Skip to Main content Skip to Navigation
Journal articles

Adaptive Automation Triggered by EEG-Based Mental Workload Index: A Passive Brain-Computer Interface Application in Realistic Air Traffic Control Environment

Abstract : Adaptive Automation (AA) is a promising approach to keep the task workload demand within appropriate levels in order to avoid both the under- and over-load 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 behavior (e.g., mental workload) of a subject by analyzing 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 toward potentially dangerous conditions of underload.
Complete list of metadatas

Cited literature [69 references]  Display  Hide  Download

https://hal-enac.archives-ouvertes.fr/hal-01395581
Contributor : Laurence Porte <>
Submitted on : Thursday, November 10, 2016 - 11:39:42 PM
Last modification on : Wednesday, July 24, 2019 - 11:50:44 PM
Document(s) archivé(s) le : Monday, March 20, 2017 - 4:10:51 PM

File

fnhum-10-00539.pdf
Publication funded by an institution

Identifiers

Collections

ENAC | IHM | ACHIL | LII

Citation

Pietro Aricò, Gianluca Borghini, Gianluca Di Flumeri, Alfredo Colosimo, Stefano Bonelli, et al.. 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, Frontiers, 2016, ⟨10.3389/fnhum.2016.00539⟩. ⟨hal-01395581⟩

Share

Metrics

Record views

232

Files downloads

838