License: Creative Commons Attribution-NoDerivs 3.0 Unported license (CC BY-ND 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.VLUDS.2011.113
URN: urn:nbn:de:0030-drops-37459
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2012/3745/
Go to the corresponding OASIcs Volume Portal

Cernea, Daniel ; Olech, Peter-Scott ; Ebert, Achim ; Kerren, Andreas

Controlling In-Vehicle Systems with a Commercial EEG Headset: Performance and Cognitive Load

pdf-format:
10.pdf (0.6 MB)

Abstract

Humans have dreamed for centuries to control their surroundings solely by the power of their minds. These aspirations have been captured by multiple science fiction creations, such as the Neuromancer 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 with vehicular systems. In this paper, we evaluate the performance of BCIs, more specifically a commercial electroencephalographic (EEG) headset in combination with vehicle dashboard systems, and highlight the advantages and limitations of this approach. Further, we investigate the cognitive load that drivers experience when interacting with secondary in-vehicle devices via touch controls or a BCI headset. As in-vehicle systems are increasingly versatile and complex, it becomes vital to capture the level of distraction and errors that controlling these secondary systems might introduce to the primary driving process. Our results suggest that the control with the EEG headset introduces less distraction to the driver, probably as it allows the eyes of the driver to remain focused on the road. Still, the control of the vehicle dashboard by EEG is efficient only for a limited number of functions, after which increasing the number of in-vehicle controls amplifies the detection of false commands.

BibTeX - Entry

@InProceedings{cernea_et_al:OASIcs:2012:3745,
  author =	{Daniel Cernea and Peter-Scott Olech and Achim Ebert and Andreas Kerren},
  title =	{{Controlling In-Vehicle Systems with a Commercial EEG Headset: Performance and Cognitive Load}},
  booktitle =	{Visualization of Large and Unstructured Data Sets: Applications in Geospatial Planning, Modeling and Engineering - Proceedings of IRTG 1131 Workshop 2011},
  pages =	{113--122},
  series =	{OpenAccess Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-46-0},
  ISSN =	{2190-6807},
  year =	{2012},
  volume =	{27},
  editor =	{Christoph Garth and Ariane Middel and Hans Hagen},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2012/3745},
  URN =		{urn:nbn:de:0030-drops-37459},
  doi =		{10.4230/OASIcs.VLUDS.2011.113},
  annote =	{Keywords: Brain-computer interface, EEG neuroheadset, EEG control, driver cog- nitive workload, in-vehicle systems.}
}

Keywords: Brain-computer interface, EEG neuroheadset, EEG control, driver cog- nitive workload, in-vehicle systems.
Collection: Visualization of Large and Unstructured Data Sets: Applications in Geospatial Planning, Modeling and Engineering - Proceedings of IRTG 1131 Workshop 2011
Issue Date: 2012
Date of publication: 16.10.2012

DROPS-Home | Fulltext Search | Imprint | Privacy Published by LZI