From Raw Data to Practical Application: EEG Parameters for Human Performance Studies in Air Traffic Control

被引:0
作者
Suarez, Maria Zamarreno [1 ]
Martinez, Juan Marin [1 ]
Moreno, Francisco Perez [1 ]
Jurado, Raquel Delgado-Aguilera [1 ]
de Frutos, Patricia Maria Lopez [2 ]
Valdes, Rosa Maria Arnaldo [1 ]
机构
[1] Univ Politecn Madrid UPM, Sch Aerosp Engn, Dept Aerosp Syst Air Transport & Airports, Madrid 28040, Spain
[2] ATM Res & Dev Reference Ctr CRIDA, Madrid 28022, Spain
关键词
air traffic control; human performance; human factors; electroencephalography; brain activity; parameters; software; EMOTION;
D O I
10.3390/aerospace11010030
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
The use of electroencephalography (EEG) techniques has many advantages in the study of human performance in air traffic control (ATC). At present, these are non-intrusive techniques that allow large volumes of data to be recorded on a continuous basis using wireless equipment. To achieve the most with these techniques, it is essential to establish appropriate EEG parameters with a clear understanding of the process followed to obtain them and their practical application. This study explains, step by step, the approach adopted to obtain six EEG parameters: excitement, stress, boredom, relaxation, engagement, and attention. It then explains all the steps involved in analysing the relationship between these parameters and two other parameters that characterise the state of the air traffic control sector during the development of real-time simulations (RTS): taskload and number of simultaneous aircraft. For this case study, the results showed the highest relationships for the engagement and attention parameters. In general, the results confirmed the potential of using these EEG parameters.
引用
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页数:27
相关论文
共 38 条
[1]  
[Anonymous], Contact Quality (CQ) vs. EEG Quality (EQ)
[2]   Adaptive Automation Triggered by EEG-Based Mental Workload Index: A Passive Brain-Computer Interface Application in Realistic Air Traffic Control Environment [J].
Arico, Pietro ;
Borghini, Gianluca ;
Di Flumeri, Gianluca ;
Colosimo, Alfredo ;
Bonelli, Stefano ;
Golfetti, Alessia ;
Pozzi, Simone ;
Imbert, Jean-Paul ;
Granger, Geraud ;
Benhacene, Railane ;
Babiloni, Fabio .
FRONTIERS IN HUMAN NEUROSCIENCE, 2016, 10
[3]   The effects of increased mental workload of air traffic controllers on time perception: Behavioral and physiological evidence [J].
Balta, Eirini ;
Psarrakis, Andreas ;
Vatakis, Argiro .
APPLIED ERGONOMICS, 2024, 115
[4]  
Bastos TF, 2012, 4TH INTERNATIONAL CONFERENCE ON INTELLIGENT HUMAN COMPUTER INTERACTION (IHCI 2012)
[5]   Non-normal data: Is ANOVA still a valid option? [J].
Blanca, Maria J. ;
Alarcon, Rafael ;
Arnau, Jaume ;
Bono, Roser ;
Bendayan, Rebecca .
PSICOTHEMA, 2017, 29 (04) :552-557
[6]   EEG-Based Cognitive Control Behaviour Assessment: an Ecological study with Professional Air Traffic Controllers [J].
Borghini, Gianluca ;
Arico, Pietro ;
Di Flumeri, Gianluca ;
Cartocci, Giulia ;
Colosimo, Alfredo ;
Bonelli, Stefano ;
Golfetti, Alessia ;
Imbert, Jean Paul ;
Granger, Geraud ;
Benhacene, Railane ;
Pozzi, Simone ;
Babiloni, Fabio .
SCIENTIFIC REPORTS, 2017, 7
[7]  
Giraldo S., 2013, P 3 INT C MUSIC EMOT
[8]   Using machine learning methods and EEG to discriminate aircraft pilot cognitive workload during flight [J].
Gorji, Hamed Taheri ;
Wilson, Nicholas ;
VanBree, Jessica ;
Hoffmann, Bradley ;
Petros, Thomas ;
Tavakolian, Kouhyar .
SCIENTIFIC REPORTS, 2023, 13 (01)
[9]  
Guragain B, 2019, IEEE ENG MED BIO, P4060, DOI [10.1109/EMBC.2019.8856429, 10.1109/embc.2019.8856429]
[10]  
Hamid NHA, 2015, 2015 IEEE 6TH CONTROL AND SYSTEM GRADUATE RESEARCH COLLOQUIUM (ICSGRC), P135, DOI 10.1109/ICSGRC.2015.7412480