EyeMMV toolbox: An eye movement post-analysis tool based on a two-step spatial dispersion threshold for fixation identification

Vassilios Krassanakis, Vassiliki Filippakopoulou, Byron Nakos

Abstract


Eye movement recordings and their analysis constitute an effective way to examine visual perception. There is a special need for the design of computer software for the performance of data analysis. The present study describes the development of a new toolbox, called EyeMMV (Eye Movements Metrics & Visualizations), for post experimental eye movement analysis. The detection of fixation events is performed with the use of an introduced algorithm based on a two-step spatial dispersion threshold. Furthermore, EyeMMV is designed to support all well-known eye tracking metrics and visualization techniques. The results of fixation identification algorithm are compared with those of an algorithm of dispersion-type with a moving window, imported in another open source analysis tool. The comparison produces outputs that are strongly correlated. The EyeMMV software is developed using the scripting language of MATLAB and the source code is distributed through GitHub under the third version of GNU General Public License (link: https://github.com/krasvas/EyeMMV).

Keywords


eye movement analysis; fixation identification; eye movement metrics; eye tracking data visualization

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DOI: http://dx.doi.org/10.16910/jemr.7.1.1

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