Citation

Sebastiani M, Di Flumeri G, Aricò P, Sciaraffa N, Babiloni F, Borghini G. Brain Sci. 2020; 10(1): e48.

Affiliation

IRCCS Fondazione Santa Lucia, Neuroelectrical Imaging and BCI Lab, Via Ardeatina 306, 00179 Rome, Italy.

Copyright

(Copyright © 2020, Switzerland Molecular Diversity Preservation International (MDPI) AG)

DOI

10.3390/brainsci10010048

PMID

31952181

Abstract

Vigilance degradation usually causes significant performance decrement. It is also considered the major factor causing the out-of-the-loop phenomenon (OOTL) occurrence. OOTL is strongly related to a high level of automation in operative contexts such as the Air Traffic Management (ATM), and it could lead to a negative impact on the Air Traffic Controllers' (ATCOs) engagement. As a consequence, being able to monitor the ATCOs' vigilance would be very important to prevent risky situations. In this context, the present study aimed to characterise and assess the vigilance level by using electroencephalographic (EEG) measures. The first study, involving 13 participants in laboratory settings allowed to find out the neurophysiological features mostly related to vigilance decrements. Those results were also confirmed under realistic ATM settings recruiting 10 professional ATCOs. The results demonstrated that (i) there was a significant performance decrement related to vigilance reduction; (ii) there were no substantial differences between the identified neurophysiological features in controlled and ecological settings, and the EEG-channel configuration defined in laboratory was able to discriminate and classify vigilance changes in ATCOs' vigilance with high accuracy (up to 84%); (iii) the derived two EEG-channel configuration was able to assess vigilance variations reporting only slight accuracy reduction.

Language: en

Keywords

ATM; air traffic controllers; high-resolution EEG; machine learning; mental states assessment; out-of-the-loop; psychomotor vigilance task; stepwise linear discriminant analysis; vigilance