| Scikit-learn: Machine learning in Python F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, ... the Journal of machine Learning research 12, 2825-2830, 2011 | 37141 | 2011 |
| API design for machine learning software: experiences from the scikit-learn project L Buitinck, G Louppe, M Blondel, F Pedregosa, A Mueller, O Grisel, ... arXiv preprint arXiv:1309.0238, 2013 | 1130 | 2013 |
| MNE software for processing MEG and EEG data A Gramfort, M Luessi, E Larson, DA Engemann, D Strohmeier, ... Neuroimage 86, 446-460, 2014 | 949 | 2014 |
| MEG and EEG data analysis with MNE-Python A Gramfort, M Luessi, E Larson, DA Engemann, D Strohmeier, ... Frontiers in neuroscience 7, 267, 2013 | 815 | 2013 |
| Machine learning for neuroimaging with scikit-learn A Abraham, F Pedregosa, M Eickenberg, P Gervais, A Mueller, J Kossaifi, ... Frontiers in neuroinformatics 8, 14, 2014 | 665 | 2014 |
| OpenMEEG: opensource software for quasistatic bioelectromagnetics A Gramfort, T Papadopoulo, E Olivi, M Clerc Biomedical engineering online 9 (1), 1-20, 2010 | 654 | 2010 |
| Brain covariance selection: better individual functional connectivity models using population prior G Varoquaux, A Gramfort, JB Poline, B Thirion arXiv preprint arXiv:1008.5071, 2010 | 382 | 2010 |
| Large scale screening of neural signatures of consciousness in patients in a vegetative or minimally conscious state JD Sitt, JR King, I El Karoui, B Rohaut, F Faugeras, A Gramfort, L Cohen, ... Brain 137 (8), 2258-2270, 2014 | 291 | 2014 |
| Local and long-range functional connectivity is reduced in concert in autism spectrum disorders S Khan, A Gramfort, NR Shetty, MG Kitzbichler, S Ganesan, JM Moran, ... Proceedings of the National Academy of Sciences 110 (8), 3107-3112, 2013 | 246 | 2013 |
| Time-frequency mixed-norm estimates: Sparse M/EEG imaging with non-stationary source activations A Gramfort, D Strohmeier, J Haueisen, MS Hämäläinen, M Kowalski NeuroImage 70, 410-422, 2013 | 202 | 2013 |
| Deep learning-based electroencephalography analysis: a systematic review Y Roy, H Banville, I Albuquerque, A Gramfort, TH Falk, J Faubert arXiv preprint arXiv:1901.05498, 2019 | 196 | 2019 |
| Mixed-norm estimates for the M/EEG inverse problem using accelerated gradient methods A Gramfort, M Kowalski, M Hämäläinen Physics in Medicine & Biology 57 (7), 1937, 2012 | 193 | 2012 |
| A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series S Chambon, MN Galtier, PJ Arnal, G Wainrib, A Gramfort IEEE Transactions on Neural Systems and Rehabilitation Engineering 26 (4 …, 2018 | 191 | 2018 |
| Multi-subject dictionary learning to segment an atlas of brain spontaneous activity G Varoquaux, A Gramfort, F Pedregosa, V Michel, B Thirion Biennial International Conference on information processing in medical …, 2011 | 177 | 2011 |
| Scikit-learn: Machine learning without learning the machinery G Varoquaux, L Buitinck, G Louppe, O Grisel, F Pedregosa, A Mueller GetMobile: Mobile Computing and Communications 19 (1), 29-33, 2015 | 167 | 2015 |
| Seeing it all: Convolutional network layers map the function of the human visual system M Eickenberg, A Gramfort, G Varoquaux, B Thirion NeuroImage 152, 184-194, 2017 | 158 | 2017 |
| Total variation regularization for fMRI-based prediction of behavior V Michel, A Gramfort, G Varoquaux, E Eger, B Thirion IEEE transactions on medical imaging 30 (7), 1328-1340, 2011 | 157 | 2011 |
| Single-trial decoding of auditory novelty responses facilitates the detection of residual consciousness JR King, F Faugeras, A Gramfort, A Schurger, I El Karoui, JD Sitt, ... Neuroimage 83, 726-738, 2013 | 130 | 2013 |
| Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals DA Engemann, A Gramfort NeuroImage 108, 328-342, 2015 | 120 | 2015 |
| A supervised clustering approach for fMRI-based inference of brain states V Michel, A Gramfort, G Varoquaux, E Eger, C Keribin, B Thirion Pattern Recognition 45 (6), 2041-2049, 2012 | 115 | 2012 |
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