40 citations · 43 across the 4 of their papers we have counts for
4 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…
2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets
Xiaoxi Wei, A. Aldo Faisal, Moritz Grosse-Wentrup +18
Transfer learning and meta-learning offer some of the most promising avenues to unlock the scalability of healthcare and consumer technologies driven by biosignal data. This is bec…
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…