Categories

All (22)

AAAI (1)

Alzheimer's Diseas (1)

EEG (7)

M/EEG (8)

MEG (1)

MRI (5)

NeurIPS (2)

Riemannian geometry (3)

age (1)

anaesthesia (1)

biomarker (4)

brain age (10)

burts suppression (1)

channel interpolation (1)

cognitive decline (1)

covariance estimation (1)

deep learning (3)

dementia (1)

denoising (1)

disorders of consciousness (1)

domain adaptation (1)

dynamic programming (1)

explainable AI (2)

interpretable ML (2)

machine learning (13)

mobile EEG (2)

multimodal (4)

multiple sclerosis (1)

neurology (2)

neuroscience (22)

open-source software (1)

paper (22)

preprint (1)

preprocessing (1)

pretext tasks (1)

propofol (1)

published (21)

regularization (1)

review (1)

self supervised (1)

sevoflurane (1)

sex (1)

sleep (1)

spatial filtering (3)

spatial whitening (1)

Full Bibliography

For a comprehensive list of my publications, please see my google scholar and ORCID pages.

Selected Publications

Peer-reviewed Articles

Exploring the neuromagnetic signatures of cognitive decline from mild cognitive impairment to Alzheimer’s disease dementia

Sinead Gaubert, Pilar Garces, Jörg F Hipp, Ricardo Bruña, Maria Eugenia Lopéz, Fernando Maestu, Delshad Vaghari, Rik Henson, Claire Paquetand Denis A Engemann

GREEN: A lightweight architecture for biomarker exploration with EEG signals

Joseph Paillard, Joerg F Hipp and Denis A Engemann

Machine learning of brain-specific biomarkers from EEG

Philipp Bomatter, Joseph Paillard, Pilar Garces, Joerg F Hipp and Denis A Engemann

Variable Importance in High-Dimensional Settings Requires Grouping

Ahmad Chamma, Bertrand Thirion and Denis A. Engemann

Statistically Valid Variable Importance Assessment through Conditional Permutations

Ahmad Chamma, Denis A. Engemann and Bertrand Thirion

Repurposing electroencephalogram monitoring of general anaesthesia for building biomarkers of brain ageing: an exploratory study

Sabbagh, David, Jérôme Cartailler, Cyril Touchard, Jona Joachim, Alexandre Mebazaa, Fabrice Vallée, Étienne Gayat, Alexandre Gramfort, and Denis A. Engemann.

Harmonizing and aligning M/EEG datasets with covariance-based techniques to enhance predictive regression modeling

Mellot, Apolline, Antoine Collas, Pedro LC Rodrigues, Denis A. Engemann, and Alexandre Gramfort

A systematic review of multimodal brain age studies: Uncovering a divergence between model accuracy and utility

Jirsaraie, Robert J., Aaron J. Gorelik, Martins M. Gatavins, Denis A. Engemann, Ryan Bogdan, Deanna M. Barch, and Aristeidis Sotiras.

A reusable benchmark of brain-age prediction from M/EEG resting-state signals

Engemann, Denis A., Apolline Mellot, Richard Höchenberger, Hubert Banville, David Sabbagh, Lukas Gemein, Tonio Ball, and Alexandre Gramfort

Predicting future cognitive decline from non-brain and multimodal brain imaging data in healthy and pathological aging

Vieira, Bruno Hebling, Franziskus Liem, Kamalaker Dadi, Denis A. Engemann, Alexandre Gramfort, Pierre Bellec, Richard Cameron Craddock et al.

Brain age as a surrogate marker for cognitive performance in multiple sclerosis

Denissen, Stijn, Denis Alexander Engemann, Alexander De Cock, Lars Costers, Johan Baijot, Jorne Laton, Iris‐Katharina Penner et al.

Robust learning from corrupted EEG with dynamic spatial filtering

Banville, Hubert, Sean UN Wood, Chris Aimone, Denis-Alexander Engemann, and Alexandre Gramfort.

Population modeling with machine learning can enhance measures of mental health

Dadi, Kamalaker, Gaël Varoquaux, Josselin Houenou, Danilo Bzdok, Bertrand Thirion, and Denis Engemann.

Uncovering the structure of clinical EEG signals with self-supervised learning

Banville, Hubert, Omar Chehab, Aapo Hyvärinen, Denis-Alexander Engemann, and Alexandre Gramfort.

Combining magnetoencephalography with magnetic resonance imaging enhances learning of surrogate-biomarkers

Engemann, Denis Alexander, Oleh Kozynets, David Sabbagh, Guillaume Lemaitre, Gaël Varoquaux, Franziskus Liem, and Alexandre Gramfort

Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states

Sabbagh, David, Pierre Ablin, Gaël Varoquaux, Alexandre Gramfort, and Denis A. Engemann.

Manifold-regression to predict from MEG/EEG brain signals without source modeling

Sabbagh, David, Pierre Ablin, Gaël Varoquaux, Alexandre Gramfort, and Denis A. Engemann.

Combining magnetoencephalography with magnetic resonance imaging enhances learning of surrogate-biomarkers

Engemann, Denis A., Federico Raimondo, Jean-Rémi King, Benjamin Rohaut, Gilles Louppe, Frédéric Faugeras, Jitka Annen, … & Jacobo Sitt

Autoreject: Automated artifact rejection for MEG and EEG data

Jas, M, Denis A Engemann, Yousra Bekhti, Federico Raimondo, Alexandre Gramfort

Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals

Engemann, Denis A., Alexandre Gramfort

Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals

Gramfort, A., Martin Luessi, Eric Larson, Denis A Engemann, Daniel Strohmeier, Christian Brodbeck, Lauri Parkkonen, Matti S Hämäläinen

Preprints

Do try this at home: Age prediction from sleep and meditation with large-scale low-cost mobile EEG

Banville, Hubert, Maurice Abou Jaoude, Sean UN Wood, Chris Aimone, Sebastian C. Holst, Alexandre Gramfort, and Denis-Alexander Engemann.