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dc.contributor.authorKlaproth, Oliver W.-
dc.contributor.authorVernaleken, Christoph-
dc.contributor.authorKrol, Laurens R.-
dc.contributor.authorHalbruegge, Marc-
dc.contributor.authorZander, Thorsten O.-
dc.contributor.authorRusswinkel, Nele-
dc.date.accessioned2020-12-08T12:10:31Z-
dc.date.available2020-12-08T12:10:31Z-
dc.date.issued2020-08-11-
dc.identifier.issn1662-4548-
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/12139-
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-11013-
dc.description.abstractThis study presents the integration of a passive brain-computer interface (pBCI) and cognitive modeling as a method to trace pilots’ perception and processing of auditory alerts and messages during operations. Missing alerts on the flight deck can result in out-of-the-loop problems that can lead to accidents. By tracing pilots’ perception and responses to alerts, cognitive assistance can be provided based on individual needs to ensure they maintain adequate situation awareness. Data from 24 participating aircrew in a simulated flight study that included multiple alerts and air traffic control messages in single pilot setup are presented. A classifier was trained to identify pilots’ neurophysiological reactions to alerts and messages from participants’ electroencephalogram (EEG). A neuroadaptive ACT-R model using EEG data was compared to a conventional normative model regarding accuracy in representing individual pilots. Results show that passive BCI can distinguish between alerts that are processed by the pilot as task-relevant or irrelevant in the cockpit based on the recorded EEG. The neuroadaptive model’s integration of this data resulted in significantly higher performance of 87% overall accuracy in representing individual pilots’ responses to alerts and messages compared to 72% accuracy of a normative model that did not consider EEG data. We conclude that neuroadaptive technology allows for implicit measurement and tracing of pilots’ perception and processing of alerts on the flight deck. Careful handling of uncertainties inherent to passive BCI and cognitive modeling shows how the representation of pilot cognitive states can be improved iteratively for providing assistance.en
dc.description.sponsorshipTU Berlin, Open-Access-Mittel – 2020en
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subject.ddc610 Medizin und Gesundheitde
dc.subject.othersituation awarenessen
dc.subject.otheraviationen
dc.subject.otherbrain-computer-interfacesen
dc.subject.otherACT-Ren
dc.subject.otherhuman-automation interactionen
dc.titleTracing Pilots’ Situation Assessment by Neuroadaptive Cognitive Modelingen
dc.typeArticleen
dc.date.updated2020-08-11T05:57:57Z-
tub.accessrights.dnbfreeen
tub.publisher.universityorinstitutionTechnische Universität Berlinen
dc.identifier.eissn1662-453X-
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.doi10.3389/fnins.2020.00795en
dcterms.bibliographicCitation.journaltitleFrontiers in Neuroscienceen
dcterms.bibliographicCitation.originalpublisherplaceLausanneen
dcterms.bibliographicCitation.volume14en
dcterms.bibliographicCitation.originalpublishernameFrontiersen
dcterms.bibliographicCitation.articlenumber795en
Appears in Collections:FG Kognitive Modellierung in dynamischen Mensch-Maschine Systemen » Publications

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