@article{BialonskiAnsmannKantz2015,
  author    = {Bialonski, Stephan and Ansmann, Gerrit and Kantz, Holger},
  title     = {Data-driven prediction and prevention of extreme events in a spatially extended excitable system},
  series = {Physical Review E},
  volume    = {92},
  journal   = {Physical Review E},
  number    = {4},
  issn      = {2470-0053},
  doi       = {10.1103/PhysRevE.92.042910},
  pages     = {042910},
  year      = {2015},
  language  = {en}
}
@article{LehnertzAnsmannBialonskietal.2014,
  author    = {Lehnertz, Klaus and Ansmann, Gerrit and Bialonski, Stephan and Dickten, Henning and Geier, Christian and Porz, Stephan},
  title     = {Evolving networks in the human epileptic brain},
  series = {Physica D: Nonlinear Phenomena},
  volume    = {267},
  journal   = {Physica D: Nonlinear Phenomena},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0167-2789},
  doi       = {10.1016/j.physd.2013.06.009},
  pages     = {7 -- 15},
  year      = {2014},
  abstract  = {Network theory provides novel concepts that promise an improved characterization of interacting dynamical systems. Within this framework, evolving networks can be considered as being composed of nodes, representing systems, and of time-varying edges, representing interactions between these systems. This approach is highly attractive to further our understanding of the physiological and pathophysiological dynamics in human brain networks. Indeed, there is growing evidence that the epileptic process can be regarded as a large-scale network phenomenon. We here review methodologies for inferring networks from empirical time series and for a characterization of these evolving networks. We summarize recent findings derived from studies that investigate human epileptic brain networks evolving on timescales ranging from few seconds to weeks. We point to possible pitfalls and open issues, and discuss future perspectives.},
  language  = {en}
}