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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.2019.00303

How neurophysiological measures can be used to enhance the evaluation of remote tower solutions

 Pietro Aricò1, 2, 3*,  Maxime Reynal4,  Gianluca Di Flumeri1, 2, 3,  Gianluca Borghini1, 2, 3, Nicolina Sciaraffa1, 2, 3,  Jean-Paul Imbert4, Christophe Hurter4, Michela Terenzi5, Ana Ferreira5,  Simone Pozzi5, Viviana Betti1, Matteo Marucci1, Alexandru Telea6 and  Fabio Babiloni1, 2, 3, 7
  • 1Sapienza University of Rome, Italy
  • 2BrainSigns, Italy
  • 3Fondazione Santa Lucia (IRCCS), Italy
  • 4École nationale de l'aviation civile, France
  • 5Deep Blue (Italy), Italy
  • 6University of Groningen, Netherlands
  • 7Hangzhou Dianzi University, China

New solutions in operational environments are often, among objective measurements, evaluated by using subjective assessment and judgement from experts. Anyhow, it has been demonstrated that subjective measures suffer from poor resolution due to a high intra and inter operator variability. Also, performance measures, if available, could provide just partial information, since an operator could achieve the same performance but experiencing a different workload. In this study we aimed to demonstrate i) the higher resolution of neurophysiological measures in comparison to subjective ones, and ii) how the simultaneous employment of neurophysiological measures and behavioural ones could allow a holistic assessment of operational tools. In this regard, we tested the effectiveness of an EEG-based neurophysiological index (WEEG index) in comparing two different solutions (i.e. Normal and Augmented) in terms of experienced workload. In this regard, 16 professional Air Traffic Controllers (ATCOs) have been asked to perform two operational scenarios. Galvanic Skin Response (GSR) has also been recorded to evaluate the level of arousal (i.e. operator involvement) during the two scenarios execution. NASA-TLX questionnaire has been used to evaluate the perceived workload, and an expert was asked to assess performance achieved by the ATCOs. Finally, reaction times on specific operational events relevant for the assessment of the two solutions, have also been collected. Results highlighted that the Augmented solution induced a local increase in subjects performance (Reaction times). At the same time, this solution induced an increase in the workload experienced by the participants (WEEG). Anyhow, this increase is still acceptable, since it did not negatively impact the performance and has to be intended only as a consequence of the higher engagement of the ATCOs. This behavioural effect is totally in line with physiological results obtained in terms of arousal (GSR), that increased during the scenario with augmentation. Subjective measures (NASA-TLX) did not highlight any significant variation in perceived workload. These results suggest that neurophysiological measure provide additional information than behavioural and subjective ones, even at a level of few seconds, and its employment during the pre-operational activities (e.g. design process) could allow a more holistic and accurate evaluation of new solutions.

Keywords: EEG, ECG, GSR, Eye blink, Mental Workload, Remote Tower Air Traffic Management, machine learning, asSWLDA

Received: 30 Apr 2019; Accepted: 14 Aug 2019.

Copyright: © 2019 Aricò, Reynal, Di Flumeri, Borghini, Sciaraffa, Imbert, Hurter, Terenzi, Ferreira, Pozzi, Betti, Marucci, Telea and Babiloni. 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: Dr. Pietro Aricò, Sapienza University of Rome, Rome, Italy, pietro.arico@uniroma1.it