nips nips2004 nips2004-20 nips2004-20-reference knowledge-graph by maker-knowledge-mining
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Author: N. J. Hill, Thomas N. Lal, Karin Bierig, Niels Birbaumer, Bernhard Schölkopf
Abstract: Motivated by the particular problems involved in communicating with “locked-in” paralysed patients, we aim to develop a braincomputer interface that uses auditory stimuli. We describe a paradigm that allows a user to make a binary decision by focusing attention on one of two concurrent auditory stimulus sequences. Using Support Vector Machine classification and Recursive Channel Elimination on the independent components of averaged eventrelated potentials, we show that an untrained user’s EEG data can be classified with an encouragingly high level of accuracy. This suggests that it is possible for users to modulate EEG signals in a single trial by the conscious direction of attention, well enough to be useful in BCI. 1
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