Empirische Inferenz Conference Paper 2005

An Auditory Paradigm for Brain-Computer Interfaces

Motivated by the particular problems involved in communicating with "locked-in" paralysed patients, we aim to develop a brain-computer 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 event-related 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.

Author(s): Hill, NJ. and Lal, TN. and Bierig, K. and Birbaumer, N. and Schölkopf, B.
Book Title: Advances in Neural Information Processing Systems 17
Journal: Advances in Neural Information Processing Systems
Pages: 569-576
Year: 2005
Month: July
Day: 0
Editors: LK Saul and Y Weiss and L Bottou
Publisher: MIT Press
Bibtex Type: Conference Paper (inproceedings)
Address: Cambridge, MA, USA
Event Name: 18th Annual Conference on Neural Information Processing Systems (NIPS 2004)
Event Place: Vancouver, BC, Canada
Digital: 0
Electronic Archiving: grant_archive
ISBN: 0-262-19534-8
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik
Links:

BibTex

@inproceedings{2794,
  title = {An Auditory Paradigm for Brain-Computer Interfaces},
  journal = {Advances in Neural Information Processing Systems},
  booktitle = {Advances in Neural Information Processing Systems 17},
  abstract = {Motivated by the particular problems involved in communicating with "locked-in" paralysed patients, we aim to develop a brain-computer 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 event-related 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.},
  pages = {569-576},
  editors = {LK Saul and Y Weiss and L Bottou},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = jul,
  year = {2005},
  slug = {2794},
  author = {Hill, NJ. and Lal, TN. and Bierig, K. and Birbaumer, N. and Sch{\"o}lkopf, B.},
  month_numeric = {7}
}