Benchmarking cEEGrid and Solid Gel-Based Electrodes to Classify Inattentional Deafness in a Flight Simulator

dc.contributor.authorSomon, Bertille
dc.contributor.authorGiebeler, Yasmina
dc.contributor.authorDarmet, Ludovic
dc.contributor.authorDehais, Frédéric
dc.date.accessioned2022-11-08T08:55:19Z
dc.date.available2022-11-08T08:55:19Z
dc.date.issued2022-01-06
dc.date.updated2022-09-04T22:42:36Z
dc.description.abstractTransfer from experiments in the laboratory to real-life tasks is challenging due notably to the inability to reproduce the complexity of multitasking dynamic everyday life situations in a standardized lab condition and to the bulkiness and invasiveness of recording systems preventing participants from moving freely and disturbing the environment. In this study, we used a motion flight simulator to induce inattentional deafness to auditory alarms, a cognitive difficulty arising in complex environments. In addition, we assessed the possibility of two low-density EEG systems a solid gel-based electrode Enobio (Neuroelectrics, Barcelona, Spain) and a gel-based cEEGrid (TMSi, Oldenzaal, Netherlands) to record and classify brain activity associated with inattentional deafness (misses vs. hits to odd sounds) with a small pool of expert participants. In addition to inducing inattentional deafness (missing auditory alarms) at much higher rates than with usual lab tasks (34.7% compared to the usual 5%), we observed typical inattentional deafness-related activity in the time domain but also in the frequency and time-frequency domains with both systems. Finally, a classifier based on Riemannian Geometry principles allowed us to obtain more than 70% of single-trial classification accuracy for both mobile EEG, and up to 71.5% for the cEEGrid (TMSi, Oldenzaal, Netherlands). These results open promising avenues toward detecting cognitive failures in real-life situations, such as real flight.
dc.identifier.eissn2673-6195
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/17638
dc.identifier.urihttps://doi.org/10.14279/depositonce-16422
dc.language.isoen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.ddc152 Sinneswahrnehmung, Bewegung, Emotionen, Triebede
dc.subject.ddc153 Kognitive Prozesse, Intelligenzde
dc.subject.otherelectroencephalographyen
dc.subject.othermachine learningen
dc.subject.otherRiemannian Geometryen
dc.subject.otherflight simulatoren
dc.subject.otherinattentional deafnessen
dc.subject.otherEvent-Related Spectral Perturbationen
dc.subject.othermobile EEGen
dc.subject.otherneuroergonomicsen
dc.subject.otherERSPen
dc.titleBenchmarking cEEGrid and Solid Gel-Based Electrodes to Classify Inattentional Deafness in a Flight Simulator
dc.typeArticle
dc.type.versionpublishedVersion
dcterms.bibliographicCitation.articlenumber802486
dcterms.bibliographicCitation.doi10.3389/fnrgo.2021.802486
dcterms.bibliographicCitation.journaltitleFrontiers in Neuroergonomics
dcterms.bibliographicCitation.originalpublishernameFrontiers
dcterms.bibliographicCitation.originalpublisherplaceLausanne
dcterms.bibliographicCitation.volume2
tub.accessrights.dnbfree
tub.affiliationTechnische Universität Berlin::Fak. 5 Verkehrs- und Maschinensysteme::Inst. Psychologie und Arbeitswissenschaft::FG Kognitionspsychologie & Kognitive Ergonomie
tub.publisher.universityorinstitutionTechnische Universität Berlin
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