A brain computer interface with online feedback based on magnetoencephalography

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 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". Appearing in Proceedings of the 22nd International Conference on Machine Learning, Bonn, Germany, 2005. Copyright 200...

• Type: Conference
• Year: 2005
• Where: ICML
• Authors: Bernhard Schölkopf, Hubert Preißl, Jürgen Mellinger, Martin Bogdan, Michael Schröder 0002, N. Jeremy Hill, Niels Birbaumer, Thilo Hinterberger, Thomas Hofmann, Thomas Navin Lal, Wolfgang Rosenstiel
• Keywords: BCI Approach, ICML 2005, Machine Learning, Motor Imagery Task, Online Meg-based Bci