# Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals +
###### Abstract  
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.  
cognitive states; mental workload; EEG analysis; neural networks; multimodal data fusion +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).  
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.  
prevents one from studying workload directly, it is feasible to infer it from various quantifiable variables [8]. There exist many proposals for recognizing workload based on physiological features, such as hearth rate, eye movement and dilation, electroencephalogram (EEG), and electrocardiogram (ECG) [9; 10]. The recent emergence of low cost EEG headsets has driven new researches (such as interaction with home devices, teaching-learning educative methods, and mentally control robotic arms) further than the medical screening of neurological disorders. In the particular case of cognitive state assessment, EEG alone is becoming the preferred sensor for addressing its characterization [11; 12; 13]. However, there is not enough evidence in the literature to validate how well models generalize to new subjects performing tasks of a workload similar to the ones included during the model's training.  
The goal of this study was to characterize the mental workloads of airplane pilots in the cockpit from the analysis of EEG signals.  
The remainder of this paper is organized as follows: Section 2 summarizes the state-of-the-art related work. Section 3 details the ground truth generation. Section 4 explains the models used to recognize the different levels of workload. Section 5 presents the experimental design. Section 6 is devoted to the experimental results. Finally, Section 7 outlines the conclusions and future work.