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EEG-fMRI: Ballistocardiogram Artifact Reduction by Surrogate Method for Improved Source Localization.

Mateusz Rusiniak1, Harald Bornfleth1, Jae-Hyun Cho1

  • 1Research Department, BESA GmbH, Gräfelfing, Germany.

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Summary

New surrogate source models significantly improve simultaneous EEG-fMRI analysis by effectively removing ballistocardiogram (BCG) artifacts. These methods, principal components analysis (PCA-S) and independent components analysis (ICA-S), minimize distortion of brain signals compared to existing techniques.

Keywords:
artifact removalblind source separation (BSS)independent component analysis (ICA)multimodal imagingoptimal basis set (OBS)pulse artifact (PA)simultaneous EEG and fMRIspatial filter (SF)

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Simultaneous electroencephalography-functional magnetic resonance imaging (EEG-fMRI) offers rich insights into brain activity but is challenged by artifacts.
  • The ballistocardiogram (BCG) artifact is particularly difficult to remove without compromising neural signal integrity.
  • Effective artifact removal is crucial for accurate analysis of simultaneous EEG-fMRI data.

Purpose of the Study:

  • To evaluate surrogate source models for separating BCG artifacts from neural signals in EEG-fMRI recordings.
  • To compare the efficacy of principal components analysis (PCA-S) and independent components analysis (ICA-S) against established BCG removal methods.
  • To assess the impact of artifact removal methods on electroencephalography (EEG) event-related potential (ERP) and source localization analyses.

Main Methods:

  • Developed and applied surrogate source models (PCA-S and ICA-S) to separate artifactual signals.
  • Compared PCA-S and ICA-S with Blind Source Separation (BSS), Optimal Basis Set (OBS), and OBS-ICA using resting-state EEG-fMRI data.
  • Evaluated methods based on artifact threshold survival, signal-to-noise ratio (SNR), source localization error, and signal variance explained.

Main Results:

  • PCA-S and ICA-S demonstrated superior performance in BCG artifact removal compared to established methods.
  • Significant improvements were observed in source localization accuracy using surrogate source models.
  • The PCA-S approach was successfully applied to a Berger experiment, confirming its practical utility.

Conclusions:

  • Surrogate source models (PCA-S and ICA-S) offer a substantial advancement for analyzing simultaneous EEG-fMRI data.
  • These methods effectively remove BCG artifacts with minimal distortion of neural signals, particularly benefiting source analysis.
  • The findings suggest a new standard for artifact removal in combined EEG-fMRI research.