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Monitoring pilot’s cognitive fatigue with engagement features in simulated and actual flight conditions using an hybrid fNIRS-EEG passive BCI

Dehais, Frédéric and Duprès, Alban and Di Flumeri, Gianluca and Verdière, Kevin J. and Borghini, Gianluca and Babiloni, Fabio and Roy, Raphaëlle N. Monitoring pilot’s cognitive fatigue with engagement features in simulated and actual flight conditions using an hybrid fNIRS-EEG passive BCI. (2018) In: IEEE Systems, Man, and Cybernetics Society (SMC 2018), 7 October 2018 - 10 October 2018 (Miyazaki, Japan).

(Document in English)

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There is growing interest for implementing tools to monitor cognitive performance in naturalistic environments. Recent technological progress has allowed the development of new generations of brain imaging systems such as dry electrodes electroencephalography (EEG) and functional near infrared spec- troscopy (fNIRS) to investigate cortical activity in a variety of human tasks out of the laboratory. These highly portable brain imaging devices offer interesting prospects to implement passive brain computer interfaces (pBCI) and neuroadaptive technology. We developed a fNIRS-EEG based pBCI to monitor cognitive fatigue using engagement related features (EEG engagement ratio and wavelet coherence fNIRS based metrics). This mental state is known to impair cognitive performance and can jeopardize flight safety. In this preliminary study, four participants were asked to perform four identical traffic patterns along with a secondary auditory task in a flight simulator and in an actual light aircraft. The two first traffic patterns were considered as the low cognitive fatigue class, whereas the two last traffic patterns were considered as the high cognitive fatigue class. As expected, the pilots missed more auditory targets in the second part than in the first part of the experiment. Classification accuracy reached 87.2% in the flight simulator condition and 87.6% in the actual flight conditions when combining the two modalities. This study demonstrates that fNIRS and EEG-based pBCIs can monitor mental states in operational and noisy environments.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Thanks to the IEEE (Institute of Electrical and Electronics Engineers). “© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
HAL Id:hal-01959452
Audience (journal):International peer-reviewed journal
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:Université de Toulouse > Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE)
Other partners > Università di Roma - SAPIENZA (ITALY)
Laboratory name:
Deposited On:12 Sep 2018 13:51

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