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Mobile EEG-based situation awareness recognition for air traffic controllers

: Yeo, Lee Guan; Sun, Haoqi; Liu, Yisi; Trapsilawati, Fitri; Sourina, Olga; Chen, Chun-Hsien; Müller-Wittig, Wolfgang K.; Ang, Wei Tech


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society:
IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017 : Banff Center, Banff, Canada, October 5-8, 2017
Piscataway, NJ: IEEE, 2017
ISBN: 978-1-5386-1645-1
ISBN: 978-1-5386-1644-4
ISBN: 978-1-5386-1646-8
International Conference on Systems, Man, and Cybernetics (SMC) <2017, Banff>
Fraunhofer Singapore ()
air traffic control (ATC); Electroencephalography (EEG); situation awareness; human factor; neuroergonomic; Guiding Theme: Digitized Work; Research Area: Human computer interaction (HCI)

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.