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

• Published in: Frontiers in human neuroscience Vol. 10; p. 539
• Main Authors: Aricò, Pietro, Borghini, Gianluca, Di Flumeri, Gianluca, Colosimo, Alfredo, Bonelli, Stefano, Golfetti, Alessia, Pozzi, Simone, Imbert, Jean-Paul, Granger, Géraud, Benhacene, Raïlane, Babiloni, Fabio
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 26.10.2016; Frontiers; Frontiers Media S.A
• Subjects: Air traffic management (ATM); Automation; Brain; Brain research; Computer applications; Computer Science; EEG; electroencephalogram (EEG); Electroencephalography; Handbooks; human factors; human machine interaction; Human performance; Human-Computer Interaction; Implants; Learning; machine learning; Mental Workload; Neuroscience; Workloads; New York; Italy; human machine interaction; human factors; mental workload; Adaptive Automation (AA); passive brain-computer interface (pBCI); machine learning; Air Traffic Management (ATM); electroencephalogram (EEG)
• ISSN: 1662-5161; 1662-5161
• DOI: 10.3389/fnhum.2016.00539

(AA) is a promising approach to keep the task workload demand within appropriate levels in order to avoid both the - and 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, (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 (ATM) research simulator developed and hosted at ENAC (É of Toulouse, France). Twelve (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.