Classification of visual attention by microsaccades using machine learning Online publication date: Tue, 30-Apr-2024
by Soichiro Yokoo; Nobuyuki Nishiuchi; Kimihiro Yamanaka
International Journal of Biometrics (IJBM), Vol. 16, No. 3/4, 2024
Abstract: This paper proposes machine learning methods for classifying visual attention. Eye-tracking data contains a range of useful information related to human visual behaviour. In particular, many recent studies have shown a relationship between visual attention and microsaccades, a type of fixational eye movement. In this study, eye movement and pupil diameter were measured under three controlled experimental conditions requiring different visual attention levels. Microsaccades were extracted from eye-tracking data that included rapid saccades. Various machine learning methods were then used on parameters related to the extracted microsaccades to classify the level of visual attention. By cross-validating data from one participant (test data) with that from other participants (training data), we showed that the support vector machine method had the highest correct discrimination rate (77.1%). These results suggest that it is possible to classify visual attention based on microsaccades.
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