Augmented Cognition using Real-time EEG-based Adaptive Strategies for Air Traffic Control

摘要

Future air traffic systems aim at increasing both the capacity and safety of the system, necessitating thedevelopment of new metrics and advisory tools for controllers’ workload in real-time. Psychophysiologicaldata such as Electroencephalography (EEG) are used to contrast and validate subjective assessmentsand workload indices. EEG used within augmented cognition systems form situation awareness advisorytools that are able to provide real-time feedback to air-traffic control supervisors and planners. This augmentedcognition system and experiments using the system with air traffic controllers are presented. Trafficindicators are used in conjunction with EEG-driven cognitive indicators to adapt the traffic in real-timethrough Computational Red Teaming (CRT) based adaptive control mechanisms. The metrics, measures,and adaptive control mechanisms are described and evaluated. The best mechanism to improve system efficacywas found when the system allowed for real-time adaptation of traffic based on engagement metricsdriven from the EEG data.