Advertisement

Directed Components Analysis: An Analytic Method for the Removal of Biophysical Artifacts from EEG Data

  • Phan Luu
  • Robert Frank
  • Scott Kerick
  • Don M. Tucker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5638)

Abstract

Artifacts generated by biophysical sources (such as muscles, eyes, and heart) often hamper the use of EEG for the study of brain functions in basic research and applied settings. These artifacts share frequency overlap with the EEG, making frequency filtering inappropriate for their removal. Spatial decomposition methods, such as principal and independent components analysis, have been employed for the removal of the artifacts from the EEG. However, these methods have limitations that prevent their use in operational environments that require real-time analysis. We have introduced a directed components analysis (DCA) that employs a spatial template to direct the selection of target artifacts. This method is computationally efficient, allowing it to be employed in real-world applications. In this paper, we evaluate the effect of spatial undersampling of the scalp potential field on the ability of DCA to remove blink artifacts.

Keywords

EEG artifact brain activity neuroergonomic 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Cuffin, B.N., Schomer, D.L., Ives, J.R., Blume, H.: Experimental Tests of EEG Source Localization Accuracy in Spherical Head Models. Clin. Neurophysiol. 112, 46–51 (2001)CrossRefPubMedGoogle Scholar
  2. 2.
    Poolman, P., Frank, R.M., Bell, R.M., Tucker, D.M., Luu, P.: Advanced Integrated Real-Time Artifact Removal Framework. In: Schmorrow, D.D., Stanney, K.M., Reeves, L.M. (eds.) Foundations of Augmented Cognition, pp. 102–110. Falcon Books, San Ramon (2006)Google Scholar
  3. 3.
    Ille, N., Berg, P., Scherg, M.: Artifact Correction of the Ongoing EEG Using Spatial Filters Based on Artifact and Brain Signal Topographies. J. Clin. Neurophysiol. 19, 113–124 (2002)CrossRefPubMedGoogle Scholar
  4. 4.
    Srinivasan, R., Tucker, D.M., Murias, M.: Estimating the Spatial Nyquist of the Human EEG. Behav. Res. Meth. Inst., Comput. 30, 8–19 (1998)CrossRefGoogle Scholar
  5. 5.
    Luu, P., Shane, M., Pratt, N.L., Tucker, D.M.: Corticolimbic Mechanisms in the Control of Trial and Error Learning. Brain Res. 1247, 100–113 (2009)CrossRefPubMedGoogle Scholar
  6. 6.
    Luu, P., Tucker, D.M., Stripling, R.: Neural Mechanisms for Learning Actions in Context. Brain Res. 1179, 89–105 (2007)CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Phan Luu
    • 1
  • Robert Frank
    • 2
  • Scott Kerick
    • 3
  • Don M. Tucker
    • 1
  1. 1.Electrical Geodesics, Inc.EugeneUSA
  2. 2.NeuroInformatics CenterUniversity of OregonEugeneUSA
  3. 3.US Army Research LaboratoryAberdeen Proving GroundUSA

Personalised recommendations