Name
Abstract | ||
|---|---|---|
Riemannian BCI based on EEG covariance have won many data competitions and achieved very high classification results on BCI datasets. To increase the accuracy of BCI systems, we propose an approach grounded on Riemannian geometry that extends this framework to functional connectivity measures. This paper describes the approach submitted to the Clinical BCI Challenge-WCCI2020 and that ranked 1st on the task 1 of the competition. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICASSP39728.2021.9414790 | 2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021) |
Keywords | DocType | Citations |
Riemannian geometry, functional connectivity, ensemble learning, BCI | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Noûs Camille | 1 | 0 | 7.10 |
| Marie-Constance Corsi | 2 | 2 | 2.74 |
| Sylvain Chevallier | 3 | 21 | 3.67 |
| Florian Yger | 4 | 16 | 4.42 |