NMF-Based Analysis of Mobile Eye-Tracking Data
This is an incremental method for researchers analyzing eye-tracking data, providing an unsupervised approach to detect areas of interest.
The paper tackled the problem of analyzing mobile eye-tracking data by using nonnegative matrix factorization (NMF) to identify areas of interest in stimuli in an unsupervised way, and demonstrated its usefulness with data from an art gallery.
The depiction of scanpaths from mobile eye-tracking recordings by thumbnails from the stimulus allows the application of visual computing to detect areas of interest in an unsupervised way. We suggest using nonnegative matrix factorization (NMF) to identify such areas in stimuli. For a user-defined integer k, NMF produces an explainable decomposition into k components, each consisting of a spatial representation associated with a temporal indicator. In the context of multiple eye-tracking recordings, this leads to k spatial representations, where the temporal indicator highlights the appearance within recordings. The choice of k provides an opportunity to control the refinement of the decomposition, i.e., the number of areas to detect. We combine our NMF-based approach with visualization techniques to enable an exploratory analysis of multiple recordings. Finally, we demonstrate the usefulness of our approach with mobile eye-tracking data of an art gallery.