Alexandre Gramfort
Affiliations: | IDS | École Normale Supérieure de Télécommunications - ENST-Paris |
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Tolley N, Rodrigues PLC, Gramfort A, et al. (2023) Methods and considerations for estimating parameters in biophysically detailed neural models with simulation based inference. Biorxiv : the Preprint Server For Biology |
Pernet C, Garrido MI, Gramfort A, et al. (2020) Issues and recommendations from the OHBM COBIDAS MEEG committee for reproducible EEG and MEG research. Nature Neuroscience |
Sabbagh D, Ablin P, Varoquaux G, et al. (2020) Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states. Neuroimage. 222: 116893 |
Janati H, Bazeille T, Thirion B, et al. (2020) Multi-subject MEG/EEG source imaging with sparse multi-task regression. Neuroimage. 220: 116847 |
Engemann DA, Kozynets O, Sabbagh D, et al. (2020) Combining magnetoencephalography with magnetic resonance imaging enhances learning of surrogate-biomarkers. Elife. 9 |
Jaiswal A, Nenonen J, Stenroos M, et al. (2020) Comparison of beamformer implementations for MEG source localization. Neuroimage. 116797 |
Appelhoff S, Sanderson M, Brooks TL, et al. (2019) MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software. 4 |
Roy Y, Banville H, Albuquerque I, et al. (2019) Deep learning-based electroencephalography analysis: a systematic review. Journal of Neural Engineering |
Grabot L, Kononowicz TW, Dupré la Tour T, et al. (2019) The strength of alpha-beta oscillatory coupling predicts motor timing precision. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience |
Appelhoff S, Sanderson M, Brooks T, et al. (2019) MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis Journal of Open Source Software. 4: 1896 |