# RIGOLETTO -- RIemannian GeOmetry LEarning: applicaTion To cOnnectivity. A contribution to the Clinical BCI Challenge -- WCCI2020

arXiv: https://arxiv.org/abs/2102.06015 · HTML: https://mlsanity.com/paper/2102.06015/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 48 references that are in the map.
- First posted on arXiv 9 Feb 2021.

## Builds on (most cited first, 3 of 3)

- [Graph analysis of functional brain networks: practical issues in translational neuroscience](https://mlsanity.com/paper/1406.7391/) (2014)
- [Phase/amplitude synchronization of brain signals during motor imagery BCI tasks](https://mlsanity.com/paper/1912.02745/) (2019)
- [Supervised LogEuclidean Metric Learning for Symmetric Positive Definite Matrices](https://mlsanity.com/paper/1502.03505/) (2015)

## Abstract (arXiv)

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.