Abstract

With the growing volume and complexity of air traffic, air traffic controllers (ATCOs) encounter heavier burden nowadays. Therefore, human factors study in air traffic control (ATC) is increasingly essential, paving the way to a safer air transportation system. In this paper, we conducted an ATC experiment, where Electroencephalogram (EEG) data were collected throughout the experiment. Compared to traditional questionnaires and psychological tests used in human factors study, the proposed novel EEG approach provides monitoring of situation awareness (SA) in a non-invasive and non-interruptive fashion. SA was represented as the response latency in situation present assessment method (SPAM), which was predicted from EEG signals using three machine learning algorithms. Support vector regression obtained the lowest prediction error of 1.5 seconds, which is lower than 10% of the range of actual response latency. The results show that EEG is a promising approach forward in measuring situation awareness of ATCOs in both real-time and accurate manner.

Abstract

To deal with the increasing demands in Air Traffic Control (ATC), new working place designs are proposed and developed that need novel human factors evaluation tools. In this paper, we propose a novel application of Electroencephalogram (EEG)-based emotion, workload, and stress recognition algorithms to investigate the optimal length of training for Air Traffic Control Officers (ATCOs) to learn working with threedimensional (3D) display as a supplementary to the existing 2D display. We tested and applied the state-of-the-art EEG-based subject-dependent algorithms. The following experiment was carried out. Twelve ATCOs were recruited to take part in the experiment. The participants were in charge of the Terminal Control Area, providing navigation assistance to aircraft departing and approaching the airport using 2D and 3D displays. EEG data were recorded, and traditional human factors questionnaires were given to the participants after 15-minute, 60- minute, and 120-minute training. Different from the questionnaires, the EEG-based evaluation tools allow the recognition of emotions, workload, and stress with different temporal resolutions during the task performance by subjects. The results showed that 50-minute training could be enough for the ATCOs to learn the new display setting as they had relatively low stress and workload. The study demonstrated that there is a potential of applying the EEG-based human factors evaluation tools to assess novel system designs in addition to traditional questionnaire and feedback, which can be beneficial for future improvements and developments of the systems and interfaces.