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

Applied Sciences

Pau Folch¹  Debora Gil²  Aura Hernández-Sabaté²  José Yauri²  Miquel Àngel Piera³ 

[1] Aslogic, Parc de Recerca UAB, Bellaterra, 08193 Barcelona, Spain;Computer Vision Center, Universitat Autònoma de Barcelona, Bellaterra, 08193 Barcelona, Spain;Telecommunications and Systems Engineering Department, Universitat Autònoma de Barcelona, Bellaterra, 08193 Barcelona, Spain;

关键词: cognitive states;    mental workload;    EEG analysis;    neural networks;    multimodal data fusion;   

DOI  :  10.3390/app12052298

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown