Skip to main content
SpringerLink
Log in

Brain-Computer Interfaces and Diagnosis

  • Chapter
  • First Online:
Brain-Computer-Interfaces in their ethical, social and cultural contexts

Part of the book series: The International Library of Ethics, Law and Technology ((ELTE,volume 12))

Abstract

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).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Bardin, J.C., J.J. Fins, et al. 2011. Dissociations between behavioural and functional magnetic resonance imaging-based evaluations of cognitive function after brain injury. Brain 134(Pt 3): 769–782.

    Article  Google Scholar 

  • Bianchi, L., S. Sami, et al. 2010. Which physiological components are more suitable for visual ERP based brain-computer interface? A preliminary MEG/EEG study. Brain Topography 23(2): 180–185.

    Article  Google Scholar 

  • Birbaumer, N. 1997. Slow cortical potentials: Their origin, meaning, and clinical use. In Brain and behavior past, present, and future, ed. G.J.M. van Boxtel and K. Böcker, 25–39. Tilburg: Tilburg University Press.

    Google Scholar 

  • Birbaumer, N. 2006. Breaking the silence: Brain-computer interfaces (BCI) for communication and motor control. Psychophysiology 43(6): 517–532.

    Article  Google Scholar 

  • Birbaumer, N., N. Ghanayim, et al. 1999. A spelling device for the paralysed. Nature 398(6725): 297–298.

    Article  Google Scholar 

  • Birbaumer, N., A. Kübler, et al. 2000. The thought translation device (TTD) for completely paralyzed patients. IEEE Transactions on Rehabilitation Engineering 8(2): 190–193.

    Article  Google Scholar 

  • Bruno, M.A., S. Majerus, et al. 2012. Functional neuroanatomy underlying the clinical subcategorization of minimally conscious state patients. Journal of Neurology 259(6):1087–98.

    Google Scholar 

  • Chatelle, C., S. Chennu, et al. 2012. Brain-computer interfacing in disorders of consciousness. Brain Injury 26(12):1510–22.

    Article  Google Scholar 

  • Cruse, D., S. Chennu, et al. 2011. Bedside detection of awareness in the vegetative state. The Lancet 378(9809): 2088–2094.

    Article  Google Scholar 

  • Cruse, D., S. Chennu, et al. 2012. The relationship between aetiology and covert cognition in the minimally-conscious state. Neurology 78(11): 816–822.

    Article  Google Scholar 

  • Cruse, D., S. Chennu, et al. 2013. Response to: Reanalysis of ‘Bedside detection of awareness in the vegetative state: A cohort study’. The Lancet 381(9863): 291–292.

    Article  Google Scholar 

  • Elbert, T., B. Rockstroh, et al. 1980. Biofeedback of slow cortical potentials. I. Electroencephalography and Clinical Neurophysiology 48(3): 293–301.

    Article  Google Scholar 

  • Giacino, J., S. Ashwal, et al. 2002. The minimally conscious state: Definition and diagnostic criteria. Neurology 58(3): 349–353.

    Article  Google Scholar 

  • Goldfine, A.M., J.D. Victor, et al. 2011. Determination of awareness in patients with severe brain injury using EEG power spectral analysis. Clinical Neurophysiology 122(11):2157–68.

    Article  Google Scholar 

  • Goldfine, A.M., J.C. Bardin, et al. 2013. Reanalysis of Bedside detection of awareness in the vegetative state: A cohort study. The Lancet 381(9863): 289–291.

    Article  Google Scholar 

  • Gray, M., A.H. Kemp, et al. 2003. Cortical neurophysiology of anticipatory anxiety: An investigation utilizing steady state probe topography (SSPT). NeuroImage 20(2): 975–986.

    Article  Google Scholar 

  • Guger, C., G. Edlinger, et al. 2003. How many people are able to operate an EEG-based brain-computer interface (BCI)? IEEE Transactions on Neural Systems and Rehabilitation Engineering 11(2): 145–147.

    Article  Google Scholar 

  • Guger, C., S. Daban, et al. 2009. How many people are able to control a P300-based brain-computer interface (BCI)? Neuroscience Letters 462(1): 94–98.

    Article  Google Scholar 

  • Halder, S., M. Rea, et al. 2010. An auditory oddball brain-computer interface for binary choices. Clinical Neurophysiology 121(4): 516–523.

    Article  Google Scholar 

  • Hill, N.J., T.N. Lal, et al. 2006. Classifying EEG and ECoG signals without subject training for fast BCI implementation: Comparison of nonparalyzed and completely paralyzed subjects. IEEE Transactions on Neural Systems and Rehabilitation Engineering 14(2): 183–186.

    Article  Google Scholar 

  • Hoffmann, U., J.M. Vesin, et al. 2008. An efficient P300-based brain-computer interface for disabled subjects. Journal of Neuroscience Methods 167(1): 115–125.

    Article  Google Scholar 

  • Kaufmann, T., E. Hammer, et al. 2011. ERPs contributing to classification in the P300 BCI. In Proceedings of the 5th international brain-computer interface conference. Graz: Graz University of Technology.

    Google Scholar 

  • Kübler, A., B. Kotchoubey, et al. 1999. The thought translation device: A neurophysiological approach to communication in total motor paralysis. Experimental Brain Research 124(2): 223–232.

    Article  Google Scholar 

  • Kübler, A., V.K. Mushahwar, et al. 2006. BCI meeting 2005–workshop on clinical issues and applications. IEEE Transactions on Neural Systems and Rehabilitation Engineering 14(2): 131–134.

    Article  Google Scholar 

  • Kübler, A., A. Furdea, et al. 2009. A brain-computer interface controlled auditory event-related potential (p300) spelling system for locked-in patients. Annals of the New York Academy of Sciences 1157: 90–100.

    Article  Google Scholar 

  • Laureys, S., G.G. Celesia, et al. 2010. Unresponsive wakefulness syndrome: A new name for the vegetative state or apallic syndrome. BMC Medicine 8: 68.

    Article  Google Scholar 

  • Lesenfants, D., N. Partoune, et al. 2011. Design of a novel covert SSVEP-based BCI. In Proceedings of the 5th international brain-computer interface conference. Graz: University of Technology Publishing House.

    Google Scholar 

  • Lule, D., Q. Noirhomme, et al. 2013. Probing command following in patients with disorders of consciousness using a brain-computer interface. Clinical Neurophysiology 124: 101–106.

    Article  Google Scholar 

  • Monti, M.M., M.R. Coleman, et al. 2009. Executive functions in the absence of behavior: Functional imaging of the minimally conscious state. Progress in Brain Research 177: 249–260.

    Article  Google Scholar 

  • Monti, M.M., A. Vanhaudenhuyse, et al. 2010. Willful modulation of brain activity in disorders of consciousness. New England Journal of Medicine 362(7): 579–589.

    Article  Google Scholar 

  • Nakase-Richardson, R., S.A. Yablon, et al. 2009. Emergence from minimally conscious state: Insights from evaluation of posttraumatic confusion. Neurology 73(14): 1120–1126.

    Article  Google Scholar 

  • Neuper, C., R. Scherer, et al. 2005. Imagery of motor actions: Differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG. Brain Research. Cognitive Brain Research 25(3): 668–677.

    Article  Google Scholar 

  • Owen, A.M., M.R. Coleman, et al. 2006. Detecting awareness in the vegetative state. Science 313(5792): 1402.

    Article  Google Scholar 

  • Parini, S., L. Maggi, et al. 2009. A robust and self-paced BCI system based on a four class SSVEP paradigm: Algorithms and protocols for a high-transfer-rate direct brain communication. Computational Intelligence and Neuroscience 864564 doi:10.1155/2009/864564.

  • Perlstein, W.M., M.A. Cole, et al. 2003. Steady-state visual evoked potentials reveal frontally-mediated working memory activity in humans. Neuroscience Letters 342(3): 191–195.

    Article  Google Scholar 

  • Pfurtscheller, G., and F.H. Lopes da Silva. 1999. Event-related EEG/MEG synchronization and desynchronization: Basic principles. Clinical Neurophysiology 110(11): 1842–1857.

    Article  Google Scholar 

  • Pfurtscheller, G., C. Neuper, et al. 1997. EEG-based discrimination between imagination of right and left hand movement. Electroencephalography and Clinical Neurophysiology 103(6): 642–651.

    Article  Google Scholar 

  • Pham, M., T. Hinterberger, et al. 2005. An auditory brain-computer interface based on the self-regulation of slow cortical potentials. Neurorehabilitation and Neural Repair 19(3): 206–218.

    Article  Google Scholar 

  • Plum, F., and J. Posner. 1966. The diagnosis of stupor and coma. Philadelphia: F.A. Davis & Co.

    Google Scholar 

  • Regan, D. 1966. Some characteristics of average steady-state and transient responses evoked by modulated light. Electroencephalography and Clinical Neurophysiology 20(3): 238–248.

    Article  Google Scholar 

  • Regan, D. 1989. Human brain electrophysiology: Evoked potentials and evoked magnetic fields in science and medicine. New York: Elsevier.

    Google Scholar 

  • Schnakers, C., F. Perrin, et al. 2008. Voluntary brain processing in disorders of consciousness. Neurology 71: 1614–1620.

    Article  Google Scholar 

  • Schnakers, C., F. Perrin, et al. 2009. Detecting consciousness in a total locked-in syndrome: An active event-related paradigm. Neurocase 4: 1–7.

    Google Scholar 

  • Vialatte, F.B., M. Maurice, et al. 2010. Steady-state visually evoked potentials: Focus on essential paradigms and future perspectives. Progress in Neurobiology 90(4): 418–438.

    Article  Google Scholar 

Download references

Acknowledgments

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.

Author information

Authors and Affiliations

  1. Coma Science Group, Cyclotron Research Centre and Neurology Department, University of Liege, Liege, Belgium

    Camille Chatelle, Steven Laureys & Quentin Noirhomme

Authors
  1. Camille Chatelle
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Steven Laureys
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Quentin Noirhomme
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Quentin Noirhomme .

Editor information

Editors and Affiliations

  1. Department of Philosophy, University of Dresden, Dresden, Germany

    Gerd Grübler

  2. Department of Philosophy, University of Mainz, Mainz, Germany

    Elisabeth Hildt

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Chatelle, C., Laureys, S., Noirhomme, Q. (2014). Brain-Computer Interfaces and Diagnosis. In: Grübler, G., Hildt, E. (eds) Brain-Computer-Interfaces in their ethical, social and cultural contexts. The International Library of Ethics, Law and Technology, vol 12. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8996-7_3

Download citation

Publish with us

Policies and ethics

Search

Navigation