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

arXiv 2102.06015 ↗First posted 9 Feb 2021eess.SP cs.LGcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 3 of its 48 references that are in the map. First posted on arXiv 9 Feb 2021.

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3 of 48 references.

References · 3

  1. 2014Graph analysis of functional brain networks: practical issues in translational neurosciencecited by 10
  2. 2019Phase/amplitude synchronization of brain signals during motor imagery BCI taskscited by 3
  3. 2015Supervised LogEuclidean Metric Learning for Symmetric Positive Definite Matricescited by 2

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