Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals

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 addi...

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Bibliographic Details
Published in:Applied sciences 2022-03, Vol.12 (5), p.2298
Main Authors: Hernández-Sabaté, Aura, Yauri, José, Folch, Pau, Piera, Miquel Àngel, Gil, Debora
Format: Article
Language:English
Subjects:
Automatic pilots
Aviation
cognitive states
EEG
EEG analysis
Electrocardiography
Electroencephalography
Memory
Mental task performance
mental workload
multimodal data fusion
Multitasking
Neural networks
Pilots
Recognition
Sensors
Short term memory
Transfer learning
Working conditions
Workload
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app12052298