%0 Book Section %1 conf/etvis/FladFBC15 %A Flad, Nina %A Fomina, Tatiana %A Bülthoff, Heinrich H. %A Chuang, Lewis L. %B Eye Tracking and Visualization: Foundations, Techniques, and Applications %D 2015 %E Burch, Michael %E Chuang, Lewis L. %E Fisher, Brian D. %E Schmidt, Albrecht %E Weiskopf, Daniel %I Springer International Publishing %K from:leonkokkoliadis sfbtrr161 C03 2015 %P 151-167 %R 10.1007/978-3-319-47024-5_9 %T Unsupervised Clustering of EOG as a Viable Substitute for Optical Eye Tracking %U https://doi.org/10.1007/978-3-319-47024-5_9 %X 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. %@ 978-3-319-47023-8

@inbook{conf/etvis/FladFBC15, 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.}, added-at = {2020-03-11T15:39:00.000+0100}, author = {Flad, Nina and Fomina, Tatiana and Bülthoff, Heinrich H. and Chuang, Lewis L.}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/23a38184634dbb31e466d423a04b36e6c/sfbtrr161}, booktitle = {Eye Tracking and Visualization: Foundations, Techniques, and Applications}, doi = {10.1007/978-3-319-47024-5_9}, editor = {Burch, Michael and Chuang, Lewis L. and Fisher, Brian D. and Schmidt, Albrecht and Weiskopf, Daniel}, ee = {https://www.wikidata.org/entity/Q63981926}, interhash = {a9c74f0b3fd6ab1b4007b99b533fee25}, intrahash = {3a38184634dbb31e466d423a04b36e6c}, isbn = {978-3-319-47023-8}, keywords = {from:leonkokkoliadis sfbtrr161 C03 2015}, pages = {151-167}, publisher = {Springer International Publishing}, series = {Mathematics and Visualization}, timestamp = {2020-03-11T14:39:00.000+0100}, title = {Unsupervised Clustering of EOG as a Viable Substitute for Optical Eye Tracking}, url = {https://doi.org/10.1007/978-3-319-47024-5_9}, year = 2015 }