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

Michael Burch; Kuno Kurzhals; Niklas Kleinhans; Daniel Weiskopf

doi:10.1145/3204493.3204565

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

Michael Burch, Kuno Kurzhals, Niklas Kleinhans, Daniel Weiskopf

Visualization

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

18 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 language	English
Title of host publication	ETRA '18 Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications
Publisher	Association for Computing Machinery, Inc
ISBN (Electronic)	978-1-4503-5706-7
DOIs	https://doi.org/10.1145/3204493.3204565
Publication status	Published - 14 Jun 2018
Event	10th ACM Symposium on Eye Tracking Research and Applications (ETRA 2018) - Warsaw, Poland Duration: 14 Jun 2018 → 17 Jun 2018 Conference number: 10

Conference

Conference	10th ACM Symposium on Eye Tracking Research and Applications (ETRA 2018)
Abbreviated title	ETRA 2018
Country/Territory	Poland
City	Warsaw
Period	14/06/18 → 17/06/18

Keywords

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

Access to Document

10.1145/3204493.3204565

Cite this

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title = "EyeMSA: exploring eye movement data with pairwise and multiple sequence alignment",

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.",

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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, 10th ACM Symposium on Eye Tracking Research and Applications (ETRA 2018), Warsaw, Poland, 14/06/18. https://doi.org/10.1145/3204493.3204565

EyeMSA: exploring eye movement data with pairwise and multiple sequence alignment. / Burch, Michael; Kurzhals, Kuno; Kleinhans, Niklas et al.
ETRA '18 Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications . Association for Computing Machinery, Inc, 2018. 52.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Kurzhals, Kuno

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

Abstract

Conference

Keywords

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