Classifying EEG and ECoG signals without subject training for fast BCI implementation: Comparison of nonparalyzed and completely paralyzed subjects

N. Jeremy Hill, Thomas Navin Lal, Michael Schröder, Thilo Hinterberger, Barbara Wilhelm, Femke Nijboer, Ursula Mochty, Guido Widman, Christian Elger, Bernhard Schölkopf, Andrea Kübler, Niels Birbaumer

Research output: Contribution to journalArticle

82 Citations (Scopus)

Abstract

We summarize results from a series of related studies that aim to develop a motor-imagery-based brain-computer interface using a single recording session of electroencephalogram (EEG) or electrocorticogram (ECoG) signals for each subject. We apply the same experimental and analytical methods to 11 nonparalysed subjects (eight EEG, three ECoG), and to five paralyzed subjects (four EEG, one ECoG) who had been unable to communicate for some time. While it was relatively easy to obtain classifiable signals quickly from most of the nonparalyzed subjects, it proved impossible to classify the signals obtained from the paralyzed patients by the same methods. This highlights the fact that though certain BCI paradigms may work well with healthy subjects, this does not necessarily indicate success with the target user group. We outline possible reasons for this failure to transfer.

Original languageEnglish
Article number1642764
Pages (from-to)183-186
Number of pages4
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume14
Issue number2
DOIs
Publication statusPublished - Jun 2006

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Electroencephalography
Brain-Computer Interfaces
Brain computer interface
Imagery (Psychotherapy)
Healthy Volunteers

Keywords

  • Amyotrophic Lateral Sclerosis (ALS)
  • Brain
  • Brain-computer interface (BCI)
  • Computer interface human factors
  • Electrocorticography
  • Electroencephalography
  • Locked-in state
  • Paralysis
  • Pattern classification

ASJC Scopus subject areas

  • Rehabilitation
  • Biophysics
  • Bioengineering
  • Health Professions(all)

Cite this

Classifying EEG and ECoG signals without subject training for fast BCI implementation : Comparison of nonparalyzed and completely paralyzed subjects. / Hill, N. Jeremy; Lal, Thomas Navin; Schröder, Michael; Hinterberger, Thilo; Wilhelm, Barbara; Nijboer, Femke; Mochty, Ursula; Widman, Guido; Elger, Christian; Schölkopf, Bernhard; Kübler, Andrea; Birbaumer, Niels.

In: IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 14, No. 2, 1642764, 06.2006, p. 183-186.

Research output: Contribution to journalArticle

Hill, NJ, Lal, TN, Schröder, M, Hinterberger, T, Wilhelm, B, Nijboer, F, Mochty, U, Widman, G, Elger, C, Schölkopf, B, Kübler, A & Birbaumer, N 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, vol. 14, no. 2, 1642764, pp. 183-186. https://doi.org/10.1109/TNSRE.2006.875548
Hill, N. Jeremy ; Lal, Thomas Navin ; Schröder, Michael ; Hinterberger, Thilo ; Wilhelm, Barbara ; Nijboer, Femke ; Mochty, Ursula ; Widman, Guido ; Elger, Christian ; Schölkopf, Bernhard ; Kübler, Andrea ; Birbaumer, Niels. / Classifying EEG and ECoG signals without subject training for fast BCI implementation : Comparison of nonparalyzed and completely paralyzed subjects. In: IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2006 ; Vol. 14, No. 2. pp. 183-186.
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