EyeMSA: exploring eye movement data with pairwise and multiple sequence alignment

Michael Burch, Kuno Kurzhals, Niklas Kleinhans, Daniel Weiskopf

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

5 Citations (Scopus)

Abstract

Eye movement data can be regarded as a set of scan paths, each corresponding to one of the visual scanning strategies of a certain study participant. Finding common subsequences in those scan paths is a challenging task since they are typically not equally temporally long, do not consist of the same number of fixations, or do not lead along similar stimulus regions. In this paper we describe a technique based on pairwise and multiple sequence alignment to support a data analyst to see the most important patterns in the data. To reach this goal the scan paths are first transformed into a sequence of characters based on metrics as well as spatial and temporal aggregations. The result of the algorithmic data transformation is used as input for an interactive consensus matrix visualization. We illustrate the usefulness of the concepts by applying it to formerly recorded eye movement data investigating route finding tasks in public transport maps.

Original languageEnglish
Title of host publicationETRA '18 Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)978-1-4503-5706-7
DOIs
Publication statusPublished - 14 Jun 2018
Event10th ACM Symposium on Eye Tracking Research and Applications (ETRA 2018) - Warsaw, Poland
Duration: 14 Jun 201817 Jun 2018
Conference number: 10

Conference

Conference10th ACM Symposium on Eye Tracking Research and Applications (ETRA 2018)
Abbreviated titleETRA 2018
CountryPoland
CityWarsaw
Period14/06/1817/06/18

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Keywords

  • Consensus matrix visualization
  • Eye tracking
  • Multiple sequence alignment
  • Scan paths
  • Visual analytics

Cite this

Burch, M., Kurzhals, K., Kleinhans, N., & Weiskopf, D. (2018). EyeMSA: exploring eye movement data with pairwise and multiple sequence alignment. In ETRA '18 Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications [52] Association for Computing Machinery, Inc. https://doi.org/10.1145/3204493.3204565