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)


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.


Tangent Space Covariance Matrice Motor Imagery Signal Processing Method Riemannian Distance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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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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