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Iterative Subspace Decomposition for Ocular Artifact Removal from EEG Recordings

  • Cédric Gouy-Pailler
  • Reza Sameni
  • Marco Congedo
  • Christian Jutten
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5441)

Abstract

In this study, we present a method to remove ocular artifacts from electroencephalographic (EEG) recordings. This method is based on the detection of the EOG activation periods from a reference EOG channel, definition of covariance matrices containing the nonstationary information of the EOG, and applying generalized eigenvalue decomposition (GEVD) onto these matrices to rank the components in order of resemblance with the EOG. An iterative procedure is further proposed to remove the EOG components in a deflation fashion.

Keywords

Independent Component Analysis Independent Component Analysis Ocular Artifact Independent Component Analysis Method Independent Subspace 
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 2009

Authors and Affiliations

  • Cédric Gouy-Pailler
    • 1
  • Reza Sameni
    • 2
  • Marco Congedo
    • 1
  • Christian Jutten
    • 1
  1. 1.GIPSA-labDIS/CNRS/INPG-UJF-Stendhal Domaine UniversitaireSaint Martin d’Hères CedexFrance
  2. 2.College of Electrical and Computer EngineeringShiraz UniversityShirazIran

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