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

Pietro Aricò,Stefano Bonelli,Géraud Granger,Fabio Babiloni,Alessia Golfetti,Gianluca Di Flumeri,Jean-Paul Imbert,Simone Pozzi,Gianluca Borghini,Alfredo Colosimo,Raïlane Benhacene

doi:10.3389/fnhum.2016.00539

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.

Highlights

The main goal of Human Factor (HF) studies is to ensure good interactions between the work environment and human capabilities (Wickens, 1992)
We present a passive-BCI system fully integrated with a high realistic Air Traffic Management (ATM) simulator able to trigger adaptive solutions in real-time depending on the mental workload estimated by means of the Air Traffic Controller (ATCO)’s brain activity
We expected that the proposed system was able: (i) to trigger in the right way the ATM interface, and (ii) to induce a decreasing of the mental workload perceived by the operators when the adaptive solutions were activated and an increasing in task performances execution

Summary

Introduction

The main goal of Human Factor (HF) studies is to ensure good interactions between the work environment and human capabilities (Wickens, 1992). An AA-based system is able to adjust continuously the proper LOA, i.e., to assign the authority on specific functions to either the humans or the automated system, depending on the task difficulty and the operator’s workload It has been demonstrated how Adaptive Automation is superior to Static Automation, since the former is able to ensure operator’s workload within the optimum range, preserve his/her skill level, guarantee continuous task involvement, and vigilance, increasing his/her performance (Rouse, 1988; Wickens, 1992; Byrne and Parasuraman, 1996). Neurophysiological measures can be used to trigger the AA, and to highlight why AAs are important for enhance the safety in high-risk and high-demanding tasks This potentially offers new perspectives for adaptive intervention to optimize performance by acting on specific aspects of the operator’s behavior

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