@inproceedings{a4ce3d41c01d468e892634658656d59f,
title = "Cognitive workload classification using eye-tracking and EEG data",
abstract = "It has been shown that an increased mental workload in pilots could lead to a decrease in their situation awareness, which could lead, in turn, to a worse piloting performance and ultimately to critical human errors. Assessing the current pilot's psycho-physiological state is a hot topic of interest for developing advanced embedded cockpits systems capable of adapting their behavior to the state and performance of the pilot. In this work, we investigate a method to classify different levels of cognitive workload starting from synchronized EEG and eye-tracking information. The classifier object of the research is targeted to score a performance high enough to be applicable as a gauge for performance of unobtrusive monitoring systems working with data of lower quality.",
keywords = "Adaptive systems, Cognitive workload, Electro-encephalography (EEG), Empathic systems, Eye-tracking, Multiclass classification",
author = "Lobo, {Jesus L.} and Ser, {Javier Del} and {De Simone}, Flavia and Roberta Presta and Simona Collina and Zdenek Moravek",
note = "Publisher Copyright: {\textcopyright} 2016 ACM.; International Conference on Human-Computer Interaction in Aerospace, HCI-Aero 2016 ; Conference date: 14-09-2016 Through 16-09-2016",
year = "2016",
month = sep,
day = "14",
doi = "10.1145/2950112.2964585",
language = "English",
series = "Proceedings of the International Conference on Human-Computer Interaction in Aerospace, HCI-Aero 2016",
publisher = "Association for Computing Machinery, Inc",
booktitle = "Proceedings of the International Conference on Human-Computer Interaction in Aerospace, HCI-Aero 2016",
}