Tobii Pro Lab eye openness

Eye openness – less noise, more signal

Capturing eyelid movements can enrich psychology, neuroscience, and clinical research and improve gaze signal quality in eye tracking experiments.

Oculomotor events examined through eye tracking can be used to better understand human cognition, behavior, and physiological states. For instance, peak saccade velocity can be used to infer mental workload (Bachurina & Arsalidou, 2022) and pupil size dynamics can indicate arousal levels and changes in the intensity of attention (Strauch et al., 2022). Visual access to the external world, and thus to eye movement measures, is disrupted every 4 to 6 seconds by eyelid closure – a blink. The human brain has developed perceptual mechanisms which allow these mini blackouts to go unheeded. However, capturing this hardly noticeable eyelid movement can enrich psychology, neuroscience, and clinical research and improve gaze signal quality in eye tracking experiments.

Eye openness – a new signal to measure eyelid kinematics

Just as there are different types of eye movements, various indices of eyelid movements exist. Spontaneous eye blink is the most readily observable eyelid movement, but one can also measure partial blinks, eyelid saccades, or eyelid closing/opening times. At Tobii, we developed a new eye tracking signal for researchers – eye openness (EO) data signal, which represents a measure of the largest sphere that can fit between the upper and lower eyelids. The EO signal provides the basis for accurate eyelid movement detection, including different types of blinks.

What can eyelid movements reveal about cognitive and physiological processes?

The EO signal has been mainly used to detect drivers’ drowsiness - the major risk factor for driving accidents. Eyelid movements are one of the most stable and meaningful features for drowsiness detection. The characteristics of eyelid movements provide several metrics that can be used for drowsiness estimation (e.g., percentage of eye closure (PERCLOS), eyelid closing/opening duration and speed, blink rate).

Eyelid movements grant meaningful insights into human psychology and physiology. Spontaneous blink rate is robustly affected by mental workload, physiological state, and motivation (Biggs et al., 2015; Cori et al., 2019; Marquart et al., 2015). For instance, long blinks indicate fatigue and drowsiness, while short ones are related to sustained attention and increased motivation. Researchers in various fields measure blinks as a readout of perception of time (Terhune et al., 2016), creative thinking (Agnoli et al., 2022), and visual attention (Nakano et al., 2009).

Cognitive process

Eye and eyelid movements are neuroanatomically linked. Eyelid movement dysfunctions can serve as a biomarker for various clinical conditions and complement the insights derived from other eye movement indices. For example, increased blink rate during fixation and pursuit can indicate the progression of Alzheimer’s (Coubard, 2016). With the EO signal at hand, it would now be possible to assess various eyelid movement-related dysfunctions, such as dry eye disease, blepharospasm, and ptosis (Hamedani & Gold, 2017). In animal and human studies, the eye blink rate is also used as a proxy for striatal dopamine availability. The EO signal now could help identify clinical conditions characterized by aberrant dopamine levels, such as Parkinson’s or schizophrenia (Jongkees & Colzato, 2016).

Eyelid dynamics measurements for accurate artifact detection

Eyelid movement-related artifacts cause significant data alterations in the eye tracking data. There are various methods within the eye tracking community to detect eye blinks. However, some methods rely on missing data values or noise interpretation as blinks, which pose a risk for inaccurate blink detection and unwanted data included in the analysis. For instance, pupil diameter measurements can be contaminated (usually underestimated) if a blink is not correctly detected and removed. With the new EO signal, it is now possible to accurately estimate blinks and obtain a correct pupil diameter measurement.

Eyelid movements also perturb saccades. Saccades are accompanied by ballistic eyelid movements, so-called eyelid saccades, which occur in synchrony with the rotation of the eyeball. Blink perturbed saccades often show a diminished peak velocity and a two- or three-fold increase in their duration (Goossens & Van Opstal, 2000). In some medical conditions (e.g., Huntington’s) an accurate saccade detection is crucial as it can help indicate disease stage and progression (Miranda et al., 2016). An accurate eyelid movement detection by EO signal could help tackle inaccuracies in saccades indices.

Eyelid movements are a source of artifacts also in electroencephalographic (EEG) data. Every EEG recording is contaminated by artifacts emerging from blinks, eyelid saccades, and post-saccadic eyelid movements. Eyelid movement-related artifacts originate from the changes in the resistance between the positively charged cornea and forehead as the eyelid slides down over the cornea (Plöchl et al., 2012). Eyelid movement detection with the new EO signal could ease eyelid artifacts identification, especially in the scenarios where electrooculographic (EOG) measurements are unavailable.

Tobii Pro Spectrum

Key takeaways

With the EO signal, researchers can measure various eyelid movements and benefit from an improved gaze signal quality in their eye tracking study. Here are the key benefits of EO signal:

  • Driver’s drowsiness estimation (e.g., PERCLOS, blink rate, eyelid closing/opening duration, and speed)
  • Insights into human psychology, physiology, and cognitive processes
  • Assessment of eyelid movement-related dysfunctions
  • Accurate blink artifact detection for improved pupil diameter and saccades measurements
  • Easier eyelid artefact detection in EEG data, especially where EOG measurements are not available

Tobii aims to invest in the improvement of EO signal. Sign up below if you would like to be the first to receive a technical report on the EO signal once it is ready.

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Eye openness signal is currently available in Tobii Pro Spectrum eye tracker with Firmware 2.6.1. You can access the signal on all Tobii Pro SDK (version 1.10) bindings and for all supported OS versions.
For the mentioned Spectrum hardware, running the latest FW, Tobii Pro Lab (version 1.194), can record the data, visualize it during replay, and export the raw data as well as several metrics based on eye openness.

Bibliography

Agnoli, S., Mastria, S., Zanon, M., & Corazza, G. E. (2022). Dopamine supports idea originality: The role of spontaneous eye blink rate on divergent thinking. Psychological Research.

Bachurina, V., & Arsalidou, M. (2022). Multiple levels of mental attentional demand modulate peak saccade velocity and blink rate. Heliyon, 8(1).

Biggs, A. T., Adamo, S. H., & Mitroff, S. R. (2015). Mo’ Money, Mo’ Problems: Monetary Motivation Can Exacerbate the Attentional Blink. Perception, 44(4), 410–422.

Cori, J. M., Anderson, C., Shekari Soleimanloo, S., Jackson, M. L., & Howard, M. E. (2019). Narrative review: Do spontaneous eye blink parameters provide a useful assessment of state drowsiness? Sleep Medicine Reviews, 45, 95–104.

Coubard, O. A. (2016). What do we know about eye movements in Alzheimer’s disease? The past 37 years and future directions. Biomarkers in Medicine, 10(7), 677–680.

Goossens, H. h. l. m., & Van Opstal, A. J. (2000). Blink-Perturbed Saccades in Monkey. I. Behavioral Analysis. Journal of Neurophysiology, 83(6), 3411–3429.

Hamedani, A. G., & Gold, D. R. (2017). Eyelid Dysfunction in Neurodegenerative, Neurogenetic, and Neurometabolic Disease. Frontiers in Neurology, 8.

Jongkees, B. J., & Colzato, L. S. (2016). Spontaneous eye blink rate as predictor of dopamine-related cognitive function—A review. Neuroscience & Biobehavioral Reviews, 71, 58–82.

Marquart, G., Cabrall, C., & de Winter, J. (2015). Review of Eye-related Measures of Drivers’ Mental Workload. Procedia Manufacturing, 3, 2854–2861.

Miranda, Â., Lavrador, R., Júlio, F., Januário, C., Castelo-Branco, M., & Caetano, G. (2016). Classification of Huntington’s disease stage with support vector machines: A study on oculomotor performance. Behavior Research Methods, 48(4), 1667–1677.

Nakano, T., Yamamoto, Y., Kitajo, K., Takahashi, T., & Kitazawa, S. (2009). Synchronization of spontaneous eyeblinks while viewing video stories. Proceedings of the Royal Society B: Biological Sciences, 276(1673), 3635–3644.

Plöchl, M., Ossandón, J., & König, P. (2012). Combining EEG and eye tracking: Identification, characterization, and correction of eye movement artifacts in electroencephalographic data. Frontiers in Human Neuroscience, 6.

Strauch, C., Wang, C.-A., Einhäuser, W., Stigchel, S. V. der, & Naber, M. (2022). Pupillometry as an integrated readout of distinct attentional networks. Trends in Neurosciences, 0(0).

Terhune, D. B., Sullivan, J. G., & Simola, J. M. (2016). Time dilates after spontaneous blinking. Current Biology, 26(11), R459–R460.