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Volume 10
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Journal of Eye Movement Research is published by MDPI from Volume 18 Issue 1 (2025). Previous articles were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence, and they are hosted by MDPI on mdpi.com as a courtesy and upon agreement with Bern Open Publishing (BOP).
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Open AccessArticle
by Oliver Hein Oliver Hein Wolfgang H. Zangemeister Wolfgang H. Zangemeister
Neurological University Clinic Hamburg UKE, Hamburg, Germany
J. Eye Mov. Res. 2017, 10(1), 1-25; https://doi.org/10.16910/jemr.10.1.1
Submission received: 24 July 2016 / Published: 13 March 2017
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Abstract
Recent years have witnessed a remarkable growth in the way mathematics, informatics, and computer science can process data. In disciplines such as machine learning, pattern recognition, computer vision, computational neurology, molecular biology, information retrieval, etc., many new methods have been developed to cope with the ever increasing amount and complexity of the data. These new methods offer interesting possibilities for processing, classifying and interpreting eye-tracking data. The present paper exemplifies the application of topological arguments to improve the evaluation of eye-tracking data. The task of classifying raw eye-tracking data into saccades and fixations, with a single, simple as well as intuitive argument, described as coherence of spacetime, is discussed, and the hierarchical ordering of the fixations into dwells is shown. The method, namely identification by topological characteristics (ITop), is parameter-free and needs no pre-processing and post-processing of the raw data. The general and robust topological argument is easy to expand into complex settings of higher visual tasks, making it possible to identify visual strategies.
Keywords: gaze trajectory; event detection; topological data analysis (TDA); clustering; parameter-free classification; visual strategy; global scanpath; local scanpath
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MDPI and ACS Style
Hein, O.; Zangemeister, W.H. Topology for Gaze Analyses—Raw Data Segmentation. J. Eye Mov. Res. 2017, 10, 1-25. https://doi.org/10.16910/jemr.10.1.1
AMA Style
Hein O, Zangemeister WH. Topology for Gaze Analyses—Raw Data Segmentation. Journal of Eye Movement Research. 2017; 10(1):1-25. https://doi.org/10.16910/jemr.10.1.1
Chicago/Turabian Style
Hein, Oliver, and Wolfgang H. Zangemeister. 2017. "Topology for Gaze Analyses—Raw Data Segmentation" Journal of Eye Movement Research 10, no. 1: 1-25. https://doi.org/10.16910/jemr.10.1.1
APA Style
Hein, O., & Zangemeister, W. H. (2017). Topology for Gaze Analyses—Raw Data Segmentation. Journal of Eye Movement Research, 10(1), 1-25. https://doi.org/10.16910/jemr.10.1.1
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MDPI and ACS Style
Hein, O.; Zangemeister, W.H. Topology for Gaze Analyses—Raw Data Segmentation. J. Eye Mov. Res. 2017, 10, 1-25. https://doi.org/10.16910/jemr.10.1.1
AMA Style
Hein O, Zangemeister WH. Topology for Gaze Analyses—Raw Data Segmentation. Journal of Eye Movement Research. 2017; 10(1):1-25. https://doi.org/10.16910/jemr.10.1.1
Chicago/Turabian Style
Hein, Oliver, and Wolfgang H. Zangemeister. 2017. "Topology for Gaze Analyses—Raw Data Segmentation" Journal of Eye Movement Research 10, no. 1: 1-25. https://doi.org/10.16910/jemr.10.1.1
APA Style
Hein, O., & Zangemeister, W. H. (2017). Topology for Gaze Analyses—Raw Data Segmentation. Journal of Eye Movement Research, 10(1), 1-25. https://doi.org/10.16910/jemr.10.1.1
J. Eye Mov. Res., EISSN 1995-8692, Published by MDPI
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