@inproceedings{6434,
  title = {MLSP Competition, 2010: Description of first place method},
  journal = {Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010)},
  abstract = {Our winning approach to the 2010 MLSP Competition is based on a generative method for P300-based BCI decoding, successfully applied to visual spellers. Here, generative has a double meaning. On the one hand, we work with a probability density model of the data given the target/non target labeling, as opposed to discriminative (e.g. SVM-based) methods. On the other hand, the natural consequence of this approach is a decoding based on comparing the observation to templates generated from the data.},
  pages = {112-113},
  publisher = {IEEE},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Piscataway, NJ, USA},
  month = sep,
  year = {2010},
  author = {Leiva, JM. and Martens, SMM.},
  doi = {10.1109/MLSP.2010.5589243},
  month_numeric = {9}
}