Clinical BCI Challenge-WCCI2020: RIGOLETTO – RIemannian GeOmetry LEarning, applicaTion To cOnnectivity

02/09/2021

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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.

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Clinical BCI Challenge-WCCI2020: RIGOLETTO – RIemannian GeOmetry LEarning, applicaTion To cOnnectivity

Using Riemannian geometry for SSVEP-based Brain Computer Interface

Functional connectivity ensemble method to enhance BCI performance (FUCONE)

Manifold-regression to predict from MEG/EEG brain signals without source modeling

Functional random effects modeling of brain shape and connectivity

Towards the Classification of Error-Related Potentials using Riemannian Geometry

Challenge IEEE-ISBI/TCB : Application of Covariance matrices and wavelet marginals

Essential Motor Cortex Signal Processing: an ERP and functional connectivity MATLAB toolbox – User Guide

Clinical BCI Challenge-WCCI2020: RIGOLETTO – RIemannian GeOmetry LEarning, applicaTion To cOnnectivity

Related Research

Using Riemannian geometry for SSVEP-based Brain Computer Interface

Functional connectivity ensemble method to enhance BCI performance (FUCONE)

Manifold-regression to predict from MEG/EEG brain signals without source modeling

Functional random effects modeling of brain shape and connectivity

Towards the Classification of Error-Related Potentials using Riemannian Geometry

Challenge IEEE-ISBI/TCB : Application of Covariance matrices and wavelet marginals

Essential Motor Cortex Signal Processing: an ERP and functional connectivity MATLAB toolbox – User Guide