no code implementations • 5 Apr 2024 • Peter Wassenaar, Pierre Guetschel, Michael Tangermann
In the BCI field, introspection and interpretation of brain signals are desired for providing feedback or to guide rapid paradigm prototyping but are challenging due to the high noise level and dimensionality of the signals.
no code implementations • 27 Mar 2024 • Guido Klein, Pierre Guetschel, Gianluigi Silvestri, Michael Tangermann
Data scarcity in the brain-computer interface field can be alleviated through the use of generative models, specifically diffusion models.
no code implementations • 18 Mar 2024 • Pierre Guetschel, Thomas Moreau, Michael Tangermann
Motivated by the challenge of seamless cross-dataset transfer in EEG signal processing, this article presents an exploratory study on the use of Joint Embedding Predictive Architectures (JEPAs).
1 code implementation • 4 Sep 2023 • Pierre Guetschel, Michael Tangermann
Performance of this transfer approach is then tested on other trials of the receiver dataset.
no code implementations • 20 Jun 2023 • Jan Sosulski, Michael Tangermann
In each trial, for every available letter our approach makes the hypothesis that it is in fact the attended letter, and calculates the ERPs based on each of these hypotheses.
no code implementations • 23 Mar 2023 • Pierre Guetschel, Théodore Papadopoulo, Michael Tangermann
In offline analyses using EEG data of 14 subjects, we tested the embeddings' feasibility and compared their efficiency with state-of-the-art deep learning models and conventional machine learning pipelines.
1 code implementation • IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence, and Neural Engineering (MetroXRAINE) 2022 • Pierre Guetschel, Théodore Papadopoulo, Michael Tangermann
Neurophysiological time-series recordings of brain activity like the electroencephalogram (EEG) or local field potentials can be decoded by machine learning models in order to either control an application, e. g., for communication or rehabilitation after stroke, or to passively monitor the ongoing brain state of the subject, e. g., in a demanding work environment.
1 code implementation • 4 Feb 2022 • Jan Sosulski, Michael Tangermann
Covariance matrices of noisy multichannel electroencephalogram time series data are hard to estimate due to high dimensionality.
no code implementations • 26 Aug 2021 • Jan Sosulski, David Hübner, Aaron Klein, Michael Tangermann
We could show that for 8 out of 13 subjects, the proposed approach using Bayesian optimization succeeded to select the individually optimal SOA out of multiple evaluated SOA values.
no code implementations • 3 Sep 2019 • Henrich Kolkhorst, Wolfram Burgard, Michael Tangermann
Robot motions in the presence of humans should not only be feasible and safe, but also conform to human preferences.
1 code implementation • 27 Apr 2018 • Andreas Meinel, Henrich Kolkhorst, Michael Tangermann
Based on electroencephalography data of 18 healthy subjects, we found that the components' distinct temporal envelope dynamics are highly subject-specific.
no code implementations • 22 Nov 2017 • Sebastian Castaño-Candamil, Andreas Meinel, Michael Tangermann
In the present contribution, we thrive to remove many shortcomings of current simulation frameworks and propose a versatile alternative, that allows for objective evaluation and benchmarking of novel data-driven decoding methods for neural signals.
5 code implementations • 15 Mar 2017 • Robin Tibor Schirrmeister, Jost Tobias Springenberg, Lukas Dominique Josef Fiederer, Martin Glasstetter, Katharina Eggensperger, Michael Tangermann, Frank Hutter, Wolfram Burgard, Tonio Ball
PLEASE READ AND CITE THE REVISED VERSION at Human Brain Mapping: http://onlinelibrary. wiley. com/doi/10. 1002/hbm. 23730/full Code available here: https://github. com/robintibor/braindecode