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Alexandre Gramfort

Showing results (31-40 of 73) with videos related to

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Neuroimage|May 23, 2020
Predictive regression modeling with MEG/EEG: from source power to signals and cognitive statesDavid Sabbagh, Pierre Ablin, Gaël Varoquaux, et al.
Plos Computational Biology|February 26, 2024
Methods and considerations for estimating parameters in biophysically detailed neural models with simulation based inferenceNicholas Tolley, Pedro L C Rodrigues, Alexandre Gramfort, et al.
Frontiers in Neuroscience|May 2, 2022
Editorial: From Raw MEG/EEG to Publication: How to Perform MEG/EEG Group Analysis With Free Academic SoftwareArnaud Delorme, Robert Oostenveld, Francois Tadel, et al.
Neuroimage|October 13, 2010
Phase delays within visual cortex shape the response to steady-state visual stimulationBenoit Cottereau, Jean Lorenceau, Alexandre Gramfort, et al.
IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society|April 12, 2018
A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time SeriesStanislas Chambon, Mathieu N Galtier, Pierrick J Arnal, et al.
Imaging Neuroscience (Cambridge, Mass.)|August 13, 2025
Harmonizing and aligning M/EEG datasets with covariance-based techniques to enhance predictive regression modelingApolline Mellot, Antoine Collas, Pedro L C Rodrigues, et al.
Journal of Neural Engineering|June 1, 2019
Deep learning-based electroencephalography analysis: a systematic reviewYannick Roy, Hubert Banville, Isabela Albuquerque, et al.
Cerebral Cortex (New York, N.Y. : 1991)|October 16, 2014
Spatiotemporal Signatures of Lexical-Semantic PredictionEllen F Lau, Kirsten Weber, Alexandre Gramfort, et al.
Neuroimage|February 19, 2022
Robust learning from corrupted EEG with dynamic spatial filteringHubert Banville, Sean U N Wood, Chris Aimone, et al.
Elife|May 20, 2020
Combining magnetoencephalography with magnetic resonance imaging enhances learning of surrogate-biomarkersDenis A Engemann, Oleh Kozynets, David Sabbagh, et al.
Pageof 8

Showing results (31-40 of 73) with videos related to

Sort By:
Pageof 8
Neuroimage|May 23, 2020
Predictive regression modeling with MEG/EEG: from source power to signals and cognitive statesDavid Sabbagh, Pierre Ablin, Gaël Varoquaux, et al.
Plos Computational Biology|February 26, 2024
Methods and considerations for estimating parameters in biophysically detailed neural models with simulation based inferenceNicholas Tolley, Pedro L C Rodrigues, Alexandre Gramfort, et al.
Frontiers in Neuroscience|May 2, 2022
Editorial: From Raw MEG/EEG to Publication: How to Perform MEG/EEG Group Analysis With Free Academic SoftwareArnaud Delorme, Robert Oostenveld, Francois Tadel, et al.
Neuroimage|October 13, 2010
Phase delays within visual cortex shape the response to steady-state visual stimulationBenoit Cottereau, Jean Lorenceau, Alexandre Gramfort, et al.
IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society|April 12, 2018
A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time SeriesStanislas Chambon, Mathieu N Galtier, Pierrick J Arnal, et al.
Imaging Neuroscience (Cambridge, Mass.)|August 13, 2025
Harmonizing and aligning M/EEG datasets with covariance-based techniques to enhance predictive regression modelingApolline Mellot, Antoine Collas, Pedro L C Rodrigues, et al.
Journal of Neural Engineering|June 1, 2019
Deep learning-based electroencephalography analysis: a systematic reviewYannick Roy, Hubert Banville, Isabela Albuquerque, et al.
Cerebral Cortex (New York, N.Y. : 1991)|October 16, 2014
Spatiotemporal Signatures of Lexical-Semantic PredictionEllen F Lau, Kirsten Weber, Alexandre Gramfort, et al.
Neuroimage|February 19, 2022
Robust learning from corrupted EEG with dynamic spatial filteringHubert Banville, Sean U N Wood, Chris Aimone, et al.
Elife|May 20, 2020
Combining magnetoencephalography with magnetic resonance imaging enhances learning of surrogate-biomarkersDenis A Engemann, Oleh Kozynets, David Sabbagh, et al.
Pageof 8