EEG-based Secondary Task Detection in a Multiple Objective Operational Environment

Real world operational environments often require the integration of complex multiple-objective tasks that necessitate split attention and individual prioritization in human operators. This study examines the effect of secondary task presence on operator electroencephalogram (EEG) activity in two different multiple-objective remotely piloted aircraft (RPA) simulations. Eight participants completed simulated aerial reconnaissance tasks of varying difficulties, while continuously monitoring and responding to radio traffic requesting distance, speed, and elevation calculations that required expedient mathematical reasoning. In these realistic dynamic task scenarios, balanced random forest and binary logistic regression classifiers are used to measure the effectiveness of 35 physiological markers in detecting operator workload changes. Results suggest that within-subject random forest models perform reasonably well even when trained using alternative primary tasks. Additionally, novel evidence supporting the importance of delta band (1-3Hz) brain activity for task detection is reported.