108 citations · 131 across the 10 of their papers we have counts for
4 papers · 1 filter
Evaluating the structure of cognitive tasks with transfer learning
Bruno Aristimunha, Raphael Y. de Camargo, Walter H. Lopez Pinaya +3
Electroencephalography (EEG) decoding is a challenging task due to the limited availability of labelled data. While transfer learning is a promising technique to address this chall…
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…
Minimizing subject-dependent calibration for BCI with Riemannian transfer learning
Salim Khazem, Sylvain Chevallier, Quentin Barthélemy +2
Calibration is still an important issue for user experience in Brain-Computer Interfaces (BCI). Common experimental designs often involve a lengthy training period that raises the…
RIGOLETTO -- RIemannian GeOmetry LEarning: applicaTion To cOnnectivity. A contribution to the Clinical BCI Challenge -- WCCI2020
Marie-Constance Corsi, Florian Yger, Sylvain Chevallier +1
This short technical report describes the approach submitted to the Clinical BCI Challenge-WCCI2020. This submission aims to classify motor imagery task from EEG signals and relies…