Keeping pilots in the loop : an explainable spatiotemporal EEG-driven deep learning framework for adaptive automation in cruising flight phase

Abstract

Automation has been extensively used in flight operations, so pilots are less involved in actual flight control. With the long idle time during cruising, pilots may have their vigilance level reduced and eventually become out-of-the-loop. This research proposes a two-stage explainable adaptive automation approach to keep pilots in the loop based on Convolutional Neural Networks, Long Short-Term Memory, and EEG data collected from 24 participants in a one-hour simulator-based flight task in each level of automation. Our proposed spatiotemporal model yields test accuracy of 0.9918 and 0.9907 in the first and second stages, respectively, outperforming other benchmarking models by 30.79% and 10.73%, respectively. Furthermore, the Shapley additive explanations are adopted to strengthen the model interpretability and trustworthiness for safety-critical applications. Our model successfully identified that high delta and theta waves with low beta and gamma waves contribute positively to the out-of-the-loop state. It indicates that the classification aligns with the theoretical background and is trustworthy. The trustworthy adaptive deep learning model supports the dynamical automation configuration for improving human-automation collaboration in cruising flights.