Workload estimator using EEG and eye-tracking - Tashev, Ivan; Beauchene, Christine; Winters, R. Michael; Wang, Yu Te; Johnston, David; Bridges, Nathaniel; Estepp, Justin R. (EMBC 2024, 2024)
In this paper we present a workload estimator based on biological signals - electroencephalographic and eye-tracking. The workload estimator is person- and session- independent, designed to work in a virtual reality flight simulator environment and is a part of our adaptive training system. The novel component is using objective evaluation of the workload, based on the flight logs, as labels for training the regression neural network. As evaluation parameter is selected the correlation with the objective labels. The paper contains the results from using several feature sets and estimators, where the best estimator achieves correlation with objective labels of 0.84. Index Terms—workload estimation, electroencephalography, eye-tracking, adaptive training system