TY - JOUR A1 - Bialonski, Stephan A1 - Allefeld, C. A1 - Wellmer, J. A1 - Elger, C. A1 - Lehnertz, K. T1 - An approach to identify synchronization clusters within the epileptic network JF - Klinische Neurophysiologie Y1 - 2008 U6 - http://dx.doi.org/10.1055/s-2008-1072881 VL - 39 IS - 1 SP - A79 ER - TY - CHAP A1 - Bialonski, Stephan T1 - Are interaction clusters in epileptic networks predictive of seizures? T2 - Epilepsy: The Intersection of Neurosciences, Biology, Mathematics, Engineering, and Physics Y1 - 2016 SN - 978-143983886-0 SP - 349 EP - 355 PB - CRC Press ER - TY - JOUR A1 - Bialonski, Stephan A1 - Lehnertz, Klaus T1 - Assortative mixing in functional brain networks during epileptic seizures JF - Chaos: An Interdisciplinary Journal of Nonlinear Science Y1 - 2013 U6 - http://dx.doi.org/10.1063/1.4821915 VL - 23 IS - 3 SP - 033139 ER - TY - JOUR A1 - Schwabedal, Justus T. C. A1 - Sippel, Daniel A1 - Brandt, Moritz D. A1 - Bialonski, Stephan T1 - Automated Classification of Sleep Stages and EEG Artifacts in Mice with Deep Learning N2 - 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. Y1 - 2018 U6 - http://dx.doi.org/10.48550/arXiv.1809.08443 ER - TY - CHAP A1 - Blaneck, Patrick Gustav A1 - Bornheim, Tobias A1 - Grieger, Niklas A1 - Bialonski, Stephan T1 - Automatic readability assessment of german sentences with transformer ensembles T2 - Proceedings of the GermEval 2022 Workshop on Text Complexity Assessment of German Text N2 - 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. Y1 - 2022 U6 - http://dx.doi.org/10.48550/arXiv.2209.04299 N1 - Proceedings of the 18th Conference on Natural Language Processing/Konferenz zur Verarbeitung natürlicher Sprache (KONVENS 2022) 12-15 September, 2022 University of Potsdam Potsdam, Germany SP - 57 EP - 62 PB - Association for Computational Linguistics CY - Potsdam ER - TY - CHAP A1 - Osterhage, Hannes A1 - Bialonski, Stephan A1 - Staniek, Matthäus A1 - Schindler, Kaspar A1 - Wagner, Tobias A1 - Elger, Christian E. A1 - Lehnertz, Klaus T1 - Bivariate and multivariate time series analysis techniques and their potential impact for seizure prediction T2 - Seizure Prediction in Epilepsy: From Basic Mechanisms to Clinical Applications Y1 - 2008 SN - 978-3-527-62519-2 U6 - http://dx.doi.org/10.1002/9783527625192.ch15 SP - 189 EP - 208 PB - Wiley-VCH CY - Weinheim ER - TY - JOUR A1 - Bialonski, Stephan A1 - Ansmann, Gerrit A1 - Kantz, Holger T1 - Data-driven prediction and prevention of extreme events in a spatially extended excitable system JF - Physical Review E Y1 - 2015 U6 - http://dx.doi.org/10.1103/PhysRevE.92.042910 SN - 2470-0053 VL - 92 IS - 4 SP - 042910 ER - TY - JOUR A1 - Bialonski, Stephan A1 - Grieger, Niklas T1 - Der KI-Chatbot ChatGPT: Eine Herausforderung für die Hochschulen JF - Die neue Hochschule N2 - Essays, Gedichte, Programmcode: ChatGPT generiert automatisch Texte auf bisher unerreicht hohem Niveau. Dieses und nachfolgende Systeme werden nicht nur die akademische Welt nachhaltig verändern. Y1 - 2023 U6 - http://dx.doi.org/10.5281/zenodo.7533758 SN - 0340-448X VL - 2023 IS - 1 SP - 24 EP - 27 PB - HLB CY - Bonn ER - TY - JOUR A1 - Allefeld, Carsten A1 - Bialonski, Stephan T1 - Detecting synchronization clusters in multivariate time series via coarse-graining of Markov chains JF - Physical Review E Y1 - 2007 U6 - http://dx.doi.org/10.1103/PhysRevE.76.066207 SN - 2470-0053 VL - 76 IS - 6 SP - 066207 ER - TY - JOUR A1 - Karnatak, Rajat A1 - Kantz, Holger A1 - Bialonski, Stephan T1 - Early warning signal for interior crises in excitable systems JF - Physical Review E Y1 - 2017 U6 - http://dx.doi.org/10.1103/PhysRevE.96.042211 SN - 2470-0053 VL - 96 IS - 4 SP - 042211 ER - TY - CHAP A1 - Lehnertz, Klaus A1 - Bialonski, Stephan A1 - Horstmann, Marie-Therese A1 - Krug, Dieter A1 - Rothkegel, Alexander A1 - Staniek, Matthäus A1 - Wagner, Tobias T1 - Epilepsy T2 - Reviews of Nonlinear Dynamics and Complexity, Volume 2 Y1 - 2010 SN - 9783527628001 U6 - http://dx.doi.org/10.1002/9783527628001.ch5 SP - 159 EP - 200 PB - Wiley-VCH ER - TY - JOUR A1 - Ngamga, Eulalie Joelle A1 - Bialonski, Stephan A1 - Marwan, Norbert A1 - Kurths, Jürgen A1 - Geier, Christian A1 - Lehnertz, Klaus T1 - Evaluation of selected recurrence measures in discriminating pre-ictal and inter-ictal periods from epileptic EEG data JF - Physics Letters A N2 - 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. Y1 - 2016 U6 - http://dx.doi.org/10.1016/j.physleta.2016.02.024 SN - 0375-9601 VL - 380 IS - 16 SP - 1419 EP - 1425 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Schindler, Kaspar A. A1 - Bialonski, Stephan A1 - Horstmann, Marie-Therese A1 - Elger, Christian E. A1 - Lehnertz, Klaus T1 - Evolving functional network properties and synchronizability during human epileptic seizures JF - Chaos: An Interdisciplinary Journal of Nonlinear Science Y1 - 2008 U6 - http://dx.doi.org/10.1063/1.2966112 SN - 1089-7682 VL - 18 IS - 3 SP - 033119 ER - TY - JOUR A1 - Lehnertz, Klaus A1 - Ansmann, Gerrit A1 - Bialonski, Stephan A1 - Dickten, Henning A1 - Geier, Christian A1 - Porz, Stephan T1 - Evolving networks in the human epileptic brain JF - Physica D: Nonlinear Phenomena N2 - 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. Y1 - 2014 U6 - http://dx.doi.org/10.1016/j.physd.2013.06.009 SN - 0167-2789 VL - 267 SP - 7 EP - 15 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Mulhern, Colm A1 - Bialonski, Stephan A1 - Kantz, Holger T1 - Extreme events due to localization of energy JF - Physical Review E Y1 - 2015 U6 - http://dx.doi.org/10.1103/PhysRevE.91.012918 SN - 2470-0053 VL - 91 IS - 1 SP - 012918 ER - TY - CHAP A1 - Bornheim, Tobias A1 - Grieger, Niklas A1 - Bialonski, Stephan T1 - FHAC at GermEval 2021: Identifying German toxic, engaging, and fact-claiming comments with ensemble learning T2 - Proceedings of the GermEval 2021 Workshop on the Identification of Toxic, Engaging, and Fact-Claiming Comments : 17th Conference on Natural Language Processing KONVENS 2021 Y1 - 2021 U6 - http://dx.doi.org/10.48415/2021/fhw5-x128 N1 - SP - 105 EP - 111 PB - Heinrich Heine University CY - Düsseldorf ER - TY - JOUR A1 - Bialonski, Stephan A1 - Horstmann, Marie-Therese A1 - Lehnertz, Klaus T1 - From brain to earth and climate systems: Small-world interaction networks or not? JF - Chaos: An Interdisciplinary Journal of Nonlinear Science N2 - 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. Y1 - 2010 U6 - http://dx.doi.org/10.1063/1.3360561 SN - 1089-7682 VL - 20 IS - 1 PB - AIP Publishing CY - Melville, NY ER - TY - CHAP A1 - Bialonski, Stephan A1 - Lehnertz, Klaus T1 - From time series to complex networks: an overview T2 - Recent Advances in Predicting and Preventing Epileptic Seizures: Proceedings of the 5th International Workshop on Seizure Prediction N2 - 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. Y1 - 2013 SN - 978-981-4525-36-7 U6 - http://dx.doi.org/10.1142/9789814525350_0010 SP - 132 EP - 147 ER - TY - JOUR A1 - Geier, Christian A1 - Bialonski, Stephan A1 - Elger, Christian E. A1 - Lehnertz, Klaus T1 - How important is the seizure onset zone for seizure dynamics? JF - Seizure Y1 - 2015 U6 - http://dx.doi.org/10.1016/j.seizure.2014.10.013 SN - 1059-1311 VL - 25 SP - 160 EP - 166 ER - TY - JOUR A1 - Bialonski, Stephan A1 - Lehnertz, Klaus T1 - Identifying phase synchronization clusters in spatially extended dynamical systems JF - Physical Review E Y1 - 2006 U6 - http://dx.doi.org/10.1103/PhysRevE.74.051909 SN - 2470-0053 VL - 74 IS - 5 SP - 051909 ER -