Unsupervised Clustering of EOG as a Viable Substitute for Optical Eye Tracking
| Autor: | Heinrich H. Buelthoff, Lewis L. Chuang, Nina Flad, Tatiana Fomina |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
medicine.diagnostic_test
Computer science business.industry 05 social sciences Pattern recognition Electrooculography Electroencephalography Mixture model 050105 experimental psychology medicine Eye tracking 0501 psychology and cognitive sciences Artificial intelligence Cluster analysis business Unsupervised clustering |
| Zdroj: | Eye Tracking and Visualization ISBN: 9783319470238 ETVIS Eye Tracking and Visualization: Foundations, Techniques, and Applications: ETVIS 2015 Mathematics and Visualization |
| DOI: | 10.1007/978-3-319-47024-5_9 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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