Bastien Pasdeloup — accepted AI-conference papers

2025

REVE: A Foundation Model for EEG - Adapting to Any Setup with Large-Scale Pretraining on 25,000 Subjects

Yassine El Ouahidi, Jonathan Lys, Philipp Thölke, Nicolas Farrugia, Bastien Pasdeloup, Vincent Gripon +2

NeurIPS 2025poster

Foundation models have transformed AI by reducing reliance on task-specific data through large-scale pretraining. While successful in language and vision, their adoption in EEG has lagged due to the heterogeneity of public datasets, which are collected under varying protocols, devices, and electrode…

Cited by 0 Source ↗Scholar ↗

2023

Spatial Graph Signal Interpolation with an Application for Merging BCI Datasets with Various Dimensionalities

Yassine El Ouahidi, Lucas Drumetz, Giulia Lioi, Nicolas Farrugia, Bastien Pasdeloup, Vincent Gripon

ICASSP 2023accepted

BCI Motor Imagery datasets usually are small and have different electrodes setups. When training a Deep Neural Network, one may want to capitalize on all these datasets to increase the amount of data available and hence obtain good generalization results. To this end, we introduce a spatial graph si…

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2016

Towards a characterization of the uncertainty curve for graphs

Bastien Pasdeloup, Vincent Gripon, Grégoire Mercier, Dominique Pastor

ICASSP 2016accepted

Signal processing on graphs is a recent research domain that aims at generalizing classical tools in signal processing, in order to analyze signals evolving on complex domains. Such domains are represented by graphs, for which one can compute a particular matrix, called the normalized Laplacian. It…

Cited by 3 Source ↗Scholar ↗