Brain Topography

, Volume 31, Issue 3, pp 337–345 | Cite as

Heart–Brain Interactions in the MR Environment: Characterization of the Ballistocardiogram in EEG Signals Collected During Simultaneous fMRI

  • Marco Marino
  • Quanying Liu
  • Mariangela Del Castello
  • Cristiana Corsi
  • Nicole Wenderoth
  • Dante Mantini
Original Paper


The ballistocardiographic (BCG) artifact is linked to cardiac activity and occurs in electroencephalographic (EEG) recordings acquired inside the magnetic resonance (MR) environment. Its variability in terms of amplitude, waveform shape and spatial distribution over subject’s scalp makes its attenuation a challenging task. In this study, we aimed to provide a detailed characterization of the BCG properties, including its temporal dependency on cardiac events and its spatio-temporal dynamics. To this end, we used high-density EEG data acquired during simultaneous functional MR imaging in six healthy volunteers. First, we investigated the relationship between cardiac activity and BCG occurrences in the EEG recordings. We observed large variability in the delay between ECG and subsequent BCG events (ECG–BCG delay) across subjects and non-negligible epoch-by-epoch variations at the single subject level. The inspection of spatial–temporal variations revealed a prominent non-stationarity of the BCG signal. We identified five main BCG waves, which were common across subjects. Principal component analysis revealed two spatially distinct patterns to explain most of the variance (85% in total). These components are possibly related to head rotation and pulse-driven scalp expansion, respectively. Our results may inspire the development of novel, more effective methods for the removal of the BCG, capable of isolating and attenuating artifact occurrences while preserving true neuronal activity.


Ballistocardiogram (BCG) Inter-trial variability Non-stationarity EEG–fMRI Multimodal imaging 



The authors would like to thank Stefan Debener and the anonymous reviewers of the manuscript for their insightful comments and suggestions, and Ronald Peeters, René Clerckx and Paul Meugens for their helpful advice and support on EEG–fMRI integration. The work was supported by the KU Leuven Special Research Fund (Grant C16/15/070), and the Research Foundation Flanders (FWO) (Grants G0F76.16N, G0936.16N and EOS. 30446199).

Supplementary material

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Supplementary material 1 (PDF 2610 KB)


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Neural Control of Movement LaboratoryETH ZurichZurichSwitzerland
  2. 2.Department of Experimental PsychologyUniversity of OxfordOxfordUK
  3. 3.Laboratory of Movement Control and NeuroplasticityKU LeuvenLouvainBelgium
  4. 4.Department of Electrical, Electronic, and Information Engineering “Gugliemo Marconi”University of BolognaBolognaItaly

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