11 citations · 11 across the 1 of their papers we have counts for
4 papers
Scaling Laws for EEG Decoding: How Much Data Is Enough?
José Maurício Nunes de Oliveira, Bruna J. Lopes, Léo Burgund +2
Deep learning has become a cornerstone of EEG-based brain decoding, with a growing number of architectures proposed every day. However, how the performance of these different model…
Automated Artifact Removal in EEG Age Prediction: A systematic comparison
Davoud Hajhassani, Paul-Adrien Graignic, Bruno Aristimunha +8
Automated EEG artifact removal may improve downstream analysis but can also alter predictive information. We benchmarked nine automated artifact removal methods against a common no…
Learning aligned EEG representations with subject-specific encoders
Bruna J. Lopes, Gabriel Schwartz, Sylvain Chevallier +2
Cross-subject EEG decoding promises more training data, but it also exposes neural networks to strong inter-subject distribution shifts. We study whether task supervision and archi…
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…