@inproceedings{3482,
  title = {A Brain Computer Interface with Online Feedback based on Magnetoencephalography},
  booktitle = {Proceedings of the 22nd International Conference on Machine Learning},
  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”.},
  pages = {465-472},
  editors = {L De Raedt and S Wrobel},
  publisher = {ACM},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {New York, NY, USA},
  year = {2005},
  slug = {3482},
  author = {Lal, TN. and Schr{\"o}der, M. and Hill, J. and Preissl, H. and Hinterberger, T. and Mellinger, J. and Bogdan, M. and Rosenstiel, W. and Hofmann, T. and Birbaumer, N. and Sch{\"o}lkopf, B.}
}