Iterative Subspace Decomposition for Ocular Artifact Removal from EEG Recordings

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5441)


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.


Independent Component Analysis Independent Component Analysis Ocular Artifact Independent Component Analysis Method Independent Subspace 
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© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  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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