| MEG and EEG data analysis with MNE-Python A Gramfort, M Luessi, E Larson, DA Engemann, D Strohmeier, ... Frontiers in Neuroinformatics 7, 267, 2013 | 4476 | 2013 |
| MNE software for processing MEG and EEG data A Gramfort, M Luessi, E Larson, DA Engemann, D Strohmeier, ... neuroimage 86, 446-460, 2014 | 2252 | 2014 |
| Assessing and tuning brain decoders: cross-validation, caveats, and guidelines G Varoquaux, PR Raamana, DA Engemann, A Hoyos-Idrobo, Y Schwartz, ... NeuroImage 145, 166-179, 2017 | 805 | 2017 |
| Autoreject: Automated artifact rejection for MEG and EEG data M Jas, DA Engemann, Y Bekhti, F Raimondo, A Gramfort NeuroImage 159, 417-429, 2017 | 639 | 2017 |
| Robust EEG-based cross-site and cross-protocol classification of states of consciousness DA Engemann, F Raimondo, JR King, B Rohaut, G Louppe, F Faugeras, ... Brain 141 (11), 3179-3192, 2018 | 360 | 2018 |
| Uncovering the structure of clinical EEG signals with self-supervised learning H Banville, O Chehab, A Hyvärinen, DA Engemann, A Gramfort Journal of Neural Engineering 18 (4), 046020, 2021 | 324 | 2021 |
| Segregation of the human medial prefrontal cortex in social cognition D Bzdok, R Langner, L Schilbach, DA Engemann, AR Laird, PT Fox, ... Frontiers in human neuroscience 7, 232, 2013 | 274 | 2013 |
| Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals DA Engemann, A Gramfort NeuroImage 108, 328-342, 2015 | 239 | 2015 |
| A reproducible MEG/EEG group study with the MNE software: recommendations, quality assessments, and good practices M Jas, E Larson, DA Engemann, J Leppäkangas, S Taulu, M Hämäläinen, ... Frontiers in neuroscience 12, 530, 2018 | 146 | 2018 |
| Combining magnetoencephalography with magnetic resonance imaging enhances learning of surrogate-biomarkers DA Engemann, O Kozynets, D Sabbagh, G Lemaître, G Varoquaux, ... Elife 9, e54055, 2020 | 114 | 2020 |
| Brain–heart interactions reveal consciousness in noncommunicating patients F Raimondo, B Rohaut, A Demertzi, M Valente, DA Engemann, M Salti, ... Annals of neurology 82 (4), 578-591, 2017 | 109 | 2017 |
| Self-supervised representation learning from electroencephalography signals H Banville, I Albuquerque, A Hyvärinen, G Moffat, DA Engemann, ... 2019 IEEE 29th International Workshop on Machine Learning for Signal …, 2019 | 107 | 2019 |
| Survival and consciousness recovery are better in the minimally conscious state than in the vegetative state F Faugeras, B Rohaut, M Valente, J Sitt, S Demeret, F Bolgert, N Weiss, ... Brain Injury 32 (1), 72-77, 2018 | 107 | 2018 |
| Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states D Sabbagh, P Ablin, G Varoquaux, A Gramfort, DA Engemann NeuroImage 222, 116893, 2020 | 104 | 2020 |
| Inference and prediction diverge in biomedicine D Bzdok, D Engemann, B Thirion Patterns 1 (8), 2020 | 104* | 2020 |
| A reusable benchmark of brain-age prediction from M/EEG resting-state signals DA Engemann, A Mellot, R Höchenberger, H Banville, D Sabbagh, ... Neuroimage 262, 119521, 2022 | 101 | 2022 |
| Mne-python E Larson, A Gramfort, DA Engemann, J Leppakangas, C Brodbeck, M Jas, ... Zenodo, 2024 | 96 | 2024 |
| Combined behavioral and electrophysiological evidence for a direct cortical effect of prefrontal tDCS on disorders of consciousness B Hermann, F Raimondo, L Hirsch, Y Huang, M Denis-Valente, P Pérez, ... Scientific reports 10 (1), 4323, 2020 | 90 | 2020 |
| Children's norm enforcement in their interactions with peers B Köymen, E Lieven, DA Engemann, H Rakoczy, F Warneken, ... Child development 85 (3), 1108-1122, 2014 | 88 | 2014 |
| Automated rejection and repair of bad trials in MEG/EEG M Jas, D Engemann, F Raimondo, Y Bekhti, A Gramfort 2016 international workshop on pattern recognition in neuroimaging (PRNI), 1-4, 2016 | 83 | 2016 |
Alexandre GramfortMetaVerified email at meta.com
