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

TitleClassifying EEG and ECoG signals without subject training for fast BCI implementation: comparison of nonparalyzed and completely paralyzed subjects.
Publication TypeJournal Article
Year of Publication2006
AuthorsHill, N Jeremy, Lal, TN, Schröder, M, Hinterberger, T, Wilhelm, B, Nijboer, F, Mochty, U, Widman, G, Elger, C, Schölkopf, B, Kübler, A, Birbaumer, N
JournalIEEE Trans Neural Syst Rehabil Eng
Volume14
Issue2
Pagination183-6
Date Published06/2006
ISSN1534-4320
KeywordsAlgorithms, Artificial Intelligence, Cluster Analysis, Computer User Training, Electroencephalography, Evoked Potentials, Female, Humans, Imagination, Male, Middle Aged, Paralysis, Pattern Recognition, Automated, User-Computer Interface
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.
DOI10.1109/TNSRE.2006.875548
Alternate JournalIEEE Trans Neural Syst Rehabil Eng
PubMed ID16792289
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