Name
Title | ||
|---|---|---|
Riemannian geometry for combining functional connectivity metrics and covariance in BCI |
Abstract | ||
|---|---|---|
Brain–computer interfaces allow interactions based on brain activities detected in electroencephalography. Despite important improvements in the last decade, some subjects still achieve poor performances without any identified cause. On the one hand, State-of-the-art methods for online decoding are based on covariance matrices seen as elements of a Riemann manifold. On the other hand, functional connectivity is a powerful method to characterize the brain activity. The proposed software combines functional connectivity and covariance within a Riemannian framework to increase the robustness of brain–computer interfaces. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.simpa.2022.100254 | Software Impacts |
Keywords | DocType | Volume |
Functional connectivity,Riemannian geometry,Brain–computer interface,Ensemble learning | Journal | 12 |
ISSN | Citations | PageRank |
2665-9638 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sylvain Chevallier | 1 | 0 | 0.34 |
| Marie-Constance Corsi | 2 | 0 | 0.34 |
| Florian Yger | 3 | 0 | 0.34 |
| Fabrizio de Vico Fallani | 4 | 133 | 20.22 |