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Multichannel Classification of Single EEG Trials with Independent Component Analysis

  • Dik Kin Wong
  • Marcos Perreau Guimaraes
  • E. Timothy Uy
  • Logan Grosenick
  • Patrick Suppes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

Abstract

We have previously shown that classification of single-trial electroencephalographic (EEG) recordings is improved by the use of either a multichannel classifier or the best independent component over a single channel classifier. In this paper, we introduce a classifier that makes explicit use of multiple independent components. Two models are compared. The first (“direct”) model uses independent components as time-series inputs, while the second (“indirect”) model remixes the components back to the signal space. The direct model resulted in significantly improved classification rates when applied to two experiments using both monopolar and bipolar settings.

Keywords

Independent Component Analysis Independent Component Independent Component Analysis Direct Model Signal Space 
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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References

  1. 1.
    Jasper, H.H.: The Ten-twenty Electrode Placement of The International Federation. Electroencephalography and Clinical Neurophysiology 10, 371–375 (1958)Google Scholar
  2. 2.
    Suppes, P., Han, B., Epelboim, J., Lu, Z.L.: Invariance Between Subjects of Brain Wave Representations of Language. Proceedings of the National Academy of Sciences 96, 12953–12958 (1999)CrossRefGoogle Scholar
  3. 3.
    Wong, D.K., Perreau Guimaraes, M., Uy, E.T., Suppes, P.: Classification of Individual Trials Based on The Best Independent Component of EEG-recorded Sentences. Neurocomputing 61, 479–484 (2004)CrossRefGoogle Scholar
  4. 4.
    Hyvarinen, A.: Independent Component Analysis by Minimization of Mutual Information. Laboratory of Computer and Information Science. Helsinki University of Technology (1997)Google Scholar
  5. 5.
    Belouchrani, A., Abed-Meraim, K., Cardoso, J.F., Moulines, E.: A Blind Source Separation Technique Using Second-order Statistics. IEEE Transactions on Signal Processing 45(2), 434–444 (1997)CrossRefGoogle Scholar
  6. 6.
    Bell, A.J., Sejnowski, T.J.: An Information-maximization Approach to Blind Separation And Blind Deconvolution. Neural Computation 7, 1129–1159 (1995)CrossRefGoogle Scholar
  7. 7.
    Delorme, A., Makeig, S.: EEGLAB: An Open Source Toolbox for Analysis of Single-trial EEG Dynamics. Journal of Neuroscience Methods 134, 9–21 (2004)CrossRefGoogle Scholar
  8. 8.
    Wong, D.K.: Multichannel Classification of Brain-wave Representations of Language by Perceptron-based Models and Independent Component Analysis (Ph.D. Dissertation). Stanford University, California, USA (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Dik Kin Wong
    • 1
  • Marcos Perreau Guimaraes
    • 1
  • E. Timothy Uy
    • 1
  • Logan Grosenick
    • 1
  • Patrick Suppes
    • 1
  1. 1.Center for Study of Language and InformationStanford UniversityUSA

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