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 …
Truely happy to have taken part of the organization of the [CuttingGardens](https://cuttinggardens2023.org), a multi-hub meeting on EEG and MEG methods! I have notably animated a session dedicated to the use of BCI with 3 insightful talks given by R. Kobler, M. Tangermann, and T. Vaughan. All the materials (and many other features!) are available in the dedicated [github page](https://github.com/mccorsi/CuttingGarden2023-RealTimeEEG_BCI)
Truely happy to take part of the organization of the [PracticalMEEG](https://practicalmeeg2022.org/) workshop! I will notably co-animate a [hands-on tutorial on OpenViBE](https://github.com/Inria-NERV/BCI-OpenViBE-PracticalMEEG2022) with my colleague Arthur Desbois and I will be a tutor during the [Fieldtrip sessions](https://www.fieldtriptoolbox.org/workshop/practicalmeeg2022/) with Robert Oostenveld and Laure Spieser!
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 …
Glad to present the chapter I wrote for the Book entitled *Machine learning for brain diseases*. This chapter aims at providing an overview of the electroencephalography and the magnetoencephalography domains. A preliminary version is available [here](https://hal.inria.fr/hal-03604421), and the code used to plot the figures and to propose some insights on M/EEG processing is available [here](https://github.com/mccorsi/ML-for-Brain-Disorders_MEEG).
Riemannian BCI based on EEG covariance have won many data competitions and achieved very high classification results on BCI datasets. To increase the accuracy of BCI systems, we propose an approach grounded on Riemannian geometry that extends this …
In this paper, we present the first proof of concept confirming the possibility to record magnetoencephalographic (MEG) signals with optically pumped magnetometers (OPMs) based on the parametric resonance of 4He atoms. The main advantage of this kind …
Discriminating mental states from brain signals is crucial for many applications in cognitive and clinical neuroscience. Most of the studies relied on the feature extraction from the activity of single brain areas, thus neglecting the potential …