40 citations · 43 across the 5 of their papers we have counts for
5 papers
Deep learning for ECoG brain-computer interface: end-to-end vs. hand-crafted features
Maciej Śliwowski, Matthieu Martin, Antoine Souloumiac +2
In brain signal processing, deep learning (DL) models have become commonly used. However, the performance gain from using end-to-end DL models compared to conventional ML approache…
Impact of dataset size and long-term ECoG-based BCI usage on deep learning decoders performance
Maciej Śliwowski, Matthieu Martin, Antoine Souloumiac +2
In brain-computer interfaces (BCI) research, recording data is time-consuming and expensive, which limits access to big datasets. This may influence the BCI system performance as m…
An adaptive closed-loop ECoG decoder for long-term and stable bimanual control of an exoskeleton by a tetraplegic
Alexandre Moly, Thomas Costecalde, Felix Martel +7
Brain-computer interfaces (BCIs) still face many challenges to step out of laboratories to be used in real-life applications. A key one persists in the high performance control of…
Decoding ECoG signal into 3D hand translation using deep learning
Maciej Śliwowski, Matthieu Martin, Antoine Souloumiac +2
Motor brain-computer interfaces (BCIs) are a promising technology that may enable motor-impaired people to interact with their environment. Designing real-time and accurate BCI is…
Online adaptive group-wise sparse NPLS for ECoG neural signal decoding
Alexandre Moly, Alexandre Aksenov, Alim Louis Benabid +1
Objective. Brain-computer interfaces (BCIs) create a new communication pathway between the brain and an effector without neuromuscular activation. BCI experiments highlighted high…