11 citations · 13 across the 3 of their papers we have counts for
4 papers · 1 filter
EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding
Bruno Aristimunha, Dung Truong, Pierre Guetschel +16
Current electroencephalogram (EEG) decoding models are typically trained on small numbers of subjects performing a single task. Here, we introduce a large-scale, code-submission-ba…
Review of Deep Representation Learning Techniques for Brain-Computer Interfaces and Recommendations
Pierre Guetschel, Sara Ahmadi, Michael Tangermann
In the field of brain-computer interfaces (BCIs), the potential for leveraging deep learning techniques for representing electroencephalogram (EEG) signals has gained substantial i…
The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark
Sylvain Chevallier, Igor Carrara, Bruno Aristimunha +6
Objective. This study conduct an extensive Brain-computer interfaces (BCI) reproducibility analysis on open electroencephalography datasets, aiming to assess existing solutions and…
An embedding for EEG signals learned using a triplet loss
Pierre Guetschel, Théodore Papadopoulo, Michael Tangermann
Neurophysiological time series recordings like the electroencephalogram (EEG) or local field potentials are obtained from multiple sensors. They can be decoded by machine learning…