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Empirical Inference Conference Paper 2005

A Brain Computer Interface with Online Feedback based on Magnetoencephalography

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Empirical Inference
Bernhard Schölkopf
  • Director
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Coordinative Intelligence
Thomas Hofmann
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Empirical Inference
Jeremy Hill

The aim of this paper is to show that machine learning techniques can be used to derive a classifying function for human brain signal data measured by magnetoencephalography (MEG), for the use in a brain computer interface (BCI). This is especially helpful for evaluating quickly whether a BCI approach based on electroencephalography, on which training may be slower due to lower signalto- noise ratio, is likely to succeed. We apply recursive channel elimination and regularized SVMs to the experimental data of ten healthy subjects performing a motor imagery task. Four subjects were able to use a trained classifier together with a decision tree interface to write a short name. Further analysis gives evidence that the proposed imagination task is suboptimal for the possible extension to a multiclass interface. To the best of our knowledge this paper is the first working online BCI based on MEG recordings and is therefore a “proof of concept”.

Author(s): Lal, TN. and Schröder, M. and Hill, J. and Preissl, H. and Hinterberger, T. and Mellinger, J. and Bogdan, M. and Rosenstiel, W. and Hofmann, T. and Birbaumer, N. and Schölkopf, B.
Book Title: Proceedings of the 22nd International Conference on Machine Learning
Pages: 465-472
Year: 2005
Day: 0
Editors: L De Raedt and S Wrobel
Publisher: ACM
Bibtex Type: Conference Paper (inproceedings)
Address: New York, NY, USA
Event Name: ICML 2005
Event Place: Bonn, Germany
Electronic Archiving: grant_archive
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik
Links:

BibTex 

@inproceedings{3482,
  title = {A Brain Computer Interface with Online Feedback based on Magnetoencephalography},
  booktitle = {Proceedings of the 22nd International Conference on Machine Learning},
  abstract = {The aim of this paper is to show that machine learning techniques can be used to derive a classifying function for human brain signal data measured by magnetoencephalography (MEG), for the use in a brain computer interface (BCI). This is especially helpful for evaluating quickly whether a BCI approach based on electroencephalography, on which training may be slower due to lower signalto-
  noise ratio, is likely to succeed. We apply recursive channel elimination and regularized SVMs to the experimental data of ten healthy subjects performing a motor imagery task. Four subjects were able to use a
  trained classifier together with a decision tree interface to write a short name. Further analysis gives evidence that the proposed imagination task is suboptimal for the possible extension to a multiclass interface. To the best
  of our knowledge this paper is the first working online BCI based on MEG recordings and is therefore a “proof of concept”.},
  pages = {465-472},
  editors = {L De Raedt and S Wrobel},
  publisher = {ACM},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {New York, NY, USA},
  year = {2005},
  slug = {3482},
  author = {Lal, TN. and Schr{\"o}der, M. and Hill, J. and Preissl, H. and Hinterberger, T. and Mellinger, J. and Bogdan, M. and Rosenstiel, W. and Hofmann, T. and Birbaumer, N. and Sch{\"o}lkopf, B.}
}