An ensemble de-noising method for spatio-temporal EEG and MEG data

Stephan Weiss, Richard Leahy, John Mosher, Robert Stewart

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EEG/MEG are important tools for non-invasive medical diagnosis and basic studies of the brain and its functioning, but often applications are limited due to a very low SNR in the data. Here, we present a discrete wavelet transform (DWT) based de-noising method for spatio-temporal EEG/MEG measurements collected by a sensor array. A robust threshold selection can be achieved by incorporating spatial information and pre-stimulus data to estimate signal and noise energies. Further improvement can be gained by applying a translation-invariant approach to the derived de-noising scheme. In simulations, the performance of the proposed method is evaluated in comparison to standard de-noising and low-rank approximation, which o ers some complementarity to our approach.
Original languageEnglish
Pages (from-to)142-153
Number of pages12
JournalEURASIP Journal on Advances in Signal Processing
Issue number4
Publication statusPublished - 1997


  • signal processing
  • de-noising
  • spatio-temporal EEG


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