Topological Data Analysis Of Eye Movements

Oleg Kachan, Arsenii Onuchin

Abstract: The increasing use of eye tracking in modern cognitive and clinical psychology, neuroscience, and ophthalmology requires new methods of objective quantitative analysis of complex eye movement data. In the current work, topological data analysis (TDA) is used to extract a new type of features of eye movements to differentiate between two eye movements groups, obtained upon the presentation of two different stimuli images - a human face, shown straight and rotated for 180 degrees, which corresponds to the processing of the normal and unusual visual information respectively. Experimental evidence shows that the proposed topology-based features have more discriminative power over the generally accepted features of eye movements, allowing to separate provided different stimuli with good accuracy. Moreover, the concatenation of the topology-based and ROI fixation ratios features further improves the performance of the classification task, showing the complementariness of the proposed topological features to the existing ones. We believe that the new class of features is able to serve as a valuable addition to the eye movement data-based medical diagnosis of mental, neurological, and ophthalmological disorders and diseases.