Accurate and efficient real-time cognitive workload assessment has many important applications, and physiological monitoring has proven quite helpful with this assessment. One such physiological signal, the electrooculogram (EOG), can provide blink rate and blink duration measures. In a recent study, we developed and validated a robust blink detection algorithm based on the vertical EOG (VEOG). This algorithm does not require baseline data and is adaptive in the sense that it works for a wide variety of individuals without any experimenter adjustments. The performance of the algorithm is quantified using truth data based on video recordings. The algorithm produced blink rate and blink duration data for participants in a simulated remotely piloted aircraft experiment. Although this paper focuses on the blink detection algorithm, some results from the study will be included. Specifically, it was found that participants blinked fewer times and with a shorter duration in the more difficult experimental conditions.
Epling, S. L.,
& Galster, S.
(2015). The Electrooculogram and a New Blink Detection Algorithm. .