@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{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{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{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{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{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}
}
@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}
}
@book{Bialonski2012,
  author    = {Bialonski, Stephan},
  title     = {Inferring complex networks from time series of dynamical systems: Pitfalls, misinterpretations, and possible solutions},
  publisher = {Universit{\"a}ts- und Landesbibliothek Bonn},
  address   = {Bonn},
  pages     = {Online-Ausgabe (III, 135 S. : Ill., graph. Darst.)},
  year      = {2012},
  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{BialonskiWellmerElgeretal.2006,
  author    = {Bialonski, Stephan and Wellmer, J{\"o}rg and Elger, Christian E. and Lehnertz, Klaus},
  title     = {Interictal focus localization in neocortical lesional epilepsies with synchronization cluster analysis},
  series = {Epilepsia},
  volume    = {47},
  journal   = {Epilepsia},
  issn      = {0013-9580},
  pages     = {36},
  year      = {2006},
  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{LehnertzBialonskiHorstmannetal.2009,
  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     = {Synchronization phenomena in human epileptic brain networks},
  series = {Journal of neuroscience methods},
  volume    = {183},
  journal   = {Journal of neuroscience methods},
  number    = {1},
  issn      = {0165-0270},
  doi       = {10.1016/j.jneumeth.2009.05.015},
  pages     = {42 -- 48},
  year      = {2009},
  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{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{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}
}
@article{BialonskiSchindlerElgeretal.2008,
  author    = {Bialonski, Stephan and Schindler, K. and Elger, C. E. and Lehnertz, Klaus},
  title     = {Lateralized characteristics of the evolution of EEG correlation during focal onset seizures: a mechanism to prevent secondary generalization?},
  series = {Epilepsia},
  volume    = {49},
  journal   = {Epilepsia},
  issn      = {0013-9580},
  pages     = {11 -- 11},
  year      = {2008},
  abstract  = {Rationale: Previous studies [Topolnik et al., Cereb Cortex 2003; 13: 883; Schindler et al., Brain 2007; 130: 65] indicate that the termination of focal onset seizures may be causally related to an increase of global neuronal correlation during the second half of the seizures. This increase was observed to occur earlier in complex partial seizures than in secondarily generalized seizures. We here address the question whether such an increase of neuronal correlation prior to seizure end is indeed a global phenomenon, involving both hemispheres or whether there are side-specific differences. Methods: We analyzed 20 focal onset seizures (10 complex partial, 10 secondarily generalized seizures) recorded in 13 patients who underwent presurgical evaluation of focal epilepsies of different origin. EEG was recorded intracranially from bilaterally implanted subdural strip and intrahippocampal depth electrodes. Utilizing a moving window approach, we investigated the evolution of the maximum cross correlation for all channel combinations during seizures. For each moving window the mean value of the maximum cross correlation (MCC) between all electrode contacts was computed separately for each hemisphere. After normalization of seizure durations, MCC values of the ipsi- and contralateral hemisphere for all seizures were determined. Results: We observed that the MCC of the contralateral hemisphere in complex partial seizures increased during the first half of the seizure, whereas, for the same time interval, the MCC of the ipsilateral hemisphere even declined below the level of the pre-seizure period. In contrast, no significant differences between both hemispheres could be observed for secondarily generalized seizures where both hemispheres showed a simultaneous increase of MCC during the second half of the seizures. The level of MCC for the contralateral hemisphere was higher for complex partial seizures than for secondarily generalized seizures during the first half of the seizure. Conclusions: Our findings indicate that there are indeed lateralized differences in the evolution of global neuronal correlation during complex partial and secondarily generalized seizures. The observed contralateral increase of neuronal correlation during complex partial seizures might indicate an emerging self-organizing mechanism for preventing the spread of seizure activity.},
  language  = {en}
}
@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{LehnertzMormannOsterhageetal.2007,
  author    = {Lehnertz, Klaus and Mormann, Florian and Osterhage, Hannes and Andy, M{\"u}ller and Prusseit, Jens and Chernihovskyi, Anton and Staniek, Matth{\"a}us and Krug, Dieter and Bialonski, Stephan and Elger, Christian E.},
  title     = {State-of-the-art of seizure prediction},
  series = {Journal of Clinical Neurophysiology},
  volume    = {24},
  journal   = {Journal of Clinical Neurophysiology},
  number    = {2},
  issn      = {1537-1603},
  doi       = {10.1097/WNP.0b013e3180336f16},
  pages     = {147 -- 153},
  year      = {2007},
  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}
}
@article{SchneiderSchwabedalBialonski2022,
  author    = {Schneider, Jules and Schwabedal, Justus T. C. and Bialonski, Stephan},
  title     = {Schlafspindeln - Funktion, Detektion und Nutzung als Biomarker f{\"u}r die psychiatrische Diagnostik},
  series = {Der Nervenarzt},
  journal   = {Der Nervenarzt},
  publisher = {Springer},
  address   = {Berlin, Heidelberg},
  issn      = {1433-0407},
  doi       = {10.1007/s00115-022-01340-z},
  pages     = {1 -- 8},
  year      = {2022},
  abstract  = {Hintergrund: Die Schlafspindel ist ein Graphoelement des Elektroenzephalogramms (EEG), das im Leicht- und Tiefschlaf beobachtet werden kann. Ver{\"a}nderungen der Spindelaktivit{\"a}t wurden f{\"u}r verschiedene psychiatrische Erkrankungen beschrieben. Schlafspindeln zeigen aufgrund ihrer relativ konstanten Eigenschaften Potenzial als Biomarker in der psychiatrischen Diagnostik. Methode: Dieser Beitrag liefert einen {\"U}berblick {\"u}ber den Stand der Wissenschaft zu Eigenschaften und Funktionen der Schlafspindeln sowie {\"u}ber beschriebene Ver{\"a}nderungen der Spindelaktivit{\"a}t bei psychiatrischen Erkrankungen. Verschiedene methodische Ans{\"a}tze und Ausblicke zur Spindeldetektion werden hinsichtlich deren Anwendungspotenzial in der psychiatrischen Diagnostik erl{\"a}utert. Ergebnisse und Schlussfolgerung: W{\"a}hrend Ver{\"a}nderungen der Spindelaktivit{\"a}t bei psychiatrischen Erkrankungen beschrieben wurden, ist deren exaktes Potenzial f{\"u}r die psychiatrische Diagnostik noch nicht ausreichend erforscht. Diesbez{\"u}glicher Erkenntnisgewinn wird in der Forschung gegenw{\"a}rtig durch ressourcenintensive und fehleranf{\"a}llige Methoden zur manuellen oder automatisierten Spindeldetektion ausgebremst. Neuere Detektionsans{\"a}tze, die auf Deep-Learning-Verfahren basieren, k{\"o}nnten die Schwierigkeiten bisheriger Detektionsmethoden {\"u}berwinden und damit neue M{\"o}glichkeiten f{\"u}r die praktisch},
  language  = {de}
}