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 - 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 - INPR A1 - Ringers, Christa A1 - Bialonski, Stephan A1 - Solovev, Anton A1 - Hansen, Jan N. A1 - Ege, Mert A1 - Friedrich, Benjamin M. A1 - Jurisch-Yaksi, Nathalie T1 - Preprint: Local synchronization of cilia and tissue-scale cilia alignment are sufficient for global metachronal waves T2 - bioRxiv N2 - Motile cilia are hair-like cell extensions present in multiple organs of the body. How cilia coordinate their regular beat in multiciliated epithelia to move fluids remains insufficiently understood, particularly due to lack of rigorous quantification. We combine here experiments, novel analysis tools, and theory to address this knowledge gap. We investigate collective dynamics of cilia in the zebrafish nose, due to its conserved properties with other ciliated tissues and its superior accessibility for non-invasive imaging. We revealed that cilia are synchronized only locally and that the size of local synchronization domains increases with the viscosity of the surrounding medium. Despite the fact that synchronization is local only, we observed global patterns of traveling metachronal waves across the multiciliated epithelium. Intriguingly, these global wave direction patterns are conserved across individual fish, but different for left and right nose, unveiling a chiral asymmetry of metachronal coordination. To understand the implications of synchronization for fluid pumping, we used a computational model of a regular array of cilia. We found that local metachronal synchronization prevents steric collisions and improves fluid pumping in dense cilia carpets, but hardly affects the direction of fluid flow. In conclusion, we show that local synchronization together with tissue-scale cilia alignment are sufficient to generate metachronal wave patterns in multiciliated epithelia, which enhance their physiological function of fluid pumping. Y1 - 2021 U6 - http://dx.doi.org/10.1101/2021.11.23.469646 N1 - Veröffentlicht in eLife 12:e77701 (https://doi.org/10.7554/eLife.77701). ER - TY - JOUR A1 - Ringers, Christa A1 - Bialonski, Stephan A1 - Ege, Mert A1 - Solovev, Anton A1 - Hansen, Jan Niklas A1 - Jeong, Inyoung A1 - Friedrich, Benjamin M. A1 - Jurisch-Yaksi, Nathalie T1 - Novel analytical tools reveal that local synchronization of cilia coincides with tissue-scale metachronal waves in zebrafish multiciliated epithelia JF - eLife N2 - Motile cilia are hair-like cell extensions that beat periodically to generate fluid flow along various epithelial tissues within the body. In dense multiciliated carpets, cilia were shown to exhibit a remarkable coordination of their beat in the form of traveling metachronal waves, a phenomenon which supposedly enhances fluid transport. Yet, how cilia coordinate their regular beat in multiciliated epithelia to move fluids remains insufficiently understood, particularly due to lack of rigorous quantification. We combine experiments, novel analysis tools, and theory to address this knowledge gap. To investigate collective dynamics of cilia, we studied zebrafish multiciliated epithelia in the nose and the brain. We focused mainly on the zebrafish nose, due to its conserved properties with other ciliated tissues and its superior accessibility for non-invasive imaging. We revealed that cilia are synchronized only locally and that the size of local synchronization domains increases with the viscosity of the surrounding medium. Even though synchronization is local only, we observed global patterns of traveling metachronal waves across the zebrafish multiciliated epithelium. Intriguingly, these global wave direction patterns are conserved across individual fish, but different for left and right noses, unveiling a chiral asymmetry of metachronal coordination. To understand the implications of synchronization for fluid pumping, we used a computational model of a regular array of cilia. We found that local metachronal synchronization prevents steric collisions, i.e., cilia colliding with each other, and improves fluid pumping in dense cilia carpets, but hardly affects the direction of fluid flow. In conclusion, we show that local synchronization together with tissue-scale cilia alignment coincide and generate metachronal wave patterns in multiciliated epithelia, which enhance their physiological function of fluid pumping. Y1 - 2023 U6 - http://dx.doi.org/10.7554/eLife.77701 SN - 2050-084X VL - 12 PB - eLife Sciences Publications 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 - 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 - 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 - JOUR A1 - Lehnertz, Klaus A1 - Mormann, Florian A1 - Osterhage, Hannes A1 - Andy, Müller A1 - Prusseit, Jens A1 - Chernihovskyi, Anton A1 - Staniek, Matthäus A1 - Krug, Dieter A1 - Bialonski, Stephan A1 - Elger, Christian E. T1 - State-of-the-art of seizure prediction JF - Journal of Clinical Neurophysiology Y1 - 2007 U6 - http://dx.doi.org/10.1097/WNP.0b013e3180336f16 SN - 1537-1603 VL - 24 IS - 2 SP - 147 EP - 153 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 - Lehnertz, Klaus A1 - Bialonski, Stephan A1 - Horstmann, Marie-Therese A1 - Krug, Dieter A1 - Rothkegel, Alexander A1 - Staniek, Matthäus A1 - Wagner, Tobias T1 - Synchronization phenomena in human epileptic brain networks JF - Journal of neuroscience methods Y1 - 2009 U6 - http://dx.doi.org/10.1016/j.jneumeth.2009.05.015 SN - 0165-0270 VL - 183 IS - 1 SP - 42 EP - 48 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 - Kuhnert, Marie-Therese A1 - Bialonski, Stephan A1 - Noenning, Nina A1 - Mai, Heinke A1 - Hinrichs, Hermann A1 - Helmstaedter, Christoph A1 - Lehnertz, Klaus T1 - Incidental and intentional learning of verbal episodic material differentially modifies functional brain networks JF - Plos one N2 - Learning- and memory-related processes are thought to result from dynamic interactions in large-scale brain networks that include lateral and mesial structures of the temporal lobes. We investigate the impact of incidental and intentional learning of verbal episodic material on functional brain networks that we derive from scalp-EEG recorded continuously from 33 subjects during a neuropsychological test schedule. Analyzing the networks' global statistical properties we observe that intentional but not incidental learning leads to a significantly increased clustering coefficient, and the average shortest path length remains unaffected. Moreover, network modifications correlate with subsequent recall performance: the more pronounced the modifications of the clustering coefficient, the higher the recall performance. Our findings provide novel insights into the relationship between topological aspects of functional brain networks and higher cognitive functions. Y1 - 2013 U6 - http://dx.doi.org/10.1371/journal.pone.0080273 VL - 8 IS - 11 PB - PLOS CY - San Francisco 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 - JOUR A1 - Horstmann, Marie-Therese A1 - Bialonski, Stephan A1 - Noenning, Nina A1 - Mai, Heinke A1 - Prusseit, Jens A1 - Wellmer, Jörg A1 - Hinrichs, Hermann A1 - Lehnertz, Klaus T1 - State dependent properties of epileptic brain networks: Comparative graph–theoretical analyses of simultaneously recorded EEG and MEG JF - Clinical Neurophysiology N2 - Objective To investigate whether functional brain networks of epilepsy patients treated with antiepileptic medication differ from networks of healthy controls even during the seizure-free interval. Methods We applied different rules to construct binary and weighted networks from EEG and MEG data recorded under a resting-state eyes-open and eyes-closed condition from 21 epilepsy patients and 23 healthy controls. The average shortest path length and the clustering coefficient served as global statistical network characteristics. Results Independent on the behavioral condition, epileptic brains exhibited a more regular functional network structure. Similarly, the eyes-closed condition was characterized by a more regular functional network structure in both groups. The amount of network reorganization due to behavioral state changes was similar in both groups. Consistent findings could be achieved for networks derived from EEG but hardly from MEG recordings, and network construction rules had a rather strong impact on our findings. Conclusions Despite the locality of the investigated processes epileptic brain networks differ in their global characteristics from non-epileptic brain networks. Further methodological developments are necessary to improve the characterization of disturbed and normal functional networks. Significance An increased regularity and a diminished modulation capability appear characteristic of epileptic brain networks. Y1 - 2010 U6 - http://dx.doi.org/10.1016/j.clinph.2009.10.013 SN - 1388-2457 VL - 121 IS - 2 SP - 172 EP - 185 PB - Elsevier CY - Amsterdam ER - TY - INPR A1 - Grieger, Niklas A1 - Mehrkanoon, Siamak A1 - Bialonski, Stephan T1 - Preprint: Data-efficient sleep staging with synthetic time series pretraining T2 - arXiv N2 - Analyzing electroencephalographic (EEG) time series can be challenging, especially with deep neural networks, due to the large variability among human subjects and often small datasets. To address these challenges, various strategies, such as self-supervised learning, have been suggested, but they typically rely on extensive empirical datasets. Inspired by recent advances in computer vision, we propose a pretraining task termed "frequency pretraining" to pretrain a neural network for sleep staging by predicting the frequency content of randomly generated synthetic time series. Our experiments demonstrate that our method surpasses fully supervised learning in scenarios with limited data and few subjects, and matches its performance in regimes with many subjects. Furthermore, our results underline the relevance of frequency information for sleep stage scoring, while also demonstrating that deep neural networks utilize information beyond frequencies to enhance sleep staging performance, which is consistent with previous research. We anticipate that our approach will be advantageous across a broad spectrum of applications where EEG data is limited or derived from a small number of subjects, including the domain of brain-computer interfaces. Y1 - 2024 ER - TY - JOUR A1 - Geier, Christian A1 - Lehnertz, Klaus A1 - Bialonski, Stephan T1 - Time-dependent degree-degree correlations in epileptic brain networks: from assortative to dissortative mixing JF - Frontiers in Human Neuroscience Y1 - 2015 U6 - http://dx.doi.org/10.3389/fnhum.2015.00462 SN - 1662-5161 PB - Frontiers Research Foundation CY - Lausanne 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 - INPR A1 - Bornheim, Tobias A1 - Niklas, Grieger A1 - Blaneck, Patrick Gustav A1 - Bialonski, Stephan T1 - Preprint: Speaker attribution in German parliamentary debates with QLoRA-adapted large language models T2 - Journal for Language Technology and Computational Linguistics N2 - The growing body of political texts opens up new opportunities for rich insights into political dynamics and ideologies but also increases the workload for manual analysis. Automated speaker attribution, which detects who said what to whom in a speech event and is closely related to semantic role labeling, is an important processing step for computational text analysis. We study the potential of the large language model family Llama 2 to automate speaker attribution in German parliamentary debates from 2017-2021. We fine-tune Llama 2 with QLoRA, an efficient training strategy, and observe our approach to achieve competitive performance in the GermEval 2023 Shared Task On Speaker Attribution in German News Articles and Parliamentary Debates. Our results shed light on the capabilities of large language models in automating speaker attribution, revealing a promising avenue for computational analysis of political discourse and the development of semantic role labeling systems. Y1 - 2023 U6 - http://dx.doi.org/10.48550/arXiv.2309.09902 N1 - Veröffentlichte Version verfügbar unter: https://doi.org/10.21248/jlcl.37.2024.244 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 - 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 -