Brain-Computer Interfaces and Diagnosis

  • Camille Chatelle
  • Steven Laureys
  • Quentin NoirhommeEmail author
Part of the The International Library of Ethics, Law and Technology book series (ELTE, volume 12)


Recent electrophysiological and neuroimaging studies showed the possibility to detect command-specific changes in electroencephalography (EEG) or functional magnetic resonance imaging (fMRI) signals independent of any motor pathway. These techniques could help in the improvement of the diagnosis in patients with disorders of consciousness (DOC; often suffering severe motor disabilities), providing motor-independent evidence of command following and even, in some cases, permitting communication. We here review the first results obtained by BCI-like applications in patients with DOC and discuss the challenges facing BCI research. One application which has been rarely thought for BCI is the use of these systems as diagnosis tools. Indeed, a BCI may help to detect signs of consciousness and communication in patients lacking the ability to move or speak. In this chapter, we will present the first applications of BCI approaches to detect signs of consciousness in patients with DOC. We will then highlight the main challenges that will need to be overcome in future research and some clues from studies in healthy controls and patients with locked-in syndrome (LIS).


Motor Imagery Mental Imagery Minimally Conscious State Imagery Task Minimally Conscious State Patient 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We gratefully acknowledge Audrey Vanhaudenhuyse and Martin Monti for their collaboration on the patients’ data information. This work was supported by the Belgian Fonds National de la Recherche Scientifique (FNRS), European Commission, Mind Science Foundation, James McDonnell Foundation, French Speaking Community Concerted Research Action, Fondation Léon Fredericq, Public Utility Foundation “Université Européenne du Travail” and “Fondazione Europea di Ricerca Biomedica”. This work is supported by the European ICT Programme Projects FP7-247919 DECODER. The text reflects solely the views of its authors. The European Commission is not liable for any use that may be made of the information contained therein.


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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Camille Chatelle
    • 1
  • Steven Laureys
    • 1
  • Quentin Noirhomme
    • 1
    Email author
  1. 1.Coma Science Group, Cyclotron Research Centre and Neurology DepartmentUniversity of LiegeLiegeBelgium

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