@article{2607,
  title = {Support Vector Channel Selection in BCI},
  journal = {IEEE Transactions on Biomedical Engineering},
  abstract = {Designing a Brain Computer Interface (BCI) system one can choose from a variety of
  features that may be useful for classifying brain activity during a mental task.
  For the special case of classifying EEG signals we propose the usage of the state
  of the art feature selection algorithms Recursive Feature Elimination and Zero-Norm Optimization
  which are based on the training of Support Vector Machines (SVM).
  These algorithms can provide more accurate solutions than standard filter methods for feature selection.
  We adapt the methods for the purpose of selecting EEG channels.
  For a motor imagery paradigm we show that the number of used channels can be
  reduced significantly without increasing the
  classification error.
  The resulting best channels agree well with the expected underlying cortical activity patterns during the mental tasks.
  Furthermore we show how time dependent task specific information can be visualized.},
  volume = {51},
  number = {6},
  pages = {1003-1010},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = jun,
  year = {2004},
  slug = {2607},
  author = {Lal, TN. and Schr{\"o}der, M. and Hinterberger, T. and Weston, J. and Bogdan, M. and Birbaumer, N. and Sch{\"o}lkopf, B.},
  month_numeric = {6}
}