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Books and Theses
- 2021
[b1]David Sabbagh:
Building clinical biomarkers from cerebral electrophysiology ˸ Brain Age as a measure of neurocognitive disorders. (Construction de biomarqueurs cliniques à partir de l'electrophysiologie cérébrale ˸ l'âge du cerveau comme mesure des troubles neurocognitifs). University of Paris-Saclay, France, 2021
Journal Articles
- 2022
- [j3]Denis A. Engemann
, Apolline Mellot, Richard Höchenberger, Hubert J. Banville, David Sabbagh, Lukas Gemein, Tonio Ball, Alexandre Gramfort:
A reusable benchmark of brain-age prediction from M/EEG resting-state signals. NeuroImage 262: 119521 (2022) - 2020
- [j2]David Sabbagh, Pierre Ablin, Gaël Varoquaux, Alexandre Gramfort, Denis A. Engemann:
Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states. NeuroImage 222: 116893 (2020) - 2003
- [j1]Marcel Brun, David Sabbagh, Seungchan Kim, Edward R. Dougherty:
Corrected Small-sample Estimation of the Bayes Error. Bioinform. 19(8): 944-951 (2003)
Conference and Workshop Papers
- 2019
- [c2]David Sabbagh, Pierre Ablin, Gaël Varoquaux, Alexandre Gramfort, Denis A. Engemann:
Manifold-regression to predict from MEG/EEG brain signals without source modeling. NeurIPS 2019: 7321-7332 - 2000
- [c1]David L. Sabbagh, Edward R. Dougherty:
Small-sample estimation of the error of the optimal binary filter. EUSIPCO 2000: 1-4
Informal and Other Publications
- 2019
- [i1]David Sabbagh, Pierre Ablin, Gaël Varoquaux, Alexandre Gramfort, Denis A. Engemann:
Manifold-regression to predict from MEG/EEG brain signals without source modeling. CoRR abs/1906.02687 (2019)
Coauthor Index
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