A Brain Computer Interface with Online Feedback based on Magnetoencephalography
Thomas Navin Lal, Michael Schroeder, N. Jeremy Hill, Hubert Preissl, Thilo Hinterberger, Juergen Mellinger, Martin Bogdan, Wolfgang Rosenstiel, Niels Birbaumer and Bernhard Schoelkopf
International Conference on Machine Learning (ICML) 2005.

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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