Eye-Tracking Technology and its Application in Neuroscience
Vitaliy D. Pavlenko, Tetiana V. Shamanina, Vladislav Chori · 2023
A novel approach has been developed that utilizes information models of the oculo-motor system (OMS) for diagnosing neuropsychological states of an individual. This approach is based on employing Volterra polynomial models of the “input-output” type along with eye-tracking technology. It aims to enhance the precision of OMS modeling and provides a more robust diagnosis within the proposed heuristic feature space. Integral and differential transformations of multidimensional transition characteristics of the OMS are employed in this methodology, greatly streamlining the determination of features and the practical implementation of a Bayesian classifier. The derived heuristic features contribute to heightened diagnostic accuracy by analyzing eye movements and reflecting the neuropsychological states of the individual. This research holds significant potential applications in fields such as psychology, neurology, and human-computer interaction, offering a non-invasive and potentially more accurate avenue for comprehending an individual's internal states.