Identifying Similar Eye Movement Patterns with t-SNE

In this paper we describe an approach based on the t-distributed stochastic neighbor embedding (t-SNE) focusing on projecting high-dimensional eye movement data to two dimensions. The lower-dimensional data is then represented as scatterplots
reflecting the local structure of the high-dimensional eye movement data and hence, providing a strategy to identify similar eye
movement patterns. The scatterplots can be used as means to interact with and to further annotate and analyze the data for
additional properties focusing on space, time, or participants. Since t-SNE oftentimes produces groups of data points mapped to
and overplotted in small scatterplot regions, we additionally support the modification of data point groups by a force-directed
placement as a post processing in addition to t-SNE that can be run after the initial t-SNE algorithm is stopped. This spatial
modification can be applied to each identified data point group independently which is difficult to integrate into a standard t-SNE
approach. We illustrate the usefulness of our technique by applying it to formerly conducted eye tracking studies investigating
the readability of public transport maps and map annotations.