Denoising and Averaging Techniques for Electrophysiological Data

  • Matthias Ihrke
  • Hecke Schrobsdorff
  • J. Michael Herrmann
Part of the Springer Series in Computational Neuroscience book series (NEUROSCI, volume 2)


Neurophysiological signals are often corrupted by noise that is significantly stronger than the signal itself. In electroencephalographic (EEG) data this may amount to figures of −25 dB (Flexer, 2000), for electromyography (EMG) or functional magnetic resonance imaging (fMRI) the situation is similar. The problem of the recovery of information under noise has been dealt with extensively in the literature of signal and image processing (Whalen, 1971; Castleman, 1996).


Independent Component Analysis Independent Component Analysis Variable Latency Model Wiener Filter Time Marker 
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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Matthias Ihrke
    • 1
  • Hecke Schrobsdorff
    • 1
    • 2
  • J. Michael Herrmann
    • 1
    • 3
  1. 1.Bernstein Center for Computational Neuroscience GöttingenGöttingenGermany
  2. 2.Max-Planck Institut for Dynamics and Self-OrganizationGöttingenGermany
  3. 3.School of Informatics, Institute of Perception, Action and BehaviourUniversity of EdinburghEdinburghUK

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