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

TL;DR

This paper presents a novel Riemannian geometry-based method for classifying motor imagery EEG signals using connectivity measures, achieving top performance in a clinical BCI challenge.

Contribution

It introduces a new approach combining Riemannian geometry with functional connectivity measures for EEG classification, outperforming existing methods.

Findings

Ranked 1st in the Clinical BCI Challenge task 1

Demonstrated effectiveness of connectivity-based Riemannian features

Achieved state-of-the-art classification accuracy

Abstract

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.