introduce a real-time denoising method grounded in a constant velocity model, adopting a physics-based model-oriented approach. This approach is underpinned by the assumption that there exists a consistent rate of change in the cornea-retinal potential during saccadic movements. Empirical findings reveal that this approach remarkably preserves EOG saccade signals, resulting in a substantial enhancement of up to 29% in signal preservation during the denoising process when compared to alternative techniques, such as bandpass filters, constant acceleration models, and model-based fusion methods.

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TEMoD: Target-Enabled Model-Based De-Drifting of the EOG Signal Baseline using a Battery Model of the Eye

Cited by 2 publications

References 8 publications

A review for filtering techniques of the Electrooculography (EOG) signals

A review for filtering techniques of the Electrooculography (EOG) signals

A Fusion Algorithm Based on a Constant Velocity Model for Improving the Measurement of Saccade Parameters with Electrooculography

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