@article{BialonskiAllefeldWellmeretal.2008,
  author    = {Bialonski, Stephan and Allefeld, C. and Wellmer, J. and Elger, C. and Lehnertz, K.},
  title     = {An approach to identify synchronization clusters within the epileptic network},
  series = {Klinische Neurophysiologie},
  volume    = {39},
  journal   = {Klinische Neurophysiologie},
  number    = {1},
  doi       = {10.1055/s-2008-1072881},
  pages     = {A79},
  year      = {2008},
  language  = {en}
}
@incollection{Bialonski2016,
  author    = {Bialonski, Stephan},
  title     = {Are interaction clusters in epileptic networks predictive of seizures?},
  series = {Epilepsy: The Intersection of Neurosciences, Biology, Mathematics, Engineering, and Physics},
  booktitle = {Epilepsy: The Intersection of Neurosciences, Biology, Mathematics, Engineering, and Physics},
  publisher = {CRC Press},
  isbn      = {978-143983886-0},
  pages     = {349 -- 355},
  year      = {2016},
  language  = {en}
}
@article{BialonskiLehnertz2013,
  author    = {Bialonski, Stephan and Lehnertz, Klaus},
  title     = {Assortative mixing in functional brain networks during epileptic seizures},
  series = {Chaos: An Interdisciplinary Journal of Nonlinear Science},
  volume    = {23},
  journal   = {Chaos: An Interdisciplinary Journal of Nonlinear Science},
  number    = {3},
  doi       = {10.1063/1.4821915},
  pages     = {033139},
  year      = {2013},
  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}
}
@inproceedings{BlaneckBornheimGriegeretal.2022,
  author    = {Blaneck, Patrick Gustav and Bornheim, Tobias and Grieger, Niklas and Bialonski, Stephan},
  title     = {Automatic readability assessment of german sentences with transformer ensembles},
  series = {Proceedings of the GermEval 2022 Workshop on Text Complexity Assessment of German Text},
  booktitle = {Proceedings of the GermEval 2022 Workshop on Text Complexity Assessment of German Text},
  publisher = {Association for Computational Linguistics},
  address   = {Potsdam},
  doi       = {10.48550/arXiv.2209.04299},
  pages     = {57 -- 62},
  year      = {2022},
  abstract  = {Reliable methods for automatic readability assessment have the potential to impact a variety of fields, ranging from machine translation to self-informed learning. Recently, large language models for the German language (such as GBERT and GPT-2-Wechsel) have become available, allowing to develop Deep Learning based approaches that promise to further improve automatic readability assessment. In this contribution, we studied the ability of ensembles of fine-tuned GBERT and GPT-2-Wechsel models to reliably predict the readability of German sentences. We combined these models with linguistic features and investigated the dependence of prediction performance on ensemble size and composition. Mixed ensembles of GBERT and GPT-2-Wechsel performed better than ensembles of the same size consisting of only GBERT or GPT-2-Wechsel models. Our models were evaluated in the GermEval 2022 Shared Task on Text Complexity Assessment on data of German sentences. On out-of-sample data, our best ensemble achieved a root mean squared error of 0:435.},
  language  = {en}
}
@incollection{OsterhageBialonskiStanieketal.2008,
  author    = {Osterhage, Hannes and Bialonski, Stephan and Staniek, Matth{\"a}us and Schindler, Kaspar and Wagner, Tobias and Elger, Christian E. and Lehnertz, Klaus},
  title     = {Bivariate and multivariate time series analysis techniques and their potential impact for seizure prediction},
  series = {Seizure Prediction in Epilepsy: From Basic Mechanisms to Clinical Applications},
  booktitle = {Seizure Prediction in Epilepsy: From Basic Mechanisms to Clinical Applications},
  publisher = {Wiley-VCH},
  address   = {Weinheim},
  isbn      = {978-3-527-62519-2},
  doi       = {10.1002/9783527625192.ch15},
  pages     = {189 -- 208},
  year      = {2008},
  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{BialonskiGrieger2023,
  author    = {Bialonski, Stephan and Grieger, Niklas},
  title     = {Der KI-Chatbot ChatGPT: Eine Herausforderung f{\"u}r die Hochschulen},
  series = {Die neue Hochschule},
  volume    = {2023},
  journal   = {Die neue Hochschule},
  number    = {1},
  publisher = {HLB},
  address   = {Bonn},
  issn      = {0340-448X},
  doi       = {10.5281/zenodo.7533758},
  pages     = {24 -- 27},
  year      = {2023},
  abstract  = {Essays, Gedichte, Programmcode: ChatGPT generiert automatisch Texte auf bisher unerreicht hohem Niveau. Dieses und nachfolgende Systeme werden nicht nur die akademische Welt nachhaltig ver{\"a}ndern.},
  language  = {de}
}
@article{AllefeldBialonski2007,
  author    = {Allefeld, Carsten and Bialonski, Stephan},
  title     = {Detecting synchronization clusters in multivariate time series via coarse-graining of Markov chains},
  series = {Physical Review E},
  volume    = {76},
  journal   = {Physical Review E},
  number    = {6},
  issn      = {2470-0053},
  doi       = {10.1103/PhysRevE.76.066207},
  pages     = {066207},
  year      = {2007},
  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}
}
@incollection{LehnertzBialonskiHorstmannetal.2010,
  author    = {Lehnertz, Klaus and Bialonski, Stephan and Horstmann, Marie-Therese and Krug, Dieter and Rothkegel, Alexander and Staniek, Matth{\"a}us and Wagner, Tobias},
  title     = {Epilepsy},
  series = {Reviews of Nonlinear Dynamics and Complexity, Volume 2},
  booktitle = {Reviews of Nonlinear Dynamics and Complexity, Volume 2},
  publisher = {Wiley-VCH},
  isbn      = {9783527628001},
  doi       = {10.1002/9783527628001.ch5},
  pages     = {159 -- 200},
  year      = {2010},
  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{SchindlerBialonskiHorstmannetal.2008,
  author    = {Schindler, Kaspar A. and Bialonski, Stephan and Horstmann, Marie-Therese and Elger, Christian E. and Lehnertz, Klaus},
  title     = {Evolving functional network properties and synchronizability during human epileptic seizures},
  series = {Chaos: An Interdisciplinary Journal of Nonlinear Science},
  volume    = {18},
  journal   = {Chaos: An Interdisciplinary Journal of Nonlinear Science},
  number    = {3},
  issn      = {1089-7682},
  doi       = {10.1063/1.2966112},
  pages     = {033119},
  year      = {2008},
  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}
}
@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}
}
@inproceedings{BornheimGriegerBialonski2021,
  author    = {Bornheim, Tobias and Grieger, Niklas and Bialonski, Stephan},
  title     = {FHAC at GermEval 2021: Identifying German toxic, engaging, and fact-claiming comments with ensemble learning},
  series = {Proceedings of the GermEval 2021 Workshop on the Identification of Toxic, Engaging, and Fact-Claiming Comments : 17th Conference on Natural Language Processing KONVENS 2021},
  booktitle = {Proceedings of the GermEval 2021 Workshop on the Identification of Toxic, Engaging, and Fact-Claiming Comments : 17th Conference on Natural Language Processing KONVENS 2021},
  publisher = {Heinrich Heine University},
  address   = {D{\"u}sseldorf},
  doi       = {10.48415/2021/fhw5-x128},
  pages     = {105 -- 111},
  year      = {2021},
  language  = {en}
}
@article{BialonskiHorstmannLehnertz2010,
  author    = {Bialonski, Stephan and Horstmann, Marie-Therese and Lehnertz, Klaus},
  title     = {From brain to earth and climate systems: Small-world interaction networks or not?},
  series = {Chaos: An Interdisciplinary Journal of Nonlinear Science},
  volume    = {20},
  journal   = {Chaos: An Interdisciplinary Journal of Nonlinear Science},
  number    = {1},
  publisher = {AIP Publishing},
  address   = {Melville, NY},
  issn      = {1089-7682},
  doi       = {10.1063/1.3360561},
  pages     = {013134},
  year      = {2010},
  abstract  = {We consider recent reports on small-world topologies of interaction networks derived from the dynamics of spatially extended systems that are investigated in diverse scientific fields such as neurosciences, geophysics, or meteorology. With numerical simulations that mimic typical experimental situations, we have identified an important constraint when characterizing such networks: indications of a small-world topology can be expected solely due to the spatial sampling of the system along with the commonly used time series analysis based approaches to network characterization.},
  language  = {en}
}
@incollection{BialonskiLehnertz2013,
  author    = {Bialonski, Stephan and Lehnertz, Klaus},
  title     = {From time series to complex networks: an overview},
  series = {Recent Advances in Predicting and Preventing Epileptic Seizures: Proceedings of the 5th International Workshop on Seizure Prediction},
  booktitle = {Recent Advances in Predicting and Preventing Epileptic Seizures: Proceedings of the 5th International Workshop on Seizure Prediction},
  isbn      = {978-981-4525-36-7},
  doi       = {10.1142/9789814525350_0010},
  pages     = {132 -- 147},
  year      = {2013},
  abstract  = {The network approach towards the analysis of the dynamics of complex systems has been successfully applied in a multitude of studies in the neurosciences and has yielded fascinating insights. With this approach, a complex system is considered to be composed of different constituents which interact with each other. Interaction structures can be compactly represented in interaction networks. In this contribution, we present a brief overview about how interaction networks are derived from multivariate time series, about basic network characteristics, and about challenges associated with this analysis approach.},
  language  = {en}
}
@article{GeierBialonskiElgeretal.2015,
  author    = {Geier, Christian and Bialonski, Stephan and Elger, Christian E. and Lehnertz, Klaus},
  title     = {How important is the seizure onset zone for seizure dynamics?},
  series = {Seizure},
  volume    = {25},
  journal   = {Seizure},
  issn      = {1059-1311},
  doi       = {10.1016/j.seizure.2014.10.013},
  pages     = {160 -- 166},
  year      = {2015},
  language  = {en}
}
@article{BialonskiLehnertz2006,
  author    = {Bialonski, Stephan and Lehnertz, Klaus},
  title     = {Identifying phase synchronization clusters in spatially extended dynamical systems},
  series = {Physical Review E},
  volume    = {74},
  journal   = {Physical Review E},
  number    = {5},
  issn      = {2470-0053},
  doi       = {10.1103/PhysRevE.74.051909},
  pages     = {051909},
  year      = {2006},
  language  = {en}
}