%0 Journal Article %D 2005 %T A Brain Computer Interface with Online Feedback based on Magnetoencephalography. %A Lal, T.N %A Schroeder, Michael %A Jeremy Jeremy Hill %A Preissl, Hubert %A Hinterberger, T. %A Mellinger, Jürgen %A Bogdan, Martin %A Rosenstiel, W. %A Niels Birbaumer %A Schoelkopf, Bernhard %K Brain Computer Interfaces %K User Modelling for Computer Human Interaction %X

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”.

%8 08/2005 %G eng %U http://www.researchgate.net/publication/221346004_A_brain_computer_interface_with_online_feedback_based_on_magnetoencephalography %R 10.1145/1102351.1102410