output
20132026
most citedUsing Riemannian geometry for SSVEP-based Brain Computer Interface

108 citations

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Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

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eess.SP2022★ 1 cited

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…

eess.SP2021★ 11 cited

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…

eess.SP2021★ 1 cited

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…