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

Content Provider	IEEE Xplore Digital Library
Author	Hill, N.J. Lal, T.N. Schroder, M. Hinterberger, T. Wilhelm, B. Nijboer, F. Mochty, U. Widman, G. Elger, C. Scholkopf, B. Kubler, A. Birbaumer, N.
Copyright Year	2001
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.
Sponsorship	IEEE Engineering in Medicine and Biology Society
Page Count	4
File Size	121590
Starting Page	183
Ending Page	186
File Format	PDF
ISSN	15344320
Volume Number	14
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electroencephalography Support vector machines Support vector machine classification Biomedical engineering Psychology Epilepsy Implants Computer interfaces Human factors Pattern classification pattern classification Amyotrophic Lateral Sclerosis (ALS) brain brain–computer interface (BCI) computer interface human factors electrocorticography electroencephalography locked-in state paralysis
Content Type	Text
Resource Type	Article
Subject	Neuroscience Rehabilitation Internal Medicine Biomedical Engineering Computer Science Applications

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