MNE software for processing MEG and EEG data. - Alexandre Gramfort; Martin Luessi; Eric Larson; Denis A. Engemann; Daniel Strohmeier; Christian Brodbeck; Lauri Parkkonen; Matti S. Hämäläinen (NeuroImage, 2014)

Mind the Noise Covariance When Localizing Brain Sources with M/EEG. - Denis A. Engemann; Daniel Strohmeier; Eric Larson; Alexandre Gramfort (PRNI, 2015)

Co-evolutionary behaviour selection in adaptive social networks predicts clustered marginalization of minorities. - Carl-Friedrich Schleussner; Jonathan F. Donges; Denis A. Engemann; Anders Levermann (CoRR, 2015)

Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals. - Denis A. Engemann; Alexandre Gramfort (NeuroImage, 2015)

Neuromagnetic Decomposition of Social Interaction. - Denis A. Engemann (2015)

Automated rejection and repair of bad trials in MEG/EEG. - Mainak Jas; Denis A. Engemann; Federico Raimondo; Yousra Bekhti; Alexandre Gramfort (PRNI, 2016)

Autoreject: Automated artifact rejection for MEG and EEG data. - Mainak Jas; Denis A. Engemann; Yousra Bekhti; Federico Raimondo; Alexandre Gramfort (NeuroImage, 2017)

Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines. - Gaël Varoquaux; Pradeep Reddy Raamana; Denis A. Engemann; Andrés Hoyos Idrobo; Yannick Schwartz; Bertrand Thirion (NeuroImage, 2017)

Self-Supervised Representation Learning from Electroencephalography Signals. - Hubert J. Banville; Graeme Moffat; Isabela Albuquerque; Denis-Alexander Engemann; Aapo Hyvärinen; Alexandre Gramfort (MLSP, 2019)

Manifold-regression to predict from MEG/EEG brain signals without source modeling. - David Sabbagh; Pierre Ablin; Gaël Varoquaux; Alexandre Gramfort; Denis A. Engemann (NeurIPS, 2019)

Uncovering the structure of clinical EEG signals with self-supervised learning. - Hubert J. Banville; Omar Chehab; Aapo Hyvärinen; Denis-Alexander Engemann; Alexandre Gramfort (CoRR, 2020)

Emergence of β and γ networks following multisensory training. - Daria La Rocca; Philippe Ciuciu; Denis-Alexander Engemann; Virginie van Wassenhove (NeuroImage, 2020)

Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states. - David Sabbagh; Pierre Ablin; Gaël Varoquaux; Alexandre Gramfort; Denis A. Engemann (NeuroImage, 2020)

Inference and Prediction Diverge in Biomedicine. - Danilo Bzdok; Denis A. Engemann; Bertrand Thirion (Patterns, 2020)

Learning with self-supervision on EEG data. - Alexandre Gramfort; Hubert J. Banville; Omar Chehab; Aapo Hyvärinen; Denis A. Engemann (BCI, 2021)

Robust learning from corrupted EEG with dynamic spatial filtering. - Hubert J. Banville; Sean U. N. Wood; Chris Aimone; Denis-Alexander Engemann; Alexandre Gramfort (NeuroImage, 2022)

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)

MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. - Stefan Appelhoff; Matthew Sanderson; Teon L. Brooks; Marijn van Vliet; Romain Quentin; Chris Holdgraf; Maximilien Chaumon; Ezequiel Mikulan; Kambiz Tavabi; Richard Höchenberger; Dominik Welke; Clemens Brunner 001; et al. (Zenodo, 2022)

MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. - Stefan Appelhoff; Matthew Sanderson; Teon L. Brooks; Marijn van Vliet; Romain Quentin; Chris Holdgraf; Maximilien Chaumon; Ezequiel Mikulan; Kambiz Tavabi; Richard Höchenberger; Dominik Welke; Clemens Brunner 001; et al. (Zenodo, 2022)

MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. - Stefan Appelhoff; Matthew Sanderson; Teon L. Brooks; Marijn van Vliet; Romain Quentin; Chris Holdgraf; Maximilien Chaumon; Ezequiel Mikulan; Kambiz Tavabi; Richard Höchenberger; Dominik Welke; Clemens Brunner 001; et al. (Zenodo, 2022)

Statistically Valid Variable Importance Assessment through Conditional Permutations. - Ahmad Chamma; Denis A. Engemann; Bertrand Thirion (NeurIPS, 2023)

A systematic review of multimodal brain age studies: Uncovering a divergence between model accuracy and utility. - Robert J. Jirsaraie; Aaron J. Gorelik; Martins M. Gatavins; Denis A. Engemann; Ryan Bogdan; Deanna M. Barch; Aristeidis Sotiras (Patterns, 2023)

Variable Importance in High-Dimensional Settings Requires Grouping. - Ahmad Chamma; Bertrand Thirion; Denis A. Engemann (AAAI, 2024)

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)

Measuring Variable Importance in Individual Treatment Effect Estimation with High Dimensional Data. - Joseph Paillard; Vitaliy Kolodyazhniy; Bertrand Thirion; Denis A. Engemann (CoRR, 2024)

MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. - Stefan Appelhoff; Matthew Sanderson; Teon L. Brooks; Marijn van Vliet; Romain Quentin; Chris Holdgraf; Maximilien Chaumon; Ezequiel Mikulan; Kambiz Tavabi; Richard Höchenberger; Dominik Welke; Clemens Brunner 001; et al. (Zenodo, 2024)

MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. - Stefan Appelhoff; Matthew Sanderson; Teon L. Brooks; Marijn van Vliet; Romain Quentin; Chris Holdgraf; Maximilien Chaumon; Ezequiel Mikulan; Kambiz Tavabi; Richard Höchenberger; Dominik Welke; Clemens Brunner 001; et al. (Zenodo, 2024)

Hierarchical Variable Importance with Statistical Control for Medical Data-Based Prediction. - Joseph Paillard; Antoine Collas; Denis A. Engemann; Bertrand Thirion (IPMI (2), 2025)

Emotion recognition systems with electrodermal activity: From affective science to affective computing. - Tomás A. D'Amelio; Lorenzo Ariel Galán; Emmanuel Alesandro Maldonado; Agustín Ariel Díaz Barquinero; Jerónimo Rodríguez Cuello; Nicolás M. Bruno; Enzo Tagliazucchi; Denis-Alexander Engemann (Neurocomputing, 2025)

GREEN: A lightweight architecture using learnable wavelets and Riemannian geometry for biomarker exploration with EEG signals. - Joseph Paillard; Jörg F. Hipp; Denis A. Engemann (Patterns, 2025)