@article {2148,
	title = {A Brain Computer Interface with Online Feedback based on Magnetoencephalography.},
	year = {2005},
	month = {08/2005},
	abstract = {<p><span style="font-family: arial, sans-serif; font-size: 12px; line-height: 16px;">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 {\textquotedblleft}proof of concept{\textquotedblright}.</span></p>},
	keywords = {Brain Computer Interfaces, User Modelling for Computer Human Interaction},
	doi = {10.1145/1102351.1102410},
	url = {http://www.researchgate.net/publication/221346004_A_brain_computer_interface_with_online_feedback_based_on_magnetoencephalography},
	author = {Lal, T.N and Schroeder, Michael and Jeremy Jeremy Hill and Preissl, Hubert and Hinterberger, T. and Mellinger, J{\"u}rgen and Bogdan, Martin and Rosenstiel, W. and Niels Birbaumer and Schoelkopf, Bernhard}
}
