Théo Gnassounou

According to our database, Théo Gnassounou authored at least 10 papers between 2021 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

Braindecode: toolbox for decoding raw electrophysiological brain data with deep learning models.

Bruno Aristimunha, Pierre Guetshel, Martin Wimpff, Lukas Gemein, Cédric Rommel, Hubert J. Banville, Maciej Sliwowski, Daniel Wilson, Simon Brandt, Théo Gnassounou, Joseph Paillard, Bruna Lopes, Sara Sedlar, Thomas Moreau, Sylvain Chevallier, Alexandre Gramfort, Robin Tibor Schirrmeister

Dataset, December, 2025

Leveraging Generic Time Series Foundation Models for EEG Classification.

Théo Gnassounou, Yessin Moakher, Shifeng Xie, Vasilii Feofanov, Ievgen Redko

CoRR, October, 2025

PSDNorm: Test-Time Temporal Normalization for Deep Learning on EEG Signals.

Théo Gnassounou, Antoine Collas, Rémi Flamary, Alexandre Gramfort

CoRR, March, 2025

Multi-source domain adaptation for learning on biosignals. (Adaptation multi-sources pour l'apprentissage à partir de signaux biologiques).

Théo Gnassounou

PhD thesis, 2025

SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation On Diverse Modalities.

Yanis Lalou, Théo Gnassounou, Antoine Collas, Antoine de Mathelin, Ambroise Odonnat, Thomas Moreau, Alexandre Gramfort, Rémi Flamary

Trans. Mach. Learn. Res., 2025

2024

Multi-Source and Test-Time Domain Adaptation on Multivariate Signals using Spatio-Temporal Monge Alignment.

Théo Gnassounou, Antoine Collas, Rémi Flamary, Karim Lounici, Alexandre Gramfort

CoRR, 2024

SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation.

Yanis Lalou, Théo Gnassounou, Antoine Collas, Antoine de Mathelin, Oleksii Kachaiev, Ambroise Odonnat, Alexandre Gramfort, Thomas Moreau, Rémi Flamary

CoRR, 2024

2023

Convolutional Monge Mapping Normalization for learning on biosignals.

Théo Gnassounou, Rémi Flamary, Alexandre Gramfort

CoRR, 2023

Convolution Monge Mapping Normalization for learning on sleep data.

Théo Gnassounou, Rémi Flamary, Alexandre Gramfort

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2021

Adaptive Subsampling of Multidomain Signals with Product Graphs.

Théo Gnassounou, Pierre Humbert, Laurent Oudre

Proceedings of the IEEE International Conference on Acoustics, 2021