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

  • Nina FladEmail author
  • Tatiana Fomina
  • Heinrich H. Buelthoff
  • Lewis L. ChuangEmail author
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)


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.


Gaussian Mixture Model Display Condition Scanning Behavior Fixation Cluster Benchmark Sequence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Max Planck Institute for Biological CyberneticsTuebingenGermany
  2. 2.IMPRS for Cognitive and Systems NeuroscienceTuebingenGermany
  3. 3.Max Planck Institute for Intelligent SystemsTuebingenGermany

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