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Adaptive Automation triggered by EEG-based mental workload index: a passive Brain-Computer Interface application in realistic Air Traffic Control environment

Frontiers in Human Neuroscience. 2016;10 DOI 10.3389/fnhum.2016.00539


Journal Homepage

Journal Title: Frontiers in Human Neuroscience

ISSN: 1662-5161 (Online)

Publisher: Frontiers Media S.A.

LCC Subject Category: Medicine: Internal medicine: Neurosciences. Biological psychiatry. Neuropsychiatry

Country of publisher: Switzerland

Language of fulltext: English

Full-text formats available: PDF, HTML, ePUB, XML



Pietro Aricò (University of Rome Sapienz)

Pietro Aricò (BrainSigns s.r.l.)

Pietro Aricò (IRCCS Fondazione Santa Lucia)

Gianluca Borghini (University of Rome Sapienza )

Gianluca Borghini (BrainSigns s.r.l.)

Gianluca Borghini (IRCCS Fondazione Santa Lucia)

Gianluca Di Flumeri (University of Rome Sapienza)

Gianluca Di Flumeri (BrainSigns s.r.l.)

Gianluca Di Flumeri (IRCCS Fondazione Santa Lucia)

Alfredo Colosimo (University of Rome Sapienza )

Stefano Bonelli (DeepBlue s.r.l.)

Alessia Golfetti (DeepBlue s.r.l.)

Simone Pozzi (DeepBlue s.r.l.)

Jean Paul Imbert (ENAC - Ecole Nationale de l'Aviation Civile)

Géraud Granger (ENAC - Ecole Nationale de l'Aviation Civile)

Railane Benhacene (ENAC - Ecole Nationale de l'Aviation Civile)

Fabio Babiloni (University of Rome Sapienza)

Fabio Babiloni (BrainSigns s.r.l.)


Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 14 weeks


Abstract | Full Text

Adaptive Automation (AA) is a promising approach to keep the task workload demand within appropriate levels in order to avoid both the under- and overload conditions, hence enhancing the overall performance and safety of the human-machine system. The main issue on the use of AA is how to trigger the AA solutions without affecting the operative task. In this regard, passive Brain-Computer Interface (pBCI) systems are a good candidate to activate automation, since they are able to gather information about the covert behaviour (e.g. mental workload) of a subject by analysing its neurophysiological signals (i.e. brain activity), and without interfering with the ongoing operational activity. We proposed a pBCI system able to trigger AA solutions integrated in a realistic Air Traffic Management (ATM) research simulator developed and hosted at ENAC (École Nationale de l’Aviation Civile of Toulouse, France). Twelve Air Traffic Controller (ATCO) students have been involved in the experiment and they have been asked to perform ATM scenarios with and without the support of the AA solutions. Results demonstrated the effectiveness of the proposed pBCI system, since it enabled the AA mostly during the high-demanding conditions (i.e. overload situations) inducing a reduction of the mental workload under which the ATCOs were operating. On the contrary, as desired, the AA was not activated when workload level was under the threshold, to prevent too low demanding conditions that could bring the operator’s workload level towards potentially dangerous conditions of underload.