Improving pilot mental workload classification through feature exploitation and combination: a feasibility study

Predicting high pilot mental workload is important to the United States Air Force because lives and aircraft have been lost due to errors made during periods of ;ight associated with mental overload and task saturation. Current research e0orts use psychophysiological measures such as electroencephalography (EEG), cardiac, ocular, and respiration measures in an attempt to identify and predict mental workload levels. Existing classi$cation methods successfully classify pilot mental workload using ;ight data for a single pilot on a given day, but are unsuccessful across di0erent pilots and/or days. We demonstrate a small subset of combined and calibrated psychophysiological features collected from a single pilot on a given day that accurately classi$es mental workload for a separate pilot on a di0erent day. We achieve classi$cation accuracy (CA) improvements over previous classi$ers exceeding 80% while using signi$cantly fewer features and dramatically reducing the CA variance. Without the need for EEG data, our feature combination and calibration scheme also radically reduces the raw data collection requirements, making data collection immensely easier to manage and spectacularly reducing computational processing requirements. ? 2004 Published by Elsevier Ltd.