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.

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.

In this study, we investigated the possibility to evaluate the impact of different avionic technologies on the mental workload of helicopter's pilots by measuring their brain activity with the EEG during a series of simulated missions carried out at AgustaWestland facilities in Yeovil (UK). The tested avionic technologies were: i) Head-Up Display (HUD); ii) Head-Mounted Display (HMD); iii) Full Conformal symbology (FC); iv) Flight Guidance (FG) symbology; v) Synthetic Vision System (SVS); and vi) Radar Obstacles (RO) detection system. It has been already demonstrated that in cognitive tasks, when the cerebral workload increases the EEG power spectral density (PSD) in theta band over frontal areas increases, and the EEG PSD in alpha band decreases over parietal areas. A mental workload index (MWL) has been here defined as the ratio between the frontal theta and parietal alpha EEG PSD values. Such index has been used for testing and comparing the different avionic technologies. Results suggested that the HUD provided a significant (p<;.05) workload reduction across all the flight scenarios with respect to the other technologies. In addition, the simultaneous use of FC and FG technologies (FC+FG) produced a significant decrement of the workload (p<;.01) with respect to the use of only the FC. Moreover, the use of the SVS technology provided on Head Down Display (HDD) with the simultaneous use of FC+FG and the RO seemed to produce a lower cerebral workload when compared with the use of only the FC. Interestingly, the workload estimation by means of subjective measures, provided by pilots through a NASA-TLX questionnaire, did not provide any significant differences among the different flight scenarios. These results suggested that the proposed MWL cognitive neurometrics could be used as a reliable measure of the user's mental workload, being a valid indicator for the comparison and the test of different avionic technologies.

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