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  A Brain Computer Interface with Online Feedback based on Magnetoencephalography

Lal, T., Schröder, M., Hill, J., Preissl, H., Hinterberger, T., Meilinger, J., et al. (2005). A Brain Computer Interface with Online Feedback based on Magnetoencephalography. In S. Dzeroski, L. de Raedt, & S. Wrobel (Eds.), ICML '05: 22nd international conference on Machine learning (pp. 465-472). New York, NY, USA: ACM Press.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D6D3-7 Version Permalink: https://hdl.handle.net/21.11116/0000-0005-0E10-A
Genre: Conference Paper

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https://dl.acm.org/citation.cfm?doid=1102351.1102410 (Publisher version)
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 Creators:
Lal, TN1, 2, Author           
Schröder, M, Author           
Hill, J1, 2, Author           
Preissl, H, Author           
Hinterberger, T, Author
Meilinger, J, Author
Bogdan, M, Author
Rosenstiel, W, Author
Hofmann, T, Author           
Birbaumer, N, Author
Schölkopf, B1, 2, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 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”.

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 Dates: 2005-08
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 3482
DOI: 10.1145/1102351.1102410
 Degree: -

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Title: 22nd International Conference on Machine Learning (ICML 2005)
Place of Event: Bonn, Germany
Start-/End Date: 2008-08-07 - 2008-08-11

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Title: ICML '05: 22nd international conference on Machine learning
Source Genre: Proceedings
 Creator(s):
Dzeroski, S, Editor
de Raedt, L, Editor
Wrobel, S, Editor
Affiliations:
-
Publ. Info: New York, NY, USA : ACM Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 465 - 472 Identifier: ISBN: 1-59593-180-5