Independent component approach to the analysis of EEG and MEG recordings

IEEE Trans Biomed Eng. 2000 May;47(5):589-93. doi: 10.1109/10.841330.


Multichannel recordings of the electromagnetic fields emerging from neural currents in the brain generate large amounts of data. Suitable feature extraction methods are, therefore, useful to facilitate the representation and interpretation of the data. Recently developed independent component analysis (ICA) has been shown to be an efficient tool for artifact identification and extraction from electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings. In addition, ICA has been applied to the analysis of brain signals evoked by sensory stimuli. This paper reviews our recent results in this field.

MeSH terms

  • Algorithms*
  • Artifacts*
  • Electroencephalography*
  • Evoked Potentials, Auditory / physiology
  • Evoked Potentials, Somatosensory / physiology
  • Humans
  • Magnetoencephalography*
  • Signal Processing, Computer-Assisted*