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Related Experiment Video

Updated: Feb 20, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

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Automatic and robust noise suppression in EEG and MEG: The SOUND algorithm.

Tuomas P Mutanen1, Johanna Metsomaa1, Sara Liljander2

  • 1Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, P.O. Box 12200, FI-00076, AALTO, Finland; BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, P.O. Box 340, FI-00029, HUS, Finland.

Neuroimage
|October 25, 2017
PubMed
Summary

Noise in electroencephalography (EEG) and magnetoencephalography (MEG) data is automatically removed using the SOUND algorithm. This method improves data quality and neural activity localization by modeling multi-sensor and multi-trial information.

Keywords:
ArtifactsCross-validationElectroencephalographyMagnetoencephalographyMinimum-norm estimationNoiseWiener estimation

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Area of Science:

  • Neuroscience
  • Biophysics
  • Signal Processing

Background:

  • Electroencephalography (EEG) and magnetoencephalography (MEG) data are susceptible to noise and artifacts.
  • Manual identification and rejection of "bad" channels are laborious, subjective, and inefficient.

Purpose of the Study:

  • To develop objective, automatic, and robust noise-cleaning methods for EEG and MEG data.
  • To introduce the source-estimate-utilizing noise-discarding (SOUND) algorithm.

Main Methods:

  • Modeling multi-sensor and multi-trial EEG/MEG data to calculate sensor- or trial-specific signal-to-noise ratios.
  • Utilizing anatomical head information for cross-validation between sensors in the SOUND algorithm.
  • Exploring data-driven Wiener estimators (DDWiener) for noise removal when anatomical information is unavailable.

Main Results:

  • The SOUND algorithm effectively identifies and suppresses noise and artifacts in EEG and MEG data.
  • SOUND demonstrated superior performance compared to traditional channel rejection and interpolation methods.
  • SOUND improved the accuracy of neural activity source localization by preventing noise contamination.

Conclusions:

  • The SOUND algorithm offers a significant advancement in cleaning functional brain data.
  • SOUND enables more reliable detection of active brain areas by enhancing data quality.
  • The theoretical framework of SOUND aligns with established Wiener estimation principles.