TY  - JOUR
AU  - Aura Hernandez-Sabate
AU  - Jose Elias Yauri
AU  - Pau Folch
AU  - Miquel Angel Piera
AU  - Debora Gil
PY  - 2022//
TI  - Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals
T2  - APPLSCI
JO  - Applied Sciences
SP  - 2298
VL  - 12
IS  - 5
KW  - Cognitive states
KW  - Mental workload
KW  - EEG analysis
KW  - Neural networks
KW  - Multimodal data fusion
N2  - The commercial flightdeck is a naturally multi-tasking work environment, one in which interruptions are frequent come in various forms, contributing in many cases to aviation incident reports. Automatic characterization of pilots’ workloads is essential to preventing these kind of incidents. In addition, minimizing the physiological sensor network as much as possible remains both a challenge and a requirement. Electroencephalogram (EEG) signals have shown high correlations with specific cognitive and mental states, such as workload. However, there is not enough evidence in the literature to validate how well models generalize in cases of new subjects performing tasks with workloads similar to the ones included during the model’s training. In this paper, we propose a convolutional neural network to classify EEG features across different mental workloads in a continuous performance task test that partly measures working memory and working memory capacity. Our model is valid at the general population level and it is able to transfer task learning to pilot mental workload recognition in a simulated operational environment.
UR  - http://dx.doi.org/10.3390/app12052298
N1  - IAM; ADAS; 600.139; 600.145; 600.118
ID  - Aura Hernandez-Sabate2022
ER  -