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

Christoph Guger, Damien Coyle, Donatella Mattia, Marzia De Lucia, Leigh Hochberg, Brian L. Edlow, Betts Peters, Brandon Eddy, Chang S. Nam, Quentin Noirhomme, Brendan Z. Allison, Jitka Annen

Research output: Chapter or section in a book/report/conference proceedingChapter or section

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. Brain-computer interface technology provides a suite of approaches to assess cognition and consciousness using EEG-based tools, along with the necessary hardware and software. This paper presents results with P300, steady-state visual evoked potentials (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.
Original languageEnglish
Title of host publicationBrain-Computer Interface Research
EditorsC. Guger, B. Allison, M. Lebedev
Place of PublicationCham, Switzerland
PublisherSpringer
Pages105-125
Number of pages21
ISBN (Electronic)9783319643731
ISBN (Print)9783319643731
DOIs
Publication statusPublished - 22 Aug 2017

Publication series

NameSpringerBriefs in Electrical and Computer Engineering

Keywords

  • brain-computer interface
  • DOC assessment
  • DOC prediction
  • evoked potentials
  • P300
  • motor imagery
  • event-related desynchronization

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