I am honored to receive the BCI Society Early Career Award 2025! Sincere thanks to the selection committee of the BCI Society for this recognition—it’s truly humbling to be acknowledged by a community I admire so much! This would not have been possible without the incredible support and collaboration of colleagues at NERV Lab, Centre Inria de Paris and Institut du Cerveau – Paris Brain Institute, as well as mentors and peers in the broader BCI and neuroscience fields. I'm grateful every day to work in such an inspiring environment!
This paper proposes a multilayer graph model for community detection based on multiple observations. This scenario is common when different estimators are used to infer graph edges from signals at the nodes, or when various signal measurements are …
Brain–Computer Interface (BCI) systems allow to perform actions by translating brain activity into commands. Such systems require training a classification algorithm to discriminate between mental states, using specific features from the brain …
EEG signals acquired at different electrodes can be modelled as Signals on Graph, where the graph structure reflects the underlying brain Functional Connectivity (FC), representing brain region interactions. FC gives crucial information to detect …
Objective: Relying on the idea that functional connectivity provides important insights on the underlying dynamic of neuronal interactions, we propose a novel framework that combines functional connectivity estimators and covariance-based pipelines …
Thrilled to present our last piece of work in which we combined functional connectivity estimators with Riemannian geometry via an ensemble methods to improve the classification accuracy on a large number of publicly available datasets! OA version available [here](http://arxiv.org/abs/2111.03122), code paper available [here](https://www.softwareimpacts.com/article/S2665-9638(22)00019-7/fulltext) and code used available [here](https://github.com/mccorsi/FUCONE).
Thrilled to present our last piece of work in which we combined functional connectivity estimators with Riemannian geometry via an ensemble methods to improve the classification accuracy on a large number of publicly available datasets! Preprint available [here](http://arxiv.org/abs/2111.03122) and code used available [here](https://github.com/mccorsi/FUCONE).
Truly honoured to have received the #vBCI2021 best oral presentation award ! Thank you very much to the BCI Society for this award and for having put together this incredible conference ! Slides are available in the Talks section.