@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{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{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{BialonskiCaronSchloenetal.2016, author = {Bialonski, Stephan and Caron, David A. and Schloen, Julia and Feudel, Ulrike and Kantz, Holger and Moorthi, Stefanie D.}, title = {Phytoplankton dynamics in the Southern California Bight indicate a complex mixture of transport and biology}, series = {Journal of Plankton Research}, volume = {38}, journal = {Journal of Plankton Research}, number = {4}, publisher = {Oxford University Press}, address = {Oxford}, issn = {1464-3774}, doi = {10.1093/plankt/fbv122}, pages = {1077 -- 1091}, year = {2016}, abstract = {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.}, 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{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} } @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{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{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{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} } @article{BialonskiWendlerLehnertz2011, author = {Bialonski, Stephan and Wendler, Martin and Lehnertz, Klaus}, title = {Unraveling spurious properties of interaction networks with tailored random networks}, series = {Plos one}, volume = {6}, journal = {Plos one}, number = {8}, publisher = {Plos}, address = {San Francisco}, doi = {10.1371/journal.pone.0022826}, pages = {e22826}, year = {2011}, abstract = {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 Erd{\"o}s-R{\´e}nyi 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.}, language = {en} } @article{BornheimGriegerBlanecketal.2024, author = {Bornheim, Tobias and Grieger, Niklas and Blaneck, Patrick Gustav and Bialonski, Stephan}, title = {Speaker Attribution in German Parliamentary Debates with QLoRA-adapted Large Language Models}, series = {Journal for language technology and computational linguistics : JLCL}, volume = {37}, journal = {Journal for language technology and computational linguistics : JLCL}, number = {1}, publisher = {Gesellschaft f{\"u}r Sprachtechnologie und Computerlinguistik}, address = {Regensburg}, issn = {2190-6858}, doi = {10.21248/jlcl.37.2024.244}, pages = {13 Seiten}, year = {2024}, abstract = {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.}, 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{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{GriegerSchwabedalWendeletal.2021, author = {Grieger, Niklas and Schwabedal, Justus T. C. and Wendel, Stefanie and Ritze, Yvonne and Bialonski, Stephan}, title = {Automated scoring of pre-REM sleep in mice with deep learning}, series = {Scientific Reports}, volume = {11}, journal = {Scientific Reports}, number = {Art. 12245}, publisher = {Springer Nature}, address = {London}, issn = {2045-2322}, doi = {10.1038/s41598-021-91286-0}, year = {2021}, abstract = {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.}, language = {en} } @article{HorstmannBialonskiNoenningetal.2010, author = {Horstmann, Marie-Therese and Bialonski, Stephan and Noenning, Nina and Mai, Heinke and Prusseit, Jens and Wellmer, J{\"o}rg and Hinrichs, Hermann and Lehnertz, Klaus}, title = {State dependent properties of epileptic brain networks: Comparative graph-theoretical analyses of simultaneously recorded EEG and MEG}, series = {Clinical Neurophysiology}, volume = {121}, journal = {Clinical Neurophysiology}, number = {2}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1388-2457}, doi = {10.1016/j.clinph.2009.10.013}, pages = {172 -- 185}, year = {2010}, abstract = {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.}, 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} } @article{KaulenSchwabedalSchneideretal.2022, author = {Kaulen, Lars and Schwabedal, Justus T. C. and Schneider, Jules and Ritter, Philipp and Bialonski, Stephan}, title = {Advanced sleep spindle identification with neural networks}, series = {Scientific Reports}, volume = {12}, journal = {Scientific Reports}, number = {Article number: 7686}, publisher = {Springer Nature}, address = {London}, issn = {2045-2322}, doi = {10.1038/s41598-022-11210-y}, pages = {1 -- 10}, year = {2022}, abstract = {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.}, language = {en} } @article{KuhnertBialonskiNoenningetal.2013, author = {Kuhnert, Marie-Therese and Bialonski, Stephan and Noenning, Nina and Mai, Heinke and Hinrichs, Hermann and Helmstaedter, Christoph and Lehnertz, Klaus}, title = {Incidental and intentional learning of verbal episodic material differentially modifies functional brain networks}, series = {Plos one}, volume = {8}, journal = {Plos one}, number = {11}, publisher = {PLOS}, address = {San Francisco}, doi = {10.1371/journal.pone.0080273}, pages = {e80273}, year = {2013}, abstract = {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.}, 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} }