Estimation of Cognitive Load based on the Pupil Size Dilation

Cognitive load corresponds to the amount of working memory demanded while performing a certain task. Estimation of cognitive load is crucial to many domains and the usage of pupil size dilation to accomplish this is widely researched. However, existing approaches suffer severely as they are largely based on the raw pupil size. In this study, we aim to use the frequency domain analysis of the pupil size variations to get an insight of the load imposed. We propose a cognitive load metric based on the power and frequency relations at the mean frequency of the variation in pupil size. The stimulus used is a mental addition task which is designed in a manner to induce low and high cognitive loads on the participants. Results show good separation in the metric for the tasks inducing low and high mental workloads in contrast to the state of the art methods.