no code implementations • 5 Oct 2022 • Maciej Śliwowski, Matthieu Martin, Antoine Souloumiac, Pierre Blanchart, Tetiana Aksenova
The performance gap is reduced with bigger datasets, but considering the increased computational load, end-to-end training may not be profitable for this application.
no code implementations • 8 Sep 2022 • Maciej Śliwowski, Matthieu Martin, Antoine Souloumiac, Pierre Blanchart, Tetiana Aksenova
In this study, we investigated the impact of long-term recordings on motor imagery decoding from two main perspectives: model requirements regarding dataset size and potential for patient adaptation.
no code implementations • 25 Jan 2022 • Alexandre Moly, Thomas Costecalde, Felix Martel, Christelle Larzabal, Serpil Karakas, Alexandre Verney, Guillaume Charvet, Stephan Chabardes, Alim Louis Benabid, Tetiana Aksenova
Brain-computer interfaces (BCIs) still face many challenges to step out of laboratories to be used in real-life applications.
no code implementations • 5 Oct 2021 • Maciej Śliwowski, Matthieu Martin, Antoine Souloumiac, Pierre Blanchart, Tetiana Aksenova
These models have a limited representational capacity and may fail to capture the relationship between ECoG signal and continuous hand movements.
no code implementations • 14 Oct 2020 • Alexandre Moly, Alexandre Aksenov, Alim Louis Benabid, Tetiana Aksenova
The proposed decoder was designed to create BCI systems with low computational cost suited for portable applications and tested during offline pseudo-online study based on online closed-loop BCI control of the left and right 3D arm movements of a virtual avatar from the ECoG recordings of a tetraplegic patient.