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Denis A. Engemann
Denis A. Engemann
Principal Scientist, Biomarker and Experimental Medicine Leader, F. Hoffmann-La Roche Ltd.
Zweryfikowany adres z roche.com - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
MEG and EEG data analysis with MNE-Python
A Gramfort, M Luessi, E Larson, DA Engemann, D Strohmeier, ...
Frontiers in Neuroinformatics 7, 267, 2013
29892013
MNE software for processing MEG and EEG data
A Gramfort, M Luessi, E Larson, DA Engemann, D Strohmeier, ...
neuroimage 86, 446-460, 2014
18462014
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
6722017
Autoreject: Automated artifact rejection for MEG and EEG data
M Jas, DA Engemann, Y Bekhti, F Raimondo, A Gramfort
NeuroImage 159, 417-429, 2017
4222017
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
2842018
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
2472013
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
2062021
Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals
DA Engemann, A Gramfort
NeuroImage 108, 328-342, 2015
1912015
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
1182018
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
872020
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
872017
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
842020
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
832018
Inference and prediction diverge in biomedicine
D Bzdok, D Engemann, B Thirion
Patterns 1 (8), 2020
81*2020
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
732019
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
722014
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
662020
Manifold-regression to predict from MEG/EEG brain signals without source modeling
D Sabbagh, P Ablin, G Varoquaux, A Gramfort, DA Engemann
arXiv preprint arXiv:1906.02687, 2019
612019
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
562022
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
542016
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