5 citations · 6 across the 7 of their papers we have counts for
3 papers · 1 filter
Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets
Apolline Mellot, Antoine Collas, Sylvain Chevallier +2
Combining electroencephalogram (EEG) datasets for supervised machine learning (ML) is challenging due to session, subject, and device variability. ML algorithms typically require i…
Weakly supervised covariance matrices alignment through Stiefel matrices estimation for MEG applications
Antoine Collas, Rémi Flamary, Alexandre Gramfort
This paper introduces a novel domain adaptation technique for time series data, called Mixing model Stiefel Adaptation (MSA), specifically addressing the challenge of limited label…
Convolutional Monge Mapping Normalization for learning on sleep data
Théo Gnassounou, Rémi Flamary, Alexandre Gramfort
In many machine learning applications on signals and biomedical data, especially electroencephalogram (EEG), one major challenge is the variability of the data across subjects, ses…