Recent Publications

 author = {Moreau, Thomas and Massias, Mathurin and Gramfort, Alexandre and Ablin, Pierre and Charlier, Pierre-Antoine Bannier Benjamin and Dagréou, Mathieu and la Tour, Tom Dupré and Durif, Ghislain and Dantas, Cassio F. and Klopfenstein, Quentin and Larsson, Johan and Lai, En and Lefort, Tanguy and Malézieux, Benoit and Moufad, Badr and Nguyen, Binh T. and Rakotomamonjy, Alain and Ramzi, Zaccharie and Salmon, Joseph and Vaiter, Samuel},
 copyright = {Creative Commons Attribution 4.0 International},
 doi = {10.48550/ARXIV.2206.13424},
 keywords = {Machine Learning (cs.LG), Optimization and Control (math.OC), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Mathematics, FOS: Mathematics},
 publisher = {arXiv},
 title = {Benchopt: Reproducible, efficient and collaborative optimization benchmarks},
 url = {},
 year = {2022}

 abstract = {Background EEG is a common tool for monitoring anaesthetic depth but is rarely reused at large for biomedical research. This study sets out to explore repurposing of EEG during anaesthesia to learn biomarkers of brain ageing.Methods We focused on brain age estimation as an example. Using machine learning, we reanalysed 4-electrodes EEG of 323 patients under propofol and sevoflurane. We included spatio-spectral features from stable anaesthesia for EEG-based age prediction applying recently published reference methods. Anaesthesia was considered stable when 95\% of the total power was below a frequency between 8Hz and 13Hz.Results We considered moderate-risk patients (ASA \<= 2) with propofol anaesthesia to explore predictive EEG signatures. Average alpha-band power (8-13Hz) was informative about age. Yet, state-of-the-art prediction performance was achieved by analysing the entire power spectrum from all electrodes (MAE = 8.2y, R2 = 0.65). Clinical exploration revealed that brain age was systematically linked with intra-operative burst suppression {\textendash} commonly associated with age-related postoperative cognitive issues. Surprisingly, the brain age was negatively correlated with burst suppression in high-risk patients (ASA = 3), pointing at unknown confounding effects. Secondary analyses revealed that brain-age EEG signatures were specific to propofol anaesthesia, reflected by limited prediction performance under sevoflurane and poor cross-drug generalisation.Conclusions EEG from general anaesthesia may enable state-of-the-art brain age prediction. Yet, differences between anaesthetic drugs can impact the effectiveness of repurposing EEG from anaesthesia. To unleash the dormant potential of repurposing EEG-monitoring for clinical and health research, collecting larger datasets with precisely documented drug dosage will be key enabling factors.Competing Interest StatementD.E. is a full-time employee of F. Hoffmann-La Roche Ltd.Clinical TrialNCT03876379Funding StatementThis work was supported by a 2018 "medecine numerique" (for digital medicine) thesis grant issued by Inserm (French national institute of health and medical research) and Inria (French national research institute for the digital sciences). It was also partly supported by the European Research Council Starting Grant SLAB ERC-StG-676943.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:The Ethics Advisory Committee (Chairperson Dr Jean Reignier, 48, avenue Claude Vellefaux, Paris, France) gave approval to this work on the 5 January 2016, under the reference CE SRLF 11-356. The SRLF is the French national academic society for anaesthesia and critical care consulted by the department of anaesthesiology at the Lariboisiere hospital (Paris, France).I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll data produced in the present study are available upon reasonable request to the authors.},
 author = {Sabbagh, David and Cartailler, J{\'e}r{\^o}me and Touchard, Cyril and Joachim, Jona and Mebazaa, Alexandre and Vall{\'e}e, Fabrice and Gayat, {\'E}tienne and Gramfort, Alexandre and Engemann, Denis A.},
 doi = {10.1101/2022.05.05.22274610},
 elocation-id = {2022.05.05.22274610},
 eprint = {},
 journal = {medRxiv},
 publisher = {Cold Spring Harbor Laboratory Press},
 title = {Repurposing EEG monitoring of general anaesthesia for building biomarkers of brain ageing: An exploratory study},
 url = {},
 year = {2022}

 author = {Janati, Hicham and Cuturi, Marco and Gramfort, Alexandre},
 copyright = {Creative Commons Attribution 4.0 International},
 doi = {10.48550/ARXIV.2203.05813},
 keywords = {Machine Learning (stat.ML), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
 publisher = {arXiv},
 title = {Averaging Spatio-temporal Signals using Optimal Transport and Soft Alignments},
 url = {},
 year = {2022}

 author = {Chehab, Omar and Gramfort, Alexandre and Hyvarinen, Aapo},
 booktitle = {The 38th Conference on Uncertainty in Artificial Intelligence},
 copyright = { perpetual, non-exclusive license},
 title = {The Optimal Noise in Noise-Contrastive Learning Is Not What You Think},
 url = {},
 year = {2022}

 abstract = {Building machine learning models using EEG recorded outside of the laboratory setting requires methods robust to noisy data and randomly missing channels. This need is particularly great when working with sparse EEG montages (1–6 channels), often encountered in consumer-grade or mobile EEG devices. Neither classical machine learning models nor deep neural networks trained end-to-end on EEG are typically designed or tested for robustness to corruption, and especially to randomly missing channels. While some studies have proposed strategies for using data with missing channels, these approaches are not practical when sparse montages are used and computing power is limited (e.g., wearables, cell phones). To tackle this problem, we propose dynamic spatial filtering (DSF), a multi-head attention module that can be plugged in before the first layer of a neural network to handle missing EEG channels by learning to focus on good channels and to ignore bad ones. We tested DSF on public EEG data encompassing ∼4000 recordings with simulated channel corruption and on a private dataset of ∼100 at-home recordings of mobile EEG with natural corruption. Our proposed approach achieves the same performance as baseline models when no noise is applied, but outperforms baselines by as much as 29.4% accuracy when significant channel corruption is present. Moreover, DSF outputs are interpretable, making it possible to monitor the effective channel importance in real-time. This approach has the potential to enable the analysis of EEG in challenging settings where channel corruption hampers the reading of brain signals.},
 author = {Banville, Hubert and Wood, Sean U.N. and Aimone, Chris and Engemann, Denis-Alexander and Gramfort, Alexandre},
 doi = {},
 issn = {1053-8119},
 journal = {NeuroImage},
 keywords = {Electroencephalography, Mobile EEG, Deep learning, Machine learning, Noise robustness},
 pages = {118994},
 title = {Robust learning from corrupted {EEG} with dynamic spatial filtering},
 url = {},
 volume = {251},
 year = {2022}

 author = {Rockhill, Alexander P. and Larson, Eric and Stedelin, Brittany and Mantovani, Alessandra and Raslan, Ahmed M. and Gramfort, Alexandre and Swann, Nicole C.},
 doi = {10.21105/joss.03897},
 journal = {Journal of Open Source Software},
 number = {70},
 pages = {3897},
 publisher = {The Open Journal},
 title = {Intracranial Electrode Location and Analysis in MNE-Python},
 url = {},
 volume = {7},
 year = {2022}

 abstract = {Population-level modeling can define quantitative measures of individual aging by applying machine learning to large volumes of brain images. These measures of brain age, obtained from the general population, helped characterize disease severity in neurological populations, improving estimates of diagnosis or prognosis. Magnetoencephalography (MEG) and Electroencephalography (EEG) have the potential to further generalize this approach towards prevention and public health by enabling assessments of brain health at large scales in socioeconomically diverse environments. However, more research is needed to define methods that can handle the complexity and diversity of M/EEG signals across diverse real-world contexts. To catalyse this effort, here we propose reusable benchmarks of competing machine learning approaches for brain age modeling. We benchmarked popular classical machine learning pipelines and deep learning architectures previously used for pathology decoding or brain age estimation in 4 international M/EEG cohorts from diverse countries and cultural contexts, including recordings from more than 2500 participants. Our benchmarks were built on top of the M/EEG adaptations of the BIDS standard, providing tools that can be applied with minimal modification on any M/EEG dataset provided in the BIDS format. Our results suggest that, regardless of whether classical machine learning or deep learning was used, the highest performance was reached by pipelines and architectures involving spatially aware representations of the M/EEG signals, leading to R^2 scores between 0.60-0.71. Hand-crafted features paired with random forest regression provided robust benchmarks even in situations in which other approaches failed. Taken together, this set of benchmarks, accompanied by open-source software and high-level Python scripts, can serve as a starting point and quantitative reference for future efforts at developing M/EEG-based measures of brain aging. The generality of the approach renders this benchmark reusable for other related objectives such as modeling specific cognitive variables or clinical endpoints.Highlights- We provide systematic reusable benchmarks for brain age from M/EEG signals- The benchmarks were carried out on M/EEG from four countries \> 2500 recordings- We compared machine learning pipelines capable of handling the non-linear regression task of relating biomedical outcomes to M/EEG dynamics, based on classical machine learning and deep learning- Next to data-driven methods we benchmarked template-based source localization as a practical tool for generating features less affected by electromagnetic field spread- The benchmarks are built on top of the MNE ecosystem and the braindecode package and can be applied on any M/EEG dataset presented in the BIDS formatCompeting Interest StatementD.E. is a full-time employee of F. Hoffmann-La Roche Ltd. H.B. receives graduate funding support from InteraXon Inc.},
 author = {Engemann, Denis A. and Mellot, Apolline and H{\"o}chenberger, Richard and Banville, Hubert and Sabbagh, David and Gemein, Lukas and Ball, Tonio and Gramfort, Alexandre},
 doi = {10.1101/2021.12.14.472691},
 elocation-id = {2021.12.14.472691},
 eprint = {},
 journal = {bioRxiv},
 publisher = {Cold Spring Harbor Laboratory},
 title = {A reusable benchmark of brain-age prediction from M/EEG resting-state signals},
 url = {},
 year = {2021}

Full list of publications

Short Bio

I am currently senior research scientist at Meta Reality Labs in Paris. Previously I was senior researcher (DR, HDR) at Inria, leading the MIND Team, known formerly as Parietal. My work is on statistical machine learning, signal and image processing, optimization, scientific computing and software engineering with primary applications in neuroscience and biosignal processing. Before joining Inria, I was an assistant professor for 5 years at Telecom Paris in the signal processing and machine learning department and before I was at the Martinos Center for Biomedical Imaging at Harvard University in Boston. I was also a co-director of the Center for Data Science at Université Paris-Saclay. In 2015, I got an ERC Starting Grant for my project called Signal and Learning Applied to Brain data (SLAB) and in 2019 a grant ANR Chaire on Artificial Intelligence called BrAIN.


Email @ Inria:

Email @ Meta:


  • scikit-learn - A Python project for machine learning.
  • MNE - A complete package to process EEG and MEG data: forward and inverse problems, preprocessing, stats, time-frequency analysis.
  • openmeeg - C++ package for low-frequency bio-electromagnetism including the EEG/MEG forward problem. OpenMEEG implements the Symmetric BEM which has shown to provide very accurate solutions. Some features: parallel processing, Python Bindings, Matlab integration with Fieldtrip and BrainStorm.

More on my Github Page

Ex- Inria Team

With my academic activities, I work closely with the following people:


PhD Students