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Open AccessArticle

Neurophysiological Vigilance Characterisation and Assessment: Laboratory and Realistic Validations Involving Professional Air Traffic Controllers

1
BrainSigns srl, Lungotevere Michelangelo 9, 00192 Rome, Italy
2
Department of Molecular Medicine, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
3
IRCCS Fondazione Santa Lucia, Neuroelectrical Imaging and BCI Lab, Via Ardeatina 306, 00179 Rome, Italy
4
School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
*
Author to whom correspondence should be addressed.
The authors contributed equally to the work.
Brain Sci. 2020, 10(1), 48; https://doi.org/10.3390/brainsci10010048
Received: 29 November 2019 / Revised: 8 January 2020 / Accepted: 13 January 2020 / Published: 15 January 2020
(This article belongs to the Special Issue Brain Plasticity, Cognitive Training and Mental States Assessment)
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. View Full-Text
Keywords: vigilance; mental states assessment; high-resolution EEG; machine learning; psychomotor vigilance task; stepwise linear discriminant analysis; air traffic controllers; out-of-the-loop; ATM vigilance; mental states assessment; high-resolution EEG; machine learning; psychomotor vigilance task; stepwise linear discriminant analysis; air traffic controllers; out-of-the-loop; ATM
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MDPI and ACS Style

Sebastiani, M.; Di Flumeri, G.; Aricò, P.; Sciaraffa, N.; Babiloni, F.; Borghini, G. Neurophysiological Vigilance Characterisation and Assessment: Laboratory and Realistic Validations Involving Professional Air Traffic Controllers. Brain Sci. 2020, 10, 48.

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