Cognitive Load Estimation in VR Flight Simulator

Abstract

This paper discusses the design and development of a low-cost virtual reality (VR) based flight simulator with cognitive load estimation feature using ocular and EEG signals. Focus is on exploring methods to evaluate pilot’s interactions with aircraft by means of quantifying pilot’s perceived cognitive load under different task scenarios. Realistic target tracking and context of the battlefield is designed in VR. Head mounted eye gaze tracker and EEG headset are used for acquiring pupil diameter, gaze fixation, gaze direction and EEG theta, alpha, and beta band power data in real time. We developed an AI agent model in VR and created scenarios of interactions with the piloted aircraft. To estimate the pilot’s cognitive load, we used low-frequency pupil diameter variations, fixation rate, gaze distribution pattern, EEG signal-based task load index and EEG task engagement index. We compared the physiological measures of workload with the standard user’s inceptor control-based workload metrics. Results of the piloted simulation study indicate that the metrics discussed in the paper have strong association with pilot’s perceived task difficulty.