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.

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Car driving is considered a very complex activity, consisting of different concomitant tasks and subtasks, thus it is crucial to understand the impact of different factors, such as road complexity, traffic, dashboard devices, and external events on the driver’s behavior and performance. For this reason, in particular situations the cognitive demand experienced by the driver could be very high, inducing an excessive experienced mental workload and consequently an increasing of error commission probability. In this regard, it has been demonstrated that human error is the main cause of the 57% of road accidents and a contributing factor in most of them. In this study, 20 young subjects have been involved in a real driving experiment, performed under different traffic conditions (rush hour and not) and along different road types (main and secondary streets). Moreover, during the driving tasks different specific events, in particular a pedestrian crossing the road and a car entering the traffic flow just ahead of the experimental subject, have been acted. A Workload Index based on the Electroencephalographic (EEG), i.e., brain activity, of the drivers has been employed to investigate the impact of the different factors on the driver’s workload. Eye-Tracking (ET) technology and subjective measures have also been employed in order to have a comprehensive overview of the driver’s perceived workload and to investigate the different insights obtainable from the employed methodologies. The employment of such EEG-based Workload index confirmed the significant impact of both traffic and road types on the drivers’ behavior (increasing their workload), with the advantage of being under real settings. Also, it allowed to highlight the increased workload related to external events while driving, in particular with a significant effect during those situations when the traffic was low. Finally, the comparison between methodologies revealed the higher sensitivity of neurophysiological measures with respect to ET and subjective ones. In conclusion, such an EEG-based Workload index would allow to assess objectively the mental workload experienced by the driver, standing out as a powerful tool for research aimed to investigate drivers’ behavior and providing additional and complementary insights with respect to traditional methodologies employed within road safety research.

Several models defining different types of cognitive human behaviour are available. For this work, we have selected the Skill, Rule and Knowledge (SRK) model proposed by Rasmussen in 1983. This model is currently broadly used in safety critical domains, such as the aviation. Nowadays, there are no tools able to assess at which level of cognitive control the operator is dealing with the considered task, that is if he/she is performing the task as an automated routine (skill level), as procedures-based activity (rule level), or as a problem-solving process (knowledge level). Several studies tried to model the SRK behaviours from a Human Factor perspective. Despite such studies, there are no evidences in which such behaviours have been evaluated from a neurophysiological point of view, for example, by considering brain activity variations across the different SRK levels. Therefore, the proposed study aimed to investigate the use of neurophysiological signals to assess the cognitive control behaviours accordingly to the SRK taxonomy. The results of the study, performed on 37 professional Air Traffic Controllers, demonstrated that specific brain features could characterize and discriminate the different SRK levels, therefore enabling an objective assessment of the degree of cognitive control behaviours in realistic settings.

This paper provides a focused and organized review of the research progress on neurophysiological indicators, also called "neurometrics," to show how they can effectively address some of the most important human factors (HFs) needs in the air traffic management (ATM) field. In order to better understand and highlight available opportunities of such neuroscientific applications, state of the art on the most involved HFs and related cognitive processes (e.g., mental workload and cognitive training) are presented together with examples of possible applications in current and future ATM scenarios. Furthermore, this paper will discuss the potential enhancements that further research and development activities could bring to the efficiency and safety of the ATM service.