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A pBCI to Predict Attentional Error Before it Happens in Real Flight Conditions

Dehais, Frédéric and Rida, Imad and Roy, Raphaëlle N. and Iversen, John and Mullen, Tim and Callan, Daniel E. A pBCI to Predict Attentional Error Before it Happens in Real Flight Conditions. (2019) In: IEEE International Conference on Systems, Man, and Cybernetics - IEEE SMC BMI, 6 October 2019 - 9 October 2019 (Bari, Italy).

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Official URL: https://doi.org/10.1109/SMC.2019.8914010

Abstract

Accident analyses have revealed that pilots can fail to process auditory stimuli such as alarms, a phenomenon known as inattentional deafness. The motivation of this research is to develop a passive brain computer interface that can predict the occurence of this critical phenomenon during real flight conditions. Ten volunteers, equipped with a dry-EEG system, had to fly a challenging flight scenario while responding to auditory alarms by button press. The behavioral results disclosed that the pilots missed 36% of the auditory alarms. ERP analyses confirm that this phenomenon affects auditory processing at an early (N100) and late (P300) stages as the consequence of a potential attentional bottleneck mechanism. Inter-subject classification was carried out over frequency features extracted three second epochs before the alarms’ onset using sparse representation for classification (SRC), sparse and dense representation (SDR) and more conventional approach such as linear discriminant analysis (LDA), shrinkage LDA and nearest neighbor (1-NN). In the best case, SRC and SDR gave respectively a performance of 66.9% and 65.4% of correct mean classification rate to predict the occurrence of inattentional deafness, outperforming LDA (60.6%), sLDA (60%) and 1- NN (59.6%). These results open promising perspectives for the implementation of neuroadaptive automation with as ultimate goal to enhance alarm stimulation delivery so that it is perceived and acted upon.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Thanks to the IEEE (Institute of Electrical and Electronics Engineers). This paper is available at :https://ieeexplore.ieee.org/document/8914010 “© 2019 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.
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 > The Center for Information and Neural Networks - CiNet (JAPAN)
Other partners > National Institute of Information and Communications Technology - NICT (JAPAN)
Other partners > University of California - UC San Diego (USA)
Laboratory name:
Funders:
AID - AXA Research Fund - ANITI
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Deposited By: Frédéric Dehais
Deposited On:04 Nov 2019 14:50

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