Riemannian Geometry Applied to BCI Classification

  • Alexandre Barachant
  • Stéphane Bonnet
  • Marco Congedo
  • Christian Jutten
Conference paper

DOI: 10.1007/978-3-642-15995-4_78

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6365)
Cite this paper as:
Barachant A., Bonnet S., Congedo M., Jutten C. (2010) Riemannian Geometry Applied to BCI Classification. In: Vigneron V., Zarzoso V., Moreau E., Gribonval R., Vincent E. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2010. Lecture Notes in Computer Science, vol 6365. Springer, Berlin, Heidelberg

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

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

Personalised recommendations