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Trends in BCI Research I: Brain-Computer Interfaces for Assessment of Patients with Locked-in Syndrome or Disorders of Consciousness

Chapter
Part of the SpringerBriefs in Electrical and Computer Engineering book series (BRIEFSELECTRIC)

Abstract

Patients diagnosed with complete locked in syndrome (CLIS) or a disorder of consciousness (DOC) have no reliable control of voluntary movements. Hence, assessing their cognitive functions and cognitive awareness can be challenging. The “gold standard” for such assessments relies on behavioral responses, and recent work using different neuroimaging methods has shown that behavioral diagnoses may underestimate patients’ capabilities. Thus, there is a pressing need for new methods that go beyond behavioral approaches and can help patients even if they are not able to produce any behavioral response. In one of the most prominent trends in brain-computer interface (BCI) research, many groups have been using BCI technology to provide a suite of approaches to assess cognition and consciousness using EEG-based tools. This paper presents results with P300, steady-state visual evoked potential (SSVEP) and motor imagery BCIs and other approaches with different target patients in several different real-world settings. Results confirm that EEG-based assessment can reveal details about patients’ remaining capabilities that can both change and extend diagnoses based on behavioral measures. The results can already be used in clinical practice to help physicians, patients, and families develop a more detailed and accurate assessments, and provide hope for further technical and methodological improvements through future research.

Keywords

Brain-computer interface DOC assessment DOC prediction Evoked potentials P300 Motor imagery Event-related desynchronization 

Notes

Acknowledgements

The work of g.tec was supported by the H2020 grant ComaWare and ComAlert (project number E! 9361 Com-Alert). Q. Noirhomme has received funding from the European Community’s Seventh Framework Program under grant agreement n° 602450 (IMAGEMEND). Research at OHSU was supported by NIH grant R01DC014294 and NIDILRR grant 90RE5017. Research at MGH was supported by NIH grant K23NS094538 and the American Academy of Neurology/American Brain Foundation. Research at NCSU was supported by NSF grant IIS1421948. Marzia De Lucia’s research at Lausanne University Hospital is supported by the “EUREKA-Eurostars” grant (project number E! 9361 Com-Alert). The work was partially supported by the Italian Ministry of Healthcare and the French Speaking Community Concerted Research Action (ARC-06/11-340). This paper reflects only the authors’ view and the funding sources are not liable for any use that may be made of the information contained therein.

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Copyright information

© The Author(s) 2017

Authors and Affiliations

  1. 1.g.tec Guger Technologies OGGrazAustria
  2. 2.Faculty of Computing and Engineering, School of Computing and Intelligent SystemsMagee Campus, Ulster UniversityDerryUK
  3. 3.Neuroelectrical Imaging and BCI LabFondazione Santa Lucia, IRCCSRomeItaly
  4. 4.Laboratoire de Recherche en Neuroimagerie (LREN), Department of Clinical NeurosciencesLausanne University Hospital (CHUV) and University of LausanneLausanneSwitzerland
  5. 5.Department of NeurologyMassachusetts General HospitalBostonUSA
  6. 6.Oregon Health & Science UniversityPortlandUSA
  7. 7.Brain-Computer Interface (BCI) LabNorth Carolina State UniversityRaleighUSA
  8. 8.Coma Science Group, GIGA Research and Neurology DepartmentUniversity Hospital of LiègeLiègeBelgium

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