Properties of the ballistocardiogram artefact as revealed by EEG recordings at 1.5, 3 and 7 T static magnetic field strength

Int J Psychophysiol. 2008 Mar;67(3):189-99. doi: 10.1016/j.ijpsycho.2007.05.015. Epub 2007 Jul 12.

Abstract

Electroencephalogram (EEG) data recorded simultaneously with functional magnetic resonance imaging (fMRI) suffer from severe artefacts. The ballistocardiogram (BCG) artefact in particular is as yet poorly understood and different BCG removal strategies have been proposed. In the present study, EEG data were recorded from four participants in three different MRI scanners with field strengths of 1.5, 3 and 7 T, with the aim of investigating the impact of the static magnetic field strength on the BCG artefact and independent component analysis (ICA). The results confirm that the amplitude of the BCG artefact is a function of the static magnetic field strength. Moreover, the spatial variability of the BCG artefact substantially increased at higher magnetic field strengths. A comparison of ICA before and after channel-wise BCG correction revealed that typical independent components could be more easily identified when ICA was applied after channel-wise BCG correction. Further analysis of EEG and electrocardiogram recordings points towards the contribution of at least two different processes to the origin of the BCG, which are blood movement or axial head rotation on the one hand and electrode movement at lateral sites of the head on the other. This is summarized in a preliminary BCG model that may help to explain recent inconsistencies regarding the usefulness of ICA for BCG removal. It may also guide the future development of more advanced BCG removal procedures.

Publication types

  • Comparative Study

MeSH terms

  • Adult
  • Artifacts*
  • Ballistocardiography*
  • Brain Mapping / instrumentation*
  • Cerebral Cortex / physiology
  • Electroencephalography / instrumentation*
  • Electromagnetic Fields*
  • Female
  • Humans
  • Male
  • Principal Component Analysis
  • Reference Values
  • Signal Processing, Computer-Assisted