Nonlinear Analysis of Eye-Tracking Information for Motor Imagery Assessments

• DOI: 10.3389/fnins.2019.01431
• Publication Date: 2020-01-14T23:54:08Z
• Authors: Antonio Lanata; Laura Sebastiani; Francesco Di Grut...; Stefano Di Modica; Enzo Pasquale Sci...; Alberto Greco
• Keywords: Motor Imagery

This study investigates the assessment of motor imagery (MI) ability in humans. Commonly, MI is measured through two methodologies: a self-administered questionnaire (MIQ-3) and mental chronometry (MC), which measures temporal discrepancy between actual imagined tasks. However, both rely on subjects' self-assessment do not use physiological measures. In this study, we propose novel set features extracted from nonlinear dynamics eye gaze signal to discriminate good bad imagers. To aim, designed an experiment where twenty volunteers, categorized as or imagers according MC, performed three tasks: task (MT), visual Imagery (VI), kinaesthetic (KI). Throughout experiment, was continuously monitored using eye-tracking system. Eye time series were analyzed recurrence quantification analysis reconstructed phase space compared groups. Statistical results have shown how behavior can express inner process may be used more objective physiological-based measure ability.