Stress Assessment by Combining Neurophysiological Signals and Radio Communications of Air Traffic Controllers.

Air Traffic Control (ATC) has been classified as the fourth most stressful job. In this regard, sixteen controllers were asked to perform ecological ATC simulation during which behavioral (Radio Communications with pilots - RCs), subjective (stress perception) and neurophysiological signals (brain activity and skin conductance - SC) were collected. All the considered parameters reported significant changes under high stress conditions.

A multimodal and signals fusion approach for assessing the impact of stressful events on Air Traffic Controllers.

Stress is a word used to describe human reactions to emotionally, cognitively and physically challenging experiences. A hallmark of the stress response is the activation of the autonomic nervous system, resulting in the "fight-freeze-flight" response to a threat from a dangerous situation. Consequently, the capability to objectively assess and track a controller's stress level while dealing with air traffic control (ATC) activities would make it possible to better tailor the work shift and maintain high safety levels, as well as to preserve the operator's health.

How Neurophysiological Measures Can be Used to Enhance the Evaluation of Remote Tower Solutions.

New solutions in operational environments are often, among objective measurements, evaluated by using subjective assessment and judgment from experts. Anyhow, it has been demonstrated that subjective measures suffer from poor resolution due to a high intra and inter-operator variability. Also, performance measures, if available, could provide just partial information, since an operator could achieve the same performance but experiencing a different workload.

Human-Machine Interaction Assessment by Neurophysiological Measures: A Study on Professional Air Traffic Controllers.

This study aims at investigating the possibility to employ neurophysiological measures to assess the humanmachine interaction effectiveness. Such a measure can be used to compare new technologies or solutions, with the final purpose to enhance operator's experience and increase safety. In the present work, two different interaction modalities (Normal and Augmented) related to Air Traffic Management field have been compared, by involving 10 professional air traffic controllers in a control tower simulated environment.

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

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.

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.

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

(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.

Reliability over time of EEG-based mental workload evaluation during Air Traffic Management (ATM) tasks.

Machine-learning approaches for mental workload (MW) estimation by using the user brain activity went through a rapid expansion in the last decades. In fact, these techniques allow now to measure the MW with a high time resolution (e.g.

Attentional costs and failures in air traffic control notifications.

Large display screens are common in supervisory tasks, meaning that alerts are often perceived in peripheral vision. Five air traffic control notification designs were evaluated in their ability to capture attention during an ongoing supervisory task, as well as their impact on the primary task. A range of performance measures, eye-tracking and subjective reports showed that colour, even animated, was less effective than movement, and notifications sometimes went unnoticed.