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Controlling In-Vehicle Systems with a Commercial EEG Headset: Performance and Cognitive Load
Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics. (ISOVIS)ORCID iD: 0000-0002-8585-3103
Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics. (ISOVIS)ORCID iD: 0000-0002-0519-2537
2012 (English)In: Visualization of Large and Unstructured Data Sets: Applications in Geospatial Planning, Modeling and Engineering - Proceedings of IRTG 1131 Workshop 2, Schloss Dagstuhl - Leibniz-Zentrum für Informatik , 2012Conference paper, Published paper (Refereed)
Abstract [en]

Humans have dreamed for centuries to control their surroundings solely by the power of theirminds. These aspirations have been captured by multiple science fiction creations, like theNeuromancer novel by William Gibson or the Brainstorm cinematic movie, to name just a few.Nowadays these dreams are slowly becoming reality due to a variety of brain-computer interfaces(BCI) that detect neural activation patterns and support the control of devices by brain signals.

An important field in which BCIs are being successfully integrated is the interaction withvehicular systems. In this paper we evaluate the performance of BCIs, more specifically a commercialelectroencephalographic (EEG) headset, in combination with vehicle dashboard systemsand highlight the advantages and limitations of this approach. Further, we investigate the cognitiveload that drivers experience when interacting with secondary in-vehicle devices via touchcontrols or a BCI headset. As in-vehicle systems are increasingly versatile and complex, it becomesvital to capture the level of distraction and errors that controlling these secondary systemsmight introduce to the primary driving process. Our results suggest that the control with theEEG headset introduces less distraction to the driver, probably as it allows the eyes of the driverto remain focused on the road. Still, the control of the vehicle dashboard by EEG is efficientonly for a limited number of functions, after which increasing the number of in-vehicle controlsamplifies the detection of false commands.

Place, publisher, year, edition, pages
Schloss Dagstuhl - Leibniz-Zentrum für Informatik , 2012.
Series
OpenAccess Series in Informatics (OASIcs), ISSN 2190-6807
Keywords [en]
Brain-computer interface, EEG neuroheadset, EEG control, driver cognitive workload, in-vehicle systems
National Category
Computer and Information Sciences
Research subject
Computer and Information Sciences Computer Science, Computer Science; Computer Science, Information and software visualization
Identifiers
URN: urn:nbn:se:lnu:diva-18468DOI: 10.4230/OASIcs.VLUDS.2011.113Scopus ID: 2-s2.0-84875294306OAI: oai:DiVA.org:lnu-18468DiVA, id: diva2:523533
Conference
Visualization of Large and Unstructured Data Sets - Applications in Geospatial Planning, Modeling and Engineering (VLUDS '12)
Available from: 2012-04-25 Created: 2012-04-25 Last updated: 2018-01-12Bibliographically approved

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Cernea, DanielKerren, Andreas

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