A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series. - Stanislas Chambon; Mathieu Galtier; Pierrick Arnal; Gilles Wainrib; Alexandre Gramfort (CoRR, 2017)

A Deep Learning Architecture to Detect Events in EEG Signals During Sleep. - Stanislas Chambon; Valentin Thorey; Pierrick J. Arnal; Emmanuel Mignot; Alexandre Gramfort (MLSP, 2018)

Domain adaptation with optimal transport improves EEG sleep stage classifiers. - Stanislas Chambon; Mathieu N. Galtier; Alexandre Gramfort (PRNI, 2018)

DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signal. - Stanislas Chambon; Valentin Thorey; Pierrick J. Arnal; Emmanuel Mignot; Alexandre Gramfort (CoRR, 2018)

Towards a Flexible Deep Learning Method for Automatic Detection of Clinically Relevant Multi-Modal Events in the Polysomnogram. - Alexander Neergaard Olesen; Stanislas Chambon; Valentin Thorey; Poul Jennum; Emmanuel Mignot; Helge B. D. Sørensen (EMBC, 2019)

POT: Python Optimal Transport. - Rémi Flamary; Nicolas Courty; Alexandre Gramfort; Mokhtar Z. Alaya; Aurélie Boisbunon; Stanislas Chambon; Laetitia Chapel; Adrien Corenflos; Kilian Fatras; Nemo Fournier; Léo Gautheron; Nathalie T. H. Gayraud; et al. (J. Mach. Learn. Res., 2021)

Insights from an autism imaging biomarker challenge: Promises and threats to biomarker discovery. - Nicolas Traut; Katja Heuer; Guillaume Lemaître; Anita Beggiato; David Germanaud; Monique Elmaleh; Alban Bethegnies; Laurent Bonnasse-Gahot; Weidong Cai 002; Stanislas Chambon; Freddy Cliquet; Ayoub Ghriss; et al. (NeuroImage, 2022)