Classification

HappyFeat—An interactive and efficient BCI framework for clinical applications

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 …

Measuring Neuronal Avalanches to inform Brain-Computer Interfaces

Large-scale interactions among multiple brain regions manifest as bursts of activations called neuronal avalanches, which reconfigure according to the task at hand and, hence, might constitute natural candidates to design brain-computer interfaces …

RIGOLETTO paper accepted in ICASSP !

Pleased to announce with [Sylvain Chevallier](https://sylvchev.github.io) and [Florian Yger](http://www.yger.fr) that our manuscript on our participation to the Clinical BCI Challenge WCCI2020 has been accepted for the next [ICASSP](https://2021.ieeeicassp.org) conference ! The technical report and code about the competition are available [here](https://arxiv.org/abs/2102.06015)

Our RIGOLETTO approach ranked 1st to the IEEE WCCI2020 Challenge !

Very pleased to announce with [Sylvain Chevallier](https://sylvchev.github.io) from Univ. Paris-Saclay and [Florian Yger](http://www.yger.fr) from Univ. Paris-Dauphine that our method combining functional connectivity with Riemannian geometry made it 1st to the [Clinical BCI Challenge IEEE WCCI2020](CBCIC2020sites.google.com) (within-subject category) ! More info to come, stay tuned !