RIGOLETTO -- RIemannian GeOmetry LEarning: applicaTion To cOnnectivity. A contribution to the Clinical BCI Challenge -- WCCI2020
arXiv:2102.06015v2
AI Analysis
This is an incremental improvement for brain-computer interface applications, specifically in clinical settings.
The paper tackled motor imagery classification from EEG signals by using Riemannian geometry with functional connectivity measures instead of classical covariance matrices, achieving first place in task 1 of the Clinical BCI Challenge-WCCI2020.
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 on Riemannian Geometry, with a twist. Instead of using the classical covariance matrices, we also rely on measures of functional connectivity. Our approach ranked 1st on the task 1 of the competition.