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

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.