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Conference Paper

A Brain Computer Interface with Online Feedback based on Magnetoencephalography

MPS-Authors
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Lal,  TN
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Hill,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

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.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-D6D3-7
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”.