@article{GeierLehnertzBialonski2015,
  author    = {Geier, Christian and Lehnertz, Klaus and Bialonski, Stephan},
  title     = {Time-dependent degree-degree correlations in epileptic brain networks: from assortative to dissortative mixing},
  series = {Frontiers in Human Neuroscience},
  journal   = {Frontiers in Human Neuroscience},
  publisher = {Frontiers Research Foundation},
  address   = {Lausanne},
  issn      = {1662-5161},
  doi       = {10.3389/fnhum.2015.00462},
  year      = {2015},
  language  = {en}
}
@article{MulhernBialonskiKantz2015,
  author    = {Mulhern, Colm and Bialonski, Stephan and Kantz, Holger},
  title     = {Extreme events due to localization of energy},
  series = {Physical Review E},
  volume    = {91},
  journal   = {Physical Review E},
  number    = {1},
  issn      = {2470-0053},
  doi       = {10.1103/PhysRevE.91.012918},
  pages     = {012918},
  year      = {2015},
  language  = {en}
}
@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{BialonskiCaronSchloenetal.2016,
  author    = {Bialonski, Stephan and Caron, David A. and Schloen, Julia and Feudel, Ulrike and Kantz, Holger and Moorthi, Stefanie D.},
  title     = {Phytoplankton dynamics in the Southern California Bight indicate a complex mixture of transport and biology},
  series = {Journal of Plankton Research},
  volume    = {38},
  journal   = {Journal of Plankton Research},
  number    = {4},
  publisher = {Oxford University Press},
  address   = {Oxford},
  issn      = {1464-3774},
  doi       = {10.1093/plankt/fbv122},
  pages     = {1077 -- 1091},
  year      = {2016},
  abstract  = {The stimulation and dominance of potentially harmful phytoplankton taxa at a given locale and time are determined by local environmental conditions as well as by transport to or from neighboring regions. The present study investigated the occurrence of common harmful algal bloom (HAB) taxa within the Southern California Bight, using cross-correlation functions to determine potential dependencies between HAB taxa and environmental factors, and potential links to algal transport via local hydrography and currents. A simulation study, in which Lagrangian particles were released, was used to assess travel times due to advection by prevailing ocean currents in the bight. Our results indicate that transport of some taxa may be an important mechanism for the expansion of their distributions into other regions, which was supported by mean travel times derived from our simulation study and other literature on ocean currents in the Southern California Bight. In other cases, however, phytoplankton dynamics were rather linked to local environmental conditions, including coastal upwelling events. Overall, our study shows that complex current patterns in the Southern California Bight may contribute significantly to the formation and expansion of HABs in addition to local environmental factors determining the spatiotemporal dynamics of phytoplankton blooms.},
  language  = {en}
}
@article{NgamgaBialonskiMarwanetal.2016,
  author    = {Ngamga, Eulalie Joelle and Bialonski, Stephan and Marwan, Norbert and Kurths, J{\"u}rgen and Geier, Christian and Lehnertz, Klaus},
  title     = {Evaluation of selected recurrence measures in discriminating pre-ictal and inter-ictal periods from epileptic EEG data},
  series = {Physics Letters A},
  volume    = {380},
  journal   = {Physics Letters A},
  number    = {16},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0375-9601},
  doi       = {10.1016/j.physleta.2016.02.024},
  pages     = {1419 -- 1425},
  year      = {2016},
  abstract  = {We investigate the suitability of selected measures of complexity based on recurrence quantification analysis and recurrence networks for an identification of pre-seizure states in multi-day, multi-channel, invasive electroencephalographic recordings from five epilepsy patients. We employ several statistical techniques to avoid spurious findings due to various influencing factors and due to multiple comparisons and observe precursory structures in three patients. Our findings indicate a high congruence among measures in identifying seizure precursors and emphasize the current notion of seizure generation in large-scale epileptic networks. A final judgment of the suitability for field studies, however, requires evaluation on a larger database.},
  language  = {en}
}
@article{KarnatakKantzBialonski2017,
  author    = {Karnatak, Rajat and Kantz, Holger and Bialonski, Stephan},
  title     = {Early warning signal for interior crises in excitable systems},
  series = {Physical Review E},
  volume    = {96},
  journal   = {Physical Review E},
  number    = {4},
  issn      = {2470-0053},
  doi       = {10.1103/PhysRevE.96.042211},
  pages     = {042211},
  year      = {2017},
  language  = {en}
}
@article{SchwabedalSippelBrandtetal.2018,
  author    = {Schwabedal, Justus T. C. and Sippel, Daniel and Brandt, Moritz D. and Bialonski, Stephan},
  title     = {Automated Classification of Sleep Stages and EEG Artifacts in Mice with Deep Learning},
  doi       = {10.48550/arXiv.1809.08443},
  year      = {2018},
  abstract  = {Sleep scoring is a necessary and time-consuming task in sleep studies. In animal models (such as mice) or in humans, automating this tedious process promises to facilitate long-term studies and to promote sleep biology as a data-driven f ield. We introduce a deep neural network model that is able to predict different states of consciousness (Wake, Non-REM, REM) in mice from EEG and EMG recordings with excellent scoring results for out-of-sample data. Predictions are made on epochs of 4 seconds length, and epochs are classified as artifactfree or not. The model architecture draws on recent advances in deep learning and in convolutional neural networks research. In contrast to previous approaches towards automated sleep scoring, our model does not rely on manually defined features of the data but learns predictive features automatically. We expect deep learning models like ours to become widely applied in different fields, automating many repetitive cognitive tasks that were previously difficult to tackle.},
  language  = {en}
}