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 - 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 - 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 - Bialonski, Stephan A1 - Caron, David A. A1 - Schloen, Julia A1 - Feudel, Ulrike A1 - Kantz, Holger A1 - Moorthi, Stefanie D. T1 - Phytoplankton dynamics in the Southern California Bight indicate a complex mixture of transport and biology JF - Journal of Plankton Research N2 - 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. Y1 - 2016 U6 - http://dx.doi.org/10.1093/plankt/fbv122 SN - 1464-3774 VL - 38 IS - 4 SP - 1077 EP - 1091 PB - Oxford University Press CY - Oxford 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 - 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 - 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 - 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 - 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 - 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 - BOOK A1 - Bialonski, Stephan T1 - Inferring complex networks from time series of dynamical systems: Pitfalls, misinterpretations, and possible solutions Y1 - 2012 N1 - Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, Diss., 2012 PB - Universitäts- und Landesbibliothek Bonn CY - Bonn 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 - Wellmer, Jörg A1 - Elger, Christian E. A1 - Lehnertz, Klaus T1 - Interictal focus localization in neocortical lesional epilepsies with synchronization cluster analysis JF - Epilepsia Y1 - 2006 SN - 0013-9580 VL - 47 SP - 36 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 - 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 - 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 - 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 - JOUR A1 - Bialonski, Stephan A1 - Schindler, K. A1 - Elger, C. E. A1 - Lehnertz, Klaus T1 - Lateralized characteristics of the evolution of EEG correlation during focal onset seizures: a mechanism to prevent secondary generalization? JF - Epilepsia N2 - 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. Y1 - 2008 SN - 0013-9580 VL - 49 SP - 11 EP - 11 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 - 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 - 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 - TY - JOUR A1 - Schneider, Jules A1 - Schwabedal, Justus T. C. A1 - Bialonski, Stephan T1 - Schlafspindeln – Funktion, Detektion und Nutzung als Biomarker für die psychiatrische Diagnostik JF - Der Nervenarzt N2 - Hintergrund: Die Schlafspindel ist ein Graphoelement des Elektroenzephalogramms (EEG), das im Leicht- und Tiefschlaf beobachtet werden kann. Veränderungen der Spindelaktivität wurden für verschiedene psychiatrische Erkrankungen beschrieben. Schlafspindeln zeigen aufgrund ihrer relativ konstanten Eigenschaften Potenzial als Biomarker in der psychiatrischen Diagnostik. Methode: Dieser Beitrag liefert einen Überblick über den Stand der Wissenschaft zu Eigenschaften und Funktionen der Schlafspindeln sowie über beschriebene Veränderungen der Spindelaktivität bei psychiatrischen Erkrankungen. Verschiedene methodische Ansätze und Ausblicke zur Spindeldetektion werden hinsichtlich deren Anwendungspotenzial in der psychiatrischen Diagnostik erläutert. Ergebnisse und Schlussfolgerung: Während Veränderungen der Spindelaktivität bei psychiatrischen Erkrankungen beschrieben wurden, ist deren exaktes Potenzial für die psychiatrische Diagnostik noch nicht ausreichend erforscht. Diesbezüglicher Erkenntnisgewinn wird in der Forschung gegenwärtig durch ressourcenintensive und fehleranfällige Methoden zur manuellen oder automatisierten Spindeldetektion ausgebremst. Neuere Detektionsansätze, die auf Deep-Learning-Verfahren basieren, könnten die Schwierigkeiten bisheriger Detektionsmethoden überwinden und damit neue Möglichkeiten für die praktisch KW - Schlafspindeldetektion KW - Psychiatrische Biomarker KW - · Psychiatrische Erkrankungen/Diagnostik KW - Elektroenzephalographie KW - Deep Learning Y1 - 2022 U6 - http://dx.doi.org/10.1007/s00115-022-01340-z SN - 1433-0407 SP - 1 EP - 8 PB - Springer CY - Berlin, Heidelberg 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 - 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 - Bornheim, Tobias A1 - Grieger, Niklas A1 - Blaneck, Patrick Gustav A1 - Bialonski, Stephan T1 - Speaker Attribution in German Parliamentary Debates with QLoRA-adapted Large Language Models JF - Journal for language technology and computational linguistics : JLCL 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. KW - large language models KW - German KW - speaker attribution KW - semantic role labeling Y1 - 2024 U6 - http://dx.doi.org/10.21248/jlcl.37.2024.244 SN - 2190-6858 VL - 37 IS - 1 PB - Gesellschaft für Sprachtechnologie und Computerlinguistik CY - Regensburg 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 - Grieger, Niklas A1 - Schwabedal, Justus T. C. A1 - Wendel, Stefanie A1 - Ritze, Yvonne A1 - Bialonski, Stephan T1 - Automated scoring of pre-REM sleep in mice with deep learning JF - Scientific Reports N2 - Reliable automation of the labor-intensive manual task of scoring animal sleep can facilitate the analysis of long-term sleep studies. In recent years, deep-learning-based systems, which learn optimal features from the data, increased scoring accuracies for the classical sleep stages of Wake, REM, and Non-REM. Meanwhile, it has been recognized that the statistics of transitional stages such as pre-REM, found between Non-REM and REM, may hold additional insight into the physiology of sleep and are now under vivid investigation. We propose a classification system based on a simple neural network architecture that scores the classical stages as well as pre-REM sleep in mice. When restricted to the classical stages, the optimized network showed state-of-the-art classification performance with an out-of-sample F1 score of 0.95 in male C57BL/6J mice. When unrestricted, the network showed lower F1 scores on pre-REM (0.5) compared to the classical stages. The result is comparable to previous attempts to score transitional stages in other species such as transition sleep in rats or N1 sleep in humans. Nevertheless, we observed that the sequence of predictions including pre-REM typically transitioned from Non-REM to REM reflecting sleep dynamics observed by human scorers. Our findings provide further evidence for the difficulty of scoring transitional sleep stages, likely because such stages of sleep are under-represented in typical data sets or show large inter-scorer variability. We further provide our source code and an online platform to run predictions with our trained network. Y1 - 2021 U6 - http://dx.doi.org/10.1038/s41598-021-91286-0 SN - 2045-2322 N1 - Corresponding author: Stephan Bialonski VL - 11 IS - Art. 12245 PB - Springer Nature CY - London 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 - TY - JOUR A1 - Kaulen, Lars A1 - Schwabedal, Justus T. C. A1 - Schneider, Jules A1 - Ritter, Philipp A1 - Bialonski, Stephan T1 - Advanced sleep spindle identification with neural networks JF - Scientific Reports N2 - Sleep spindles are neurophysiological phenomena that appear to be linked to memory formation and other functions of the central nervous system, and that can be observed in electroencephalographic recordings (EEG) during sleep. Manually identified spindle annotations in EEG recordings suffer from substantial intra- and inter-rater variability, even if raters have been highly trained, which reduces the reliability of spindle measures as a research and diagnostic tool. The Massive Online Data Annotation (MODA) project has recently addressed this problem by forming a consensus from multiple such rating experts, thus providing a corpus of spindle annotations of enhanced quality. Based on this dataset, we present a U-Net-type deep neural network model to automatically detect sleep spindles. Our model’s performance exceeds that of the state-of-the-art detector and of most experts in the MODA dataset. We observed improved detection accuracy in subjects of all ages, including older individuals whose spindles are particularly challenging to detect reliably. Our results underline the potential of automated methods to do repetitive cumbersome tasks with super-human performance. Y1 - 2022 U6 - http://dx.doi.org/10.1038/s41598-022-11210-y SN - 2045-2322 N1 - Corresponding author: Stephan Bialonski VL - 12 IS - Article number: 7686 SP - 1 EP - 10 PB - Springer Nature CY - London ER - TY - JOUR A1 - Röthenbacher, Annika A1 - Cesari, Matteo A1 - Doppler, Christopher E.J. A1 - Okkels, Niels A1 - Willemsen, Nele A1 - Sembowski, Nora A1 - Seger, Aline A1 - Lindner, Marie A1 - Brune, Corinna A1 - Stefani, Ambra A1 - Högl, Birgit A1 - Bialonski, Stephan A1 - Borghammer, Per A1 - Fink, Gereon R. A1 - Schober, Martin A1 - Sommerauer, Michael T1 - RBDtector: an open-source software to detect REM sleep without atonia according to visual scoring criteria JF - Scientific Reports N2 - REM sleep without atonia (RSWA) is a key feature for the diagnosis of rapid eye movement (REM) sleep behaviour disorder (RBD). We introduce RBDtector, a novel open-source software to score RSWA according to established SINBAR visual scoring criteria. We assessed muscle activity of the mentalis, flexor digitorum superficialis (FDS), and anterior tibialis (AT) muscles. RSWA was scored manually as tonic, phasic, and any activity by human scorers as well as using RBDtector in 20 subjects. Subsequently, 174 subjects (72 without RBD and 102 with RBD) were analysed with RBDtector to show the algorithm’s applicability. We additionally compared RBDtector estimates to a previously published dataset. RBDtector showed robust conformity with human scorings. The highest congruency was achieved for phasic and any activity of the FDS. Combining mentalis any and FDS any, RBDtector identified RBD subjects with 100% specificity and 96% sensitivity applying a cut-off of 20.6%. Comparable performance was obtained without manual artefact removal. RBD subjects also showed muscle bouts of higher amplitude and longer duration. RBDtector provides estimates of tonic, phasic, and any activity comparable to human scorings. RBDtector, which is freely available, can help identify RBD subjects and provides reliable RSWA metrics. Y1 - 2022 U6 - http://dx.doi.org/10.1038/s41598-022-25163-9 SN - 2045-2322 VL - 12 IS - Article number: 20886 SP - 1 EP - 14 PB - Springer Nature CY - London 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 - 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 - 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 - 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 - JOUR A1 - Bialonski, Stephan A1 - Wendler, Martin A1 - Lehnertz, Klaus T1 - Unraveling spurious properties of interaction networks with tailored random networks JF - Plos one N2 - We investigate interaction networks that we derive from multivariate time series with methods frequently employed in diverse scientific fields such as biology, quantitative finance, physics, earth and climate sciences, and the neurosciences. Mimicking experimental situations, we generate time series with finite length and varying frequency content but from independent stochastic processes. Using the correlation coefficient and the maximum cross-correlation, we estimate interdependencies between these time series. With clustering coefficient and average shortest path length, we observe unweighted interaction networks, derived via thresholding the values of interdependence, to possess non-trivial topologies as compared to Erdös-Rényi networks, which would indicate small-world characteristics. These topologies reflect the mostly unavoidable finiteness of the data, which limits the reliability of typically used estimators of signal interdependence. We propose random networks that are tailored to the way interaction networks are derived from empirical data. Through an exemplary investigation of multichannel electroencephalographic recordings of epileptic seizures – known for their complex spatial and temporal dynamics – we show that such random networks help to distinguish network properties of interdependence structures related to seizure dynamics from those spuriously induced by the applied methods of analysis. Y1 - 2011 U6 - http://dx.doi.org/10.1371/journal.pone.0022826 VL - 6 IS - 8 PB - Plos CY - San Francisco 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 -