Artifact removal in magnetoencephalogram background activity with independent component analysis

Javier Escudero*, Roberto Hornero, Daniel Abasolo, Alberto Fernandez, Miguel Lopez-Coronado

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The aim of this study was to assess whether independent component analysis (ICA) could be valuable to remove power line -noise, cardiac, and ocular artifacts from magnetoencephalogram (MEG) background activity. The MEGs were recorded from 11 subjects with a 148-channel whole-head magnetometer. We used a statistical criterion to estimate the number of independent components. Then, a robust ICA algorithm decomposed the MEG epochs and several methods were applied to detect those artifacts. The whole process had been previously tested on synthetic data. We found that the line noise components could be easily detected by their frequency spectrum. In addition, the ocular artifacts could be identified by their frequency characteristics and scalp topography. Moreover, the cardiac artifact was better recognized by its skewness value than by its kurtosis one. Finally, the MEG signals were compared before and after artifact rejection to evaluate our method.

Original languageEnglish
Pages (from-to)1965-1973
Number of pages9
JournalIEEE Transactions on Biomedical Engineering
Volume54
Issue number11
DOIs
Publication statusPublished - Nov 2007

Keywords

  • artifact rejection
  • higher order statistics
  • independent
  • component analysis (ICA)
  • magnetoencephalography (MEG)
  • BLIND SOURCE SEPARATION
  • AUTOMATIC REMOVAL
  • NOISE-REDUCTION
  • EEG
  • SIGNALS
  • IDENTIFICATION
  • ICA
  • ELIMINATION
  • FIELDS
  • BRAIN

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