EEG-based cognitive control behaviour assessment: an ecological study with professional air traffic controllers

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 setting

Human Factors and Neurophysiological Metrics in Air Traffic Control: a Critical Review

International audienceThis article provides the reader a focused and organised review of the research progresses on neurophysiological indicators, also called “neurometrics”, to show how neurometrics could effectively address some of the most important Human Factors (HFs) needs in the Air Traffic Management (ATM) field. The state of the art on the most involved HFs and related cognitive processes (e.g. mental workload, cognitive training) is presented together with examples of possible applications in the current and future ATM scenarios, in order to better understand and highlight the available opportunities of such neuroscientific applications. Furthermore, the paper will discuss the potential enhancement that further research and development activities could bring to the efficiency and safety of the ATM service

Skill, Rule and Knowledge - based Behaviour Detection by Means of ATCOs’ Brain Activity

International audienceThe aim of this work was to test a neuro-physiological methodology able to discriminate the Skill (S), Rule (R) and Knowledge (K) based cognitive control levels of Air-Traffic-Controllers’ performing realistic traffic management tasks . The three categories of human behaviours have been associated to specific cognitive functions (e.g. attention, memory, decision making) already investigated with Electroencephalography (EEG) measurements. A link between S-R-K behaviours and expected frequency bands configurations has been hypothesized. Eventually, specific events have been designed to trigger S, R and K like behaviours and then integrated into realistic Air Traffic Management (ATM) simulations. A machine-learning algorithm has been used to differentiate the three different levels of cognitive control by using brain features extracted from the EEG rhythms of different brain areas, that is, the frontal theta and the parietal alpha activities. Twelve professional Air-Traffic-Controllers (ATCOs) from the École Nationale de l’Aviation Civile (ENAC) of Toulouse (France) have been involved in the study. The results showed that the algorithm was able to differentiate with high discrimination accuracy (AUC > 0.7) the three S-R-K cognitive behaviours during simulated air-traffic scenarios in an ecological ATM environmen

Adaptive Automation triggered by EEG-based mental workload index: a passive Brain-Computer Interface application in realistic Air Traffic Control environment

Adaptive Automation (AA) is a promising approach to keep the task workload demand within appropriate levels in order to avoid both the under- and overload 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 behaviour (e.g. mental workload) of a subject by analysing 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 towards potentially dangerous conditions of underload

MOBILITY4EU - D2.1 - Societal needs and requirements for future transportation and mobility as well as opportunities and challenges of current solutions

Mobility4EU is a Coordination and Support Action of the European Commission started in January 2016 and lasting for 3 years, until 31 December 2018. The project will deliver a vision for the European transport system in 2030 and an action plan including a roadmap to implement that vision. The work towards that vision and action plan is based on the identification and assessment of societal challenges that will influence future transport demand and supply and the compilation of a portfolio of promising cross-modal technical and organisational transport solutions. The entire process from studying trends and options for solutions, developing a vision and finally the action plan are organized within a structured participatory approach that focuses on user-centeredness and that aims to engage a broad stakeholder community into the consultation processes. A further goal is to build a European Transport Forum that continues the work beyond the project duration and works on complementing the action plan. The present document reports on the results of researching trends and societal drivers impacting mobility demands and transport in Europe until 2030

MOBILITY4EU - D2.1 - Societal needs and requirements for future transportation and mobility as well as opportunities and challenges of current solutions

Mobility4EU is a Coordination and Support Action of the European Commission started in January 2016 and lasting for 3 years, until 31 December 2018. The project will deliver a vision for the European transport system in 2030 and an action plan including a roadmap to implement that vision. The work towards that vision and action plan is based on the identification and assessment of societal challenges that will influence future transport demand and supply and the compilation of a portfolio of promising cross-modal technical and organisational transport solutions. The entire process from studying trends and options for solutions, developing a vision and finally the action plan are organized within a structured participatory approach that focuses on user-centeredness and that aims to engage a broad stakeholder community into the consultation processes. A further goal is to build a European Transport Forum that continues the work beyond the project duration and works on complementing the action plan. The present document reports on the results of researching trends and societal drivers impacting mobility demands and transport in Europe until 2030

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

International audienceAdaptive 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