What Can Entropy Metrics Tell Us About the Characteristics of Ocular Fixation Trajectories?




Melnyk, Kateryna
Friedman, Lee
Komogortsev, Oleg

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In this study, we provide a detailed analysis of entropy measures calculated for fixation eye-movement trajectories from the three different datasets. We employed six key metrics (Fuzzy, Increment, Sample, Gridded Distribution, Phase, and Spectral Entropies). We calculate these six metrics on three sets of fixations: (1) fixations from the GazeCom dataset, (2) fixations from what we refer to as the ``Lund'' dataset, and (3) fixations from our own research laboratory (``OK Lab'' dataset). For each entropy measure, for each dataset, we closely examined the 36 fixations with the highest entropy and the 36 fixations with the lowest entropy. From this, it was clear that the nature of the information from our entropy metrics depended on which dataset was evaluated. These entropy metrics found various types of misclassified fixations in the GazeCom dataset. Two entropy metrics also detected fixation with substantial linear drift. For the Lund dataset, the only finding was that low spectral entropy was associated with what we call ``bumpy'' fixations. These are fixations with low-frequency oscillations. For the OK Lab dataset, three entropies found fixations with high-frequency noise which probably represent ocular microtremor. In this dataset, one entropy found fixations with linear drift. The between-dataset results are discussed in terms of the number of fixations in each dataset, the different eye movement stimuli employed, and the method of eye-movement classification.


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fixation eye-movements, chaos theory, entropy metrics, features, Computer Science



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