Literature DB >> 27590973

A passive brain-computer interface application for the mental workload assessment on professional air traffic controllers during realistic air traffic control tasks.

P Aricò1, G Borghini1, G Di Flumeri1, A Colosimo2, S Pozzi3, F Babiloni4.   

Abstract

In the last decades, it has been a fast-growing concept in the neuroscience field. The passive brain-computer interface (p-BCI) systems allow to improve the human-machine interaction (HMI) in operational environments, by using the covert brain activity (eg, mental workload) of the operator. However, p-BCI technology could suffer from some practical issues when used outside the laboratories. In particular, one of the most important limitations is the necessity to recalibrate the p-BCI system each time before its use, to avoid a significant reduction of its reliability in the detection of the considered mental states. The objective of the proposed study was to provide an example of p-BCIs used to evaluate the users' mental workload in a real operational environment. For this purpose, through the facilities provided by the École Nationale de l'Aviation Civile of Toulouse (France), the cerebral activity of 12 professional air traffic control officers (ATCOs) has been recorded while performing high realistic air traffic management scenarios. By the analysis of the ATCOs' brain activity (electroencephalographic signal-EEG) and the subjective workload perception (instantaneous self-assessment) provided by both the examined ATCOs and external air traffic control experts, it has been possible to estimate and evaluate the variation of the mental workload under which the controllers were operating. The results showed (i) a high significant correlation between the neurophysiological and the subjective workload assessment, and (ii) a high reliability over time (up to a month) of the proposed algorithm that was also able to maintain high discrimination accuracies by using a low number of EEG electrodes (~3 EEG channels). In conclusion, the proposed methodology demonstrated the suitability of p-BCI systems in operational environments and the advantages of the neurophysiological measures with respect to the subjective ones.
© 2016 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Air traffic management; Augmented cognition; Automatic-stop stepwise linear discriminant analysis; Electroencephalogram; Human factor; Instantaneous self-assessment; Mental workload; Neuroergonomic; Passive brain–computer interface; Stepwise linear discriminant analysis

Mesh:

Year:  2016        PMID: 27590973     DOI: 10.1016/bs.pbr.2016.04.021

Source DB:  PubMed          Journal:  Prog Brain Res        ISSN: 0079-6123            Impact factor:   2.453


  19 in total

Review 1.  Human Mental Workload: A Survey and a Novel Inclusive Definition.

Authors:  Luca Longo; Christoper D Wickens; Gabriella Hancock; Peter A Hancock
Journal:  Front Psychol       Date:  2022-06-02

2.  Joint Analysis of Eye Blinks and Brain Activity to Investigate Attentional Demand during a Visual Search Task.

Authors:  Nicolina Sciaraffa; Gianluca Borghini; Gianluca Di Flumeri; Febo Cincotti; Fabio Babiloni; Pietro Aricò
Journal:  Brain Sci       Date:  2021-04-28

Review 3.  Consumer Behaviour through the Eyes of Neurophysiological Measures: State-of-the-Art and Future Trends.

Authors:  Patrizia Cherubino; Ana C Martinez-Levy; Myriam Caratù; Giulia Cartocci; Gianluca Di Flumeri; Enrica Modica; Dario Rossi; Marco Mancini; Arianna Trettel
Journal:  Comput Intell Neurosci       Date:  2019-09-18

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

Authors:  Pietro Aricò; Gianluca Borghini; Gianluca Di Flumeri; Alfredo Colosimo; Stefano Bonelli; Alessia Golfetti; Simone Pozzi; Jean-Paul Imbert; Géraud Granger; Raïlane Benhacene; Fabio Babiloni
Journal:  Front Hum Neurosci       Date:  2016-10-26       Impact factor: 3.169

5.  Brain Interaction during Cooperation: Evaluating Local Properties of Multiple-Brain Network.

Authors:  Nicolina Sciaraffa; Gianluca Borghini; Pietro Aricò; Gianluca Di Flumeri; Alfredo Colosimo; Anastasios Bezerianos; Nitish V Thakor; Fabio Babiloni
Journal:  Brain Sci       Date:  2017-07-21

6.  ICA-Derived EEG Correlates to Mental Fatigue, Effort, and Workload in a Realistically Simulated Air Traffic Control Task.

Authors:  Deepika Dasari; Guofa Shou; Lei Ding
Journal:  Front Neurosci       Date:  2017-05-30       Impact factor: 4.677

7.  A New Perspective for the Training Assessment: Machine Learning-Based Neurometric for Augmented User's Evaluation.

Authors:  Gianluca Borghini; Pietro Aricò; Gianluca Di Flumeri; Nicolina Sciaraffa; Alfredo Colosimo; Maria-Trinidad Herrero; Anastasios Bezerianos; Nitish V Thakor; Fabio Babiloni
Journal:  Front Neurosci       Date:  2017-06-13       Impact factor: 4.677

8.  EEG-Based Cognitive Control Behaviour Assessment: an Ecological study with Professional Air Traffic Controllers.

Authors:  Gianluca Borghini; Pietro Aricò; Gianluca Di Flumeri; Giulia Cartocci; Alfredo Colosimo; Stefano Bonelli; Alessia Golfetti; Jean Paul Imbert; Géraud Granger; Railane Benhacene; Simone Pozzi; Fabio Babiloni
Journal:  Sci Rep       Date:  2017-04-03       Impact factor: 4.379

9.  Benchmarking Brain-Computer Interfaces Outside the Laboratory: The Cybathlon 2016.

Authors:  Domen Novak; Roland Sigrist; Nicolas J Gerig; Dario Wyss; René Bauer; Ulrich Götz; Robert Riener
Journal:  Front Neurosci       Date:  2018-01-11       Impact factor: 4.677

10.  In silico vs. Over the Clouds: On-the-Fly Mental State Estimation of Aircraft Pilots, Using a Functional Near Infrared Spectroscopy Based Passive-BCI.

Authors:  Thibault Gateau; Hasan Ayaz; Frédéric Dehais
Journal:  Front Hum Neurosci       Date:  2018-05-17       Impact factor: 3.169

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