A brain computer interface with online feedback based on magnetoencephalography

Authors:
Thomas Navin Lal;Michael Schröder;N. Jeremy Hill;Hubert Preissl;Thilo Hinterberger;Jürgen Mellinger;Martin Bogdan;Wolfgang Rosenstiel;Thomas Hofmann;Niels Birbaumer;Bernhard Schölkopf
Affiliations:
Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany;Eberhard Karls University, Tübingen, Germany;Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany;Eberhard Karls University, Tübingen, Germany;Eberhard Karls University, Tübingen, Germany;Eberhard Karls University, Tübingen, Germany;Eberhard Karls University, Tübingen, Germany;Eberhard Karls University, Tübingen, Germany;Technical University of Darmstadt, Darmstadt, Germany;Eberhard Karls University, Tübingen, Germany;Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany
Venue:
ICML '05 Proceedings of the 22nd international conference on Machine learning
Year:
2005

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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 signal-to-noise ratio, is likely to succeed. We apply RCE 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 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 MEG-based BCI and is therefore a "proof of concept".