@poster{2539,
  title = {EEG Channel Selection for Brain Computer Interface Systems Based on Support Vector Methods},
  abstract = {A Brain Computer Interface (BCI) system allows the direct interpretation of brain activity
  patterns (e.g. EEG signals) by a computer. Typical BCI applications comprise spelling aids
  or environmental control systems supporting paralyzed patients that have lost motor control
  completely.
  The design of an EEG based BCI system requires good answers for the problem of selecting
  useful features during the performance of a mental task as well as for the problem of classifying
  these features.
  For the special case of choosing appropriate EEG channels from several available channels,
  we propose the application of variants of the Support Vector Machine (SVM) for both
  problems. Although these algorithms do not rely on prior knowledge they can provide more
  accurate solutions than standard lter methods [1] for feature selection which usually incorporate
  prior knowledge about neural activity patterns during the performed mental tasks.
  For judging the importance of features we introduce a new relevance measure and apply it
  to EEG channels. Although we base the relevance measure for this purpose on the previously
  introduced algorithms, it does in general not depend on specic algorithms but can be derived
  using arbitrary combinations of feature selectors and 
  classifiers.},
  volume = {7},
  pages = {50},
  editors = {B{\"u}lthoff, H.H., H.A. Mallot, R. Ulrich and F.A. Wichmann},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = feb,
  year = {2004},
  slug = {2539},
  author = {Schr{\"o}der, M. and Lal, TN. and Bogdan, M. and Sch{\"o}lkopf, B.},
  month_numeric = {2}
}