Analysis of neurophysiological signals for the training and mental workload assessment of ATCos

Gianluca Borghini (1) , Pietro Aricò (1) , Ilenia Graziani (1) , Serenella Salinari (1) , Fabio Babiloni (1) , Jean-Paul Imbert (2) , Géraud Granger (3) , Raïlane Benhacene (3) , Linda Napoletano (4) , Michela Terenzi (4) , Simone Pozzi (4)

1 Dept. Human Physiology
2 LII - ENAC - Equipe Informatique Interactive
3 IHM Aero - ENAC - Programme transverse IHM Aéronautique
4 Deep Blue Research and Consulting

The aim of this paper is to present an extensive neurophysiological study of the Air-Traffic-Controllers (ATCos) during en route ATC simulations. In other words, the purpose was to extract neurophysiological features suitable for evaluate the learning progress and for estimate in real-time the user's workload level. In collaboration with ENAC (Toulouse, France), a task specific for the en-route ATCo has been developed and tested. The subjects have been asked to learn how to complete the task within a training period of a week and, in the second week, to execute it under different difficulty levels. During the experiments, the Electroencephalogram (EEG), the Electrocardiogram (ECG), the Electrooculogram (EOG), the behavioral data and the perception of the workload (NASA-TLX) have been collected. The results showed that the frontal theta power spectral density (PSD), the parietal alpha PSD, the heart rate (HR) and the eyeblinks rate (EBR) are reliable features by which evaluating the learning progress and the user's workload. It has been demonstrated that it could be possible i) to quantify how well the subjects complete a task, and ii) to compare subject's performances, in terms of cognitive resources. In addition, it has been presented i) a system able to significantly differentiate three workload levels, and ii) how the subjective features used for the workload evaluation remain stable over the time.

Mots clés: EEG; ECG; EOG; cognitive learning; training assessment; workload; ATM; ATC

Domaines: Interface homme-machine [cs.HC]

Gianluca Borghini, Pietro Aricò, Ilenia Graziani, Serenella Salinari, Fabio Babiloni, et al.. Analysis of neurophysiological signals for the training and mental workload assessment of ATCos. SID 2014, 4th SESAR Innovation Days, Nov 2014, Madrid, Spain. ⟨hal-01087183⟩