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Unsupervised Clustering of EOG as a Viable Substitute for Optical Eye Tracking

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Eye Tracking and Visualization (ETVIS 2015)

Part of the book series: Mathematics and Visualization ((MATHVISUAL))

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Abstract

Eye-movements are typically measured with video cameras and image recognition algorithms. Unfortunately, these systems are susceptible to changes in illumination during measurements. Electrooculography (EOG) is another approach for measuring eye-movements that does not suffer from the same weakness. Here, we introduce and compare two methods that allow us to extract the dwells of our participants from EOG signals under presentation conditions that are too difficult for optical eye tracking. The first method is unsupervised and utilizes density-based clustering. The second method combines the optical eye-tracker’s methods to determine fixations and saccades with unsupervised clustering. Our results show that EOG can serve as a sufficiently precise and robust substitute for optical eye tracking, especially in studies with changing lighting conditions. Moreover, EOG can be recorded alongside electroencephalography (EEG) without additional effort.

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Notes

  1. 1.

    It is worth noting that EOG’s spatial resolution is considered to be poor relative to the claims of many modern optical eye-trackers ( < 0. 5∘) [17].

  2. 2.

    If a point is a neighbor to cores of different clusters, it is assigned to one of these clusters at random.

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Acknowledgements

This research was supported by the Max Planck Society. The authors N.F., H.H.B. and L.L.C. thank the German Research Foundation (DFG) for financial support within project C03 of SFB/Transregio 161.

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Authors and Affiliations

  1. Max Planck Institute for Biological Cybernetics, Spemannstrasse 41, 72076, Tuebingen, Germany

    Nina Flad, Heinrich H. Buelthoff & Lewis L. Chuang

  2. IMPRS for Cognitive and Systems Neuroscience, Oesterbergstrasse 3, 72074, Tuebingen, Germany

    Nina Flad & Tatiana Fomina

  3. Max Planck Institute for Intelligent Systems, Spemannstrasse 38, 72076, Tuebingen, Germany

    Tatiana Fomina

Authors
  1. Nina Flad
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  2. Tatiana Fomina
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  3. Heinrich H. Buelthoff
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  4. Lewis L. Chuang
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Corresponding authors

Correspondence to Nina Flad or Lewis L. Chuang .

Editor information

Editors and Affiliations

  1. VISUS, University of Stuttgart, Stuttgart, Germany

    Michael Burch

  2. Max Planck Institute for Biological Cybernetics, Tübingen, Germany

    Lewis Chuang

  3. SIAT, Simon Fraser University, Surrey, British Columbia, Canada

    Brian Fisher

  4. VIS, University of Stuttgart, Stuttgart, Germany

    Albrecht Schmidt

  5. VISUS, University of Stuttgart, Stuttgart, Germany

    Daniel Weiskopf

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Flad, N., Fomina, T., Buelthoff, H.H., Chuang, L.L. (2017). Unsupervised Clustering of EOG as a Viable Substitute for Optical Eye Tracking. In: Burch, M., Chuang, L., Fisher, B., Schmidt, A., Weiskopf, D. (eds) Eye Tracking and Visualization. ETVIS 2015. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-47024-5_9

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