Applying dynamical component analysis to marker-based gait data // Ganganalyse mit Hilfe der dynamischen Komponentenanalyse
Uhl, Christian (2026)
Talk at the MTI Symposium 2026 in Remagen, Germany.
Extracting Dynamical Systems-Based Signals from Noisy and Incomplete Datasets
Uhl, Christian (2026)
Talk at 5th Symposium on Machine Learning and Dynamical Systems (MLDS 5) & Differential Equations for Data Science (DEDS 2026) in Kyoto, Japan.
Applying dynamical component analysis to marker-based gait data: a methodological guide
Orth, Samuel; Roth, Sebastian; Uhl, Christian; Hartmann, Ulrich; Friemert, Daniel (2025)
The European Physical Journal Special Topics.
DOI: 10.1140/epjs/s11734-025-02064-2
Open Access Peer Reviewed
A continuous and robust version of dynamical component analysis
Warmuth, Monika; Werner, Frank; Lämmermann, Nina; Uhl, Christian (2025)
The European Physical Journal Special Topics.
DOI: 10.1140/epjs/s11734-025-01911-6
Peer Reviewed
Comparison of Mode Selection and Reconstructions Obtained by DyCA and DMD with Respect to Noise Robustness and Sampling
Stiehl, Annika; Weeger, Nicolas; Uhl, Christian (2025)
In: Aguiar, A.P., Rocha Malonek, P., Pinto, V.H., Fontes, F.A.C.C., Chertovskih, R. (eds) CONTROLO 2024. Lecture Notes in Electrical Engineering, Springer, Cham 1325, 247–257.
DOI: 10.1007/978-3-031-81724-3_23
Peer Reviewed
Dynamical Component Analysis (DyCA) and Dynamic Mode Decomposition (DMD), both data-driven dimension reduction methods, are introduced. After application to multivariate simulated signals the techniques of mode selection and the resulting amplitudes are compared with respect to noise robustness and sampling periods. The results indicate that DyCA is a useful alternative to DMD and outperforms DMD under certain conditions. These conditions are based on the underlying dynamics in the terms of differential equations and on the noise ratios of the signals.
Comparison of Classical EEG Source Analysis with Deep Learning
Winkler, Jakob; Uhl, Christian; Geißelsöder, Stefan; Erdbrügger, Tim; Wolters, Carsten (2025)
Winkler, Jakob; Uhl, Christian; Geißelsöder, Stefan; Erdbrügger, Tim...
In: Aguiar, A.P., Rocha Malonek, P., Pinto, V.H., Fontes, F.A.C.C., Chertovskih, R. (eds) CONTROLO 2024. CONTROLO 2024. Lecture Notes in Electrical Engineering, Springer, Cham 1325, 258–267.
DOI: 10.1007/978-3-031-81724-3_24
Peer Reviewed
This paper discusses the challenges and methods for source reconstruction of evoked potentials using deep learning in the context of electroencephalography (EEG). We propose the use of deep learning to address known challenges and improve traditional approaches. We explain the creation of a suitable dataset for solving the inverse problem, including the simulation of neural activity and the use of lead field matrices for the forward solution. Furthermore, we undertake a comparative analysis of some initial deep learning models with similar classical methods.
Dynamical Component Analysis (DyCA) Python Code
Stiehl, Annika; Uhl, Christian (2024)
Open Access
DyCA and DMD: Differences and Similarities in Theory and Application
Uhl, Christian; Stiehl, Annika; Weeger, Nicolas (2024)
SIAM Conference on Mathematics of Data Science (MDS24) 2024.
Peer Reviewed
Disentangling dynamic and stochastic modes in multivariate time series
Uhl, Christian; Stiehl, Annika; Weeger, Nicolas; Schlarb, Markus; Hüper, Knut (2024)
Frontiers in Applied Mathematics and Statistics 10.
DOI: 10.3389/fams.2024.1456635
Open Access Peer Reviewed
Cost Function Approach for Dynamical Component Analysis: Full Recovery of Mixing and State Matrix
Hüper, Knut; Schlarb, Markus; Uhl, Christian (2024)
Automation 5 (3), 360-372.
DOI: 10.3390/automation5030022
Open Access Peer Reviewed
A reformulation of the dynamical component analysis (DyCA) via an
optimization-free approach is presented. The original cost function
approach is converted into a numerical linear algebra problem, i.e., the
computation of coupled singular-value decompositions. A simple
algorithm is presented together with numerical experiments to document
the feasability of the approach. This methodology is able to recover the
mixing and state matrices of multivariate signals from high-dimensional
measured data fully.
Signal analysis and classification of interictal epileptiform discharges from EEG with machine learning
Stiehl, Annika; Geißelsöder, Stefan; Anselstetter, Fabienne; Bornfleth, Harald; Ille, Nicole; Uhl, Christian (2023)
Stiehl, Annika; Geißelsöder, Stefan; Anselstetter, Fabienne; Bornfleth, Harald...
Abstracts of the 57th Annual Meeting of the German Society of Biomedical Engineering 68, 1.
DOI: 10.1515/bmte-2023-2001
Open Access Peer Reviewed
Topological Analysis of Low Dimensional Phase Space Trajectories of High Dimensional EEG Signals For Classification of Interictal Epileptiform Discharges
Stiehl, Annika; Flammer, M; Anselstetter, Fabienne; Ille, Nicole; Bornfleth, Harald; Geißelsöder, Stefan; Uhl, Christian (2023)
Stiehl, Annika; Flammer, M; Anselstetter, Fabienne; Ille, Nicole; Bornfleth, Harald...
2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), Rhodes Island, Greece, 1-5.
DOI: 10.1109/ICASSPW59220.2023.10193167
Peer Reviewed
A new topology based feature extraction method for classification of interictal epileptiform discharges (IEDs) in EEG recordings from patients with epilepsy is proposed. After dimension reduction of the recorded EEG signal, using dynamical component analysis (DyCA) or principal component analysis (PCA), a persistent homology analysis of the resulting phase space trajectories is performed. Features are extracted from the persistent homology analysis and used to train and evaluate a support vector machine (SVM). Classification results based on these persistent features are compared with statistical features of the dimension-reduced signals and combinations of all of these features. Combining the persistent and statistical features improves the results (accuracy 94.7 %) compared to using only statistical feature extraction, whereas applying only persistent features does not achieve sufficient performance. For this classification example the choice of the dimension reduction technique does not significantly influence the classification performance of the algorithm.
Robust Dynamical Component Analysis and its Application on Epileptic EEG and Motion Sense Data
Warmuth, Monika; Romberger, Philipp; Uhl, Christian; Hüper, Knut (2022)
Proceedings of the Workshop Biosignal 2022, Dresden, Deutschland.
A Matrix Formulation of Dynamical Component Analysis (DyCA)
Romberger, Philipp; Warmuth, Monika; Uhl, Christian; Hüper, Knut (2022)
Proceedings of the Workshop Biosignal 2022, Dresden.
Different montages and dimension reduction methods for EEG signal analysis of Interictal Epileptic Discharges
Stiehl, Annika; Anselstetter, Fabienne; Ille, Nicole; Bornfleth, Harald; Geißelsöder, Stefan; Uhl, Christian (2022)
Stiehl, Annika; Anselstetter, Fabienne; Ille, Nicole; Bornfleth, Harald...
BMT 2022, Abstracts of the 2022 Joint Annual Conference of the Austrian (ÖGBMT), German (VDE DGBMT) and Swiss (SSBE)Societies for Biomedical Engineering 67 (S1), 88.
DOI: 10.1515/bmt-2022-2001
Open Access Peer Reviewed
Dimension reduction methods, persistent homology ans machine learning for EEG signal analysis of Interictal Epileptic Discharges
Stiehl, Annika; Geißelsöder, Stefan; Ille, Nicole; Anselstetter, Fabienne; Bornfleth, Harald; Uhl, Christian (2022)
Stiehl, Annika; Geißelsöder, Stefan; Ille, Nicole; Anselstetter, Fabienne...
Proceedings of the Workshop Biosignale 2022 (24. - 26.08.2022).
DOI: 10.48550/arXiv.2502.12814
Peer Reviewed
Identification of Low-Dimensional Nonlinear Dynamics from High-Dimensional Simulated and Real-World Data
Paglia, Chiara; Stiehl, Annika; Uhl, Christian (2022)
In: Brito Palma, L., Neves-Silva, R., Gomes, L. (eds) CONTROLO 2022. CONTROLO 2022. Lecture Notes in Electrical Engineering, Springer, Cham 930, 205-213.
DOI: 10.1007/978-3-031-10047-5_18
Peer Reviewed
Dynamical Component Analysis: Matrix Case and Differential Geometric Point of View
Romberger, Philipp; Warmuth, Monika; Uhl, Christian; Hüper, Knut (2022)
In: Brito Palma, L., Neves-Silva, R., Gomes, L. (eds) CONTROLO 2022. CONTROLO 2022. Lecture Notes in Electrical Engineering, Springer, Cham 930, 385-394.
DOI: 10.1007/978-3-031-10047-5_34
Peer Reviewed
Dynamical Component Analysis: Updated and improved algorithm, applications, and limitations
Warmuth, Monika; Romberger, Philipp; Hüper, Knut; Uhl, Christian (2022)
Vortrag auf der International Conference on Time Series and Forecasting (ITISE) 2022, Gran Canaria, Spanien 2022.
Peer Reviewed
A Comparative Study of Dynamic Mode Decomposition (DMD) and Dynamical Component Analysis (DyCA)
Kern, Moritz; Uhl, Christian; Warmuth, Monika (2021)
In: Gonçalves, J.A., Braz-César, M., Coelho, J.P. (eds) CONTROLO 2020. CONTROLO 2020. Lecture Notes in Electrical Engineering, Springer, Cham 695, 93-103.
DOI: 10.1007/978-3-030-58653-9_9
Peer Reviewed
Prof. Dr. Christian Uhl
Hochschule Ansbach
Fakultät Technik
Residenzstr. 8
91522 Ansbach
T 0981 4877-251 christian.uhl[at]hs-ansbach.de