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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Hum. Neurosci. | doi: 10.3389/fnhum.2019.00296

BCI-based adaptive automation to prevent Out-Of-The-Loop phenomenon in Air Traffic Controllers dealing with highly automated systems

 Gianluca Di Flumeri1, 2, 3*, Francesca De Crescenzio4,  Bruno Berberian5, Oliver Ohneiser6, Jan Kramer6,  Pietro Aricò1, 2, 3,  Gianluca Borghini1, 2, 3,  Fabio Babiloni1, 2, 7, Sara Bagassi4 and Sergio Piastra4
  • 1Department of Molecular Medicine, Faculty of Pharmacy and Medicine, Sapienza University of Rome, Italy
  • 2BrainSigns, Italy
  • 3Fondazione Santa Lucia (IRCCS), Italy
  • 4Dipartimento di Ingegneria Industriale, Università degli Studi di Bologna, Italy
  • 5Office National d'Études et de Recherches Aérospatiales, Palaiseau, France
  • 6Institut für Verkehrssystemtechnik, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Germany
  • 7Department of Computer Science, Hangzhou Dianzi University, China

Increasing the level of automation in Air Traffic Management is seen as a measure to increase the performance of the service to satisfy the predicted future demand. This is expected to result in new roles for the human operator: he will mainly monitor highly automated systems and seldom intervene. Therefore, Air Traffic Controllers (ATCos) would often work in a supervisory or control mode rather than in a direct operating mode. However, it has been demonstrated how human operators in such a role are affected by human performance issues, known as Out‐Of‐The‐Loop (OOTL) phenomenon, consisting in lack of attention, loss of situational awareness and de‐skilling. A countermeasure to this phenomenon has been identified in the Adaptive Automation, i.e. a system able to allocate the operative tasks to the machine or to the operator depending on their needs. In this context, psychophysiological measures have been highlighted as powerful tool to provide a reliable, unobtrusive and real-time assessment of the ATCo’s mental state to be used as control logic for Adaptive Automation-based systems.
In this paper it is presented the so-called “Vigilance and Attention Controller”, a system based on Electroencephalography (EEG) and Eye Tracking (ET) techniques, aimed to assess in real time the vigilance level of an ATCo dealing with a highly automated human machine interface and to use this measure to adapt the Level of Automation of the interface itself.
The system has been tested on 14 professional ATCos performing two highly realistic scenarios, one with the system disabled and one with the system enabled.
The results confirmed that (i) long high automated tasks induce vigilance decreasing and OOTL-related phenomena; (ii) EEG measures are sensitive to these kinds of mental impairments; and (iii) Adaptive Automation was able to counteract this negative effect by keeping the ATCo more involved within the operative task. The results were confirmed by EEG and ET measures as well as by performance and subjective ones, providing a clear example of potential applications and related benefits of Adaptive Automation.

Keywords: Electroencephalography, eye tracking, vigilance, Out-of-the-loop, passive BCI, Adaptive Automation, Air Traffic Control, human machine interface

Received: 30 Apr 2019; Accepted: 12 Aug 2019.

Edited by:

Chang-Hwan Im, Hanyang University, South Korea

Reviewed by:

Mickael Causse, National Higher School of Aeronautics and Space, France
Yongtian He, University of Houston, United States  

Copyright: © 2019 Di Flumeri, De Crescenzio, Berberian, Ohneiser, Kramer, Aricò, Borghini, Babiloni, Bagassi and Piastra. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: PhD. Gianluca Di Flumeri, Department of Molecular Medicine, Faculty of Pharmacy and Medicine, Sapienza University of Rome, Roma, 00185, Lazio, Italy, gluca.diflumeri@gmail.com