Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-11013
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Main Title: Tracing Pilots’ Situation Assessment by Neuroadaptive Cognitive Modeling
Author(s): Klaproth, Oliver W.
Vernaleken, Christoph
Krol, Laurens R.
Halbruegge, Marc
Zander, Thorsten O.
Russwinkel, Nele
Type: Article
Language Code: en
Abstract: This 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.
URI: https://depositonce.tu-berlin.de/handle/11303/12139
http://dx.doi.org/10.14279/depositonce-11013
Issue Date: 11-Aug-2020
Date Available: 8-Dec-2020
DDC Class: 610 Medizin und Gesundheit
Subject(s): situation awareness
aviation
brain-computer-interfaces
ACT-R
human-automation interaction
Sponsor/Funder: TU Berlin, Open-Access-Mittel – 2020
License: https://creativecommons.org/licenses/by/4.0/
Journal Title: Frontiers in Neuroscience
Publisher: Frontiers
Publisher Place: Lausanne
Volume: 14
Article Number: 795
Publisher DOI: 10.3389/fnins.2020.00795
EISSN: 1662-453X
ISSN: 1662-4548
Appears in Collections:FG Kognitive Modellierung in dynamischen Mensch-Maschine Systemen » Publications

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