Riemannian Geometry Applied to BCI Classification

  • Alexandre Barachant
  • Stéphane Bonnet
  • Marco Congedo
  • Christian Jutten
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6365)

Abstract

In brain-computer interfaces based on motor imagery, covariance matrices are widely used through spatial filters computation and other signal processing methods. Covariance matrices lie in the space of Symmetric Positives-Definite (SPD) matrices and therefore, fall within the Riemannian geometry domain. Using a differential geometry framework, we propose different algorithms in order to classify covariance matrices in their native space.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Alexandre Barachant
    • 1
  • Stéphane Bonnet
    • 1
  • Marco Congedo
    • 2
  • Christian Jutten
    • 2
  1. 1.CEA, LETI, DTBS/STD/LE2SGrenobleFrance
  2. 2.Team ViBS (Vision and Brain Signal Processing), GIPSA-labCNRS, Grenoble Universities., Domaine UniviversitaireSaint Martin d’HèresFrance

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