About Me#
I am a passionate French engineer and post-doctoral researcher focused on transforming neuroscience research into practical solutions for society. I believe that achieving this requires scientific outputs to be reproducible and scalable, which drives my work in two key areas:
EEG Neurofeedback: Leveraging EEG, the most affordable neuroimaging method, to develop neurofeedback interventions that could benefit both clinical and non-clinical populations.
Reproducible and Scalable Neuroimaging Pipelines: Building robust analysis pipelines to ensure reproducibility and scalability in neuroimaging research.
My work is shaped by collaborations with four different labs, allowing me to push the boundaries in both fields. With the Sleep and Cognition Neuroimaging Lab (Prof. Sophie Schwartz) for fMRI and the Neuroling Lab (Prof. Alexis Hervais-Adelman) for MEG, I develop and apply advanced analysis pipelines using industry-grade technologies such as Docker and GitHub integration.
In collaboration with Dr. Tomas Ros as well as the Laboratory for Behavioral Neurology and Imaging of Cognition (Prof. Patrik Vuilleumier), I continue developing EEG neurofeedback paradigms. Pursuing my PhD work, I’m designing clinical trials to test the feasibility and efficacy of new home-based EEG neurofeedback paradigms.
A core part of my mission is to make the tools and techniques I develop accessible to as many people as possible. I believe that democratizing access to research tools is key to advancing our understanding of the brain. This is why I contribute to the open-source Python ecosystem, working on libraries like nipype and MNE, as well as creating and sharing my own libraries, Pycartool and Pycrostates.
Work Experiences#
Develop a home-based EEG neurofeedback software, including real-time data acquisition, artifact correction, and signal processing.
Design a cloud-based data management and analysis solution for remote monitoring.
Support research labs in data management, curation, and the analysis of large-scale datasets from various neuroimaging modalities (MEG, EEG, fMRI).
Provide automated workflows for analyzing neuro-imaging data.
Provide services for curation, preprocessing and analysis of fMRI, EEG and iEEG recordings.
PhD research project on Training spatio-temporally defined brain activity patterns with EEG neurofeedback.
Designed and conducted a clinical trial to evaluate the feasibility of a novel EEG microstate-based neurofeedback protocol.
Development of open-source python libraries for EEG microstates analysis.
Support neuroscience researchers of the Functional Brain Mapping Lab at Campus Biotech
Developed combined EEG/MRI analysis pipeline for EEG source reconstruction.
Developed user-friendly Graphical user interfaces for EEG data preprocessing including filters, resampling, ICA processing, interpolation, referencing…
In depth study of artefact cleaning algorithm by mean of large-scale EEG simulation.
Setting up a neurofeedback paradigm for tinnitus treatment (real time):
Creation and optimization of bad channel interpolation and artefact rejection algorithms (ICA)
Offline EEG data analysis of tinnitus related EEG (offline):
Design a test bench for EEG connected devices
Improve ERP integration and stock prediction
Developed software for label printing
Regulated the production flow in a wheel manufacturing company.
Education#
Lemanic Neurosicences Doctoral School
Bioengineering and Innovation in Neurosciences (Paristech)
Arts et Métiers