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Riemannian Geometry on Connectivity for Clinical BCI
- Source :
- ICASSP 2021, ICASSP 2021, Jun 2021, Toronto / Virtual, Canada. ⟨10.1109/ICASSP39728.2021.9414790⟩, ICASSP
- Publication Year :
- 2021
- Publisher :
- HAL CCSD, 2021.
-
Abstract
- International audience; 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 1 st on the task 1 of the competition.
- Subjects :
- [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing
Computer science
0206 medical engineering
Computer Science::Human-Computer Interaction
02 engineering and technology
Riemannian geometry
Machine learning
computer.software_genre
Task (project management)
03 medical and health sciences
symbols.namesake
Computer Science::Emerging Technologies
0302 clinical medicine
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing
BCI
Brain–computer interface
Quantitative Biology::Neurons and Cognition
business.industry
Functional connectivity
functional connectivity
Covariance
020601 biomedical engineering
Ensemble learning
Computer Science::Sound
Task analysis
symbols
ensemble learning
Artificial intelligence
business
computer
030217 neurology & neurosurgery
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- ICASSP 2021, ICASSP 2021, Jun 2021, Toronto / Virtual, Canada. ⟨10.1109/ICASSP39728.2021.9414790⟩, ICASSP
- Accession number :
- edsair.doi.dedup.....50c19e05495edaa1413157b711807700
- Full Text :
- https://doi.org/10.1109/ICASSP39728.2021.9414790⟩