A reusable benchmark of brain-age prediction from M/EEG resting-state signals. - Denis A. Engemann; Apolline Mellot; Richard Höchenberger; Hubert J. Banville; David Sabbagh; Lukas Gemein; Tonio Ball; Alexandre Gramfort (NeuroImage, 2022)

Novel SPD Matrix Representations Considering Cross-Frequency Coupling for EEG Classification Using Riemannian Geometry. - Maria Sayu Yamamoto; Apolline Mellot; Sylvain Chevallier; Fabien Lotte (EUSIPCO, 2023)

Physics-Informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets. - Apolline Mellot; Antoine Collas; Sylvain Chevallier; Denis A. Engemann; Alexandre Gramfort (EUSIPCO, 2024)

Geodesic Optimization for Predictive Shift Adaptation on EEG data. - Apolline Mellot; Antoine Collas; Sylvain Chevallier; Alexandre Gramfort; Denis A. Engemann (NeurIPS, 2024)

Machine learning and domain adaptation for enhancing the measure of brain health with MEG and EEG signals. (Apprentissage statistique et adaptation de domaine pour l'amélioration de la mesure de la santé cérébrale à partir de signaux MEG et EEG). - Apolline Mellot (2024)