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.
1 code implementation • Journal of Neural Engineering 2024 • Sylvain Chevallier, Igor Carrara, Bruno Aristimunha, Pierre Guetschel, Sara Sedlar, Bruna Lopes, Sebastien Velut, Salim Khazem, Thomas Moreau
The significance of this study lies in its contribution to establishing a rigorous and transparent benchmark for BCI research, offering insights into optimal methodologies and highlighting the importance of reproducibility in driving advancements within the field.
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 • 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.