EOG-Based Gaze Angle Estimation Using a Battery Model of the Eye

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

doi:10.1109/embc.2019.8856323

Cited by 8 publications

(8 citation statements)

References 7 publications

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“…Recording actual EOG data with electrodes which are perfectly-aligned with the ocular rotation axes is difficult to perform in practice since the eyes are positioned inside the skull. Therefore, the investigation reported in this work is based on simulations of the EOG potential using a dipole model of the eye (Shinomiya et al 2006, Barbara et al 2019b, which explicitly relates the EOG potential at any location to the orientation of the ocular dipole. Specifically, the potentials were simulated at the electrode positions shown in figure 1.…”

Section: Methodsmentioning

confidence: 99%

“…The battery model is a monolayer model which relates the EOG potential to the distances from the electrode to the cornea and to the retina of a single ocular globe. Barbara et al (2019b) adapted this single-eye model to cater for the influence of both ocular globes, specifically:…”

Section: Simulation Model and Assumptionsmentioning

confidence: 99%

“…Furthermore, ( ) denote the distances between the electrode and the left and right retina centre points, respectively. Denoting the eyeball radius by ρ, and the coordinate of the position of electrode E i by (x i , y i , z i ), these distances are given by Ishida et al (2015), Barbara et al (2019b):…”

Section: Simulation Model and Assumptionsmentioning

confidence: 99%

“…In this work, without loss of generality, the eyeball radius was set to ρ = 1.2 cm, the inter-pupillary distance was set to d PD = 6.9 cm, whereas D = 61.5 cm and m = ps 25 Vm I 4 (Barbara et al 2019b). The simulated electrode positions used in this model were set to be aligned with the ocular rotation axes as previously mentioned and as shown in figure 1.…”

Section: Simulation Model and Assumptionsmentioning

confidence: 99%

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Monopolar and bipolar electrooculography signal characteristics due to target displacements—have we seen the whole picture?

Barbara

Camilleri

2023

Physiol. Meas.

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The development of electrooculography (EOG)-based human-computer interface (HCI) systems is generally based on the processing of the commonly referred to horizontal and vertical bipolar EOG channels, which are computed from a horizontally-aligned and another vertically-aligned pair of electrodes, respectively. Horizontal (Vertical) target displacements are assumed to result in changes in the horizontal (vertical) EOG channel only, and any cross-talk between the bipolar channels is often neglected or incorrectly attributed solely to electrode misalignment with respect to the ocular rotation axes. Objective: The aim of this work is to demonstrate that such cross-talk is intrinsic to the geometric relationship between the orientation of the verging ocular globes and the planar displacement of the gaze target with respect to the primary gaze position (PGP). Approach: Since it is difficult to record actual EOG data with electrodes which are perfectly-aligned with the ocular rotation axes, this is studied by simulating the EOG potential values for various horizontally- and vertically-displacing targets using a dipole model of the eye. Main results: We show that cross-talk between the horizontal and vertical bipolar EOG channels is manifested even if the electrodes are aligned with the ocular rotation axes. Specifically, for a horizontally- (vertically-)displaced target, while the monopolar EOG signals obtained from the horizontally- (vertically-)aligned electrodes exhibit an expected predominant potential displacement, a smaller displacement is also exhibited in the monopolar EOG signals obtained from the vertically- (horizontally-)aligned electrodes. These unexpected displacements in the vertically- (horizontally-)aligned monopolar channels may have different magnitudes, resulting in an effective potential displacement in the vertical (horizontal) bipolar EOG channel. Significance: This is significant as it shows that, unlike in many works published so far for EOG-based ocular pose estimation, it is not sufficient to only use the horizontal (vertical) bipolar EOG channel to estimate the horizontal (vertical) displacement of the ocular pose.

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Section: Methodsmentioning

confidence: 99%

Section: Simulation Model and Assumptionsmentioning

confidence: 99%

Section: Simulation Model and Assumptionsmentioning

confidence: 99%

Section: Simulation Model and Assumptionsmentioning

confidence: 99%

See 2 more Smart Citations

Monopolar and bipolar electrooculography signal characteristics due to target displacements—have we seen the whole picture?

Barbara

Camilleri

2023

Physiol. Meas.

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“…The typical calibration method requires a laboratory setup where gaze direction is known by experimental construction or measured externally with a camera-based video eye tracker. The recorded EOG voltages, paired with known gaze directions, are used to fit a polynomial (Manabe et al, 2015) or battery model (Barbara et al, 2019). Once fit, the model can be used to estimate new gaze directions from just EOG.…”

Section: Introductionmentioning

confidence: 99%

Using natural head movements to continually calibrate EOG signals

Nezvadovitz

Rao

2022

JEMR

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Electrooculography (EOG) is the measurement of eye movements using surface electrodes adhered around the eye. EOG systems can be designed to have an unobtrusive form-factor that is ideal for eye tracking in free-living over long durations, but the relationship between voltage and gaze direction requires frequent re-calibration as the skin-electrode impedance and retinal adaptation vary over time. Here we propose a method for automatically calibrating the EOG-gaze relationship by fusing EOG signals with gyroscopic measurements of head movement whenever the vestibulo-ocular reflex (VOR) is active. The fusion is executed as recursive inference on a hidden Markov model that accounts for all rotational degrees-of-freedom and uncertainties simultaneously. This enables continual calibration using natural eye and head movements while minimizing the impact of sensor noise. No external devices like monitors or cameras are needed. On average, our method’s gaze estimates deviate by 3.54° from those of an industry-standard desktop video-based eye tracker. Such discrepancy is on par with the latest mobile video eye trackers. Future work is focused on automatically detecting moments of VOR in free-living.

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