@inproceedings{5967,
  title = {Implicit Wiener Series Analysis of Epileptic Seizure Recordings},
  journal = {Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2009)},
  booktitle = {EMBC 2009},
  abstract = {Implicit Wiener series are a powerful tool to build
  Volterra representations of time series with any degree of nonlinearity.
  A natural question is then whether higher order
  representations yield more useful models. In this work we
  shall study this question for ECoG data channel relationships
  in epileptic seizure recordings, considering whether quadratic
  representations yield more accurate classifiers than linear ones.
  To do so we first show how to derive statistical information on
  the Volterra coefficient distribution and how to construct seizure
  classification patterns over that information. As our results
  illustrate, a quadratic model seems to provide no advantages
  over a linear one. Nevertheless, we shall also show that the
  interpretability of the implicit Wiener series provides insights
  into the inter-channel relationships of the recordings.},
  pages = {5304-5307},
  editors = {Y Kim and B He and G Worrell and X Pan},
  publisher = {IEEE Service Center},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Piscataway, NJ, USA},
  month = sep,
  year = {2009},
  slug = {5967},
  author = {Barbero, A. and Franz, MO. and Drongelen, WV. and Dorronsoro, JR. and Sch{\"o}lkopf, B. and Grosse-Wentrup, M.},
  month_numeric = {9}
}