Automatic classification of eye activity for cognitive load measurement with emotion interference

Abstract

Measuring cognitive load changes can contribute to better treatment of patients, can help design effective strategies to reduce medical errors among clinicians and can facilitate user evaluation of health care information systems. This paper proposes an eye-based automatic cognitive load measurement (CLM) system toward realizing these prospects. Three types of eye activity are investigated: pupillary response, blink and eye movement (fixation and saccade). Eye activity features are investigated in the presence of emotion interference, which is a source of undesirable variability, to determine the susceptibility of CLM systems to other factors. Results from an experiment combining arithmetic-based tasks and affective image stimuli demonstrate that arousal effects are dominated by cognitive load during task execution. To minimize the arousal effect on CLM, the choice of segments for eye-based features is examined. We then propose a feature set and classify three levels of cognitive load. The performance of cognitive load level prediction was found to be close to that of a reaction time measure, showing the feasibility of eye activity features for near-real time CLM.