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Enhancing Signal and Network Integrity: Evaluating BCG Artifact Removal Techniques in Simultaneous EEG-fMRI Data.

Perihan Gülşah Gülhan1,2, Güzin Özmen3

  • 1Department of Electrical Electronics Engineering, Institute of Science, Selcuk University, Konya 42130, Turkey.

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Ballistocardiogram artifact removal in simultaneous Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) significantly impacts brain connectivity analysis. Hybrid methods, particularly Optimal Basis Set + Independent Component Analysis (OBS + ICA), offer superior performance in assessing dynamic brain networks.

Keywords:
BCG artifact removalbrain graph metricsfunctional connectivitymultimodal data analysissignal quality assessmentsimultaneous EEG-fMRI

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Data Science

Background:

  • Simultaneous Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are crucial for studying brain dynamics.
  • Ballistocardiogram (BCG) artifacts in EEG data degrade signal quality, hindering accurate brain connectivity assessment.
  • Existing artifact removal methods require comprehensive evaluation within a holistic framework.

Purpose of the Study:

  • To evaluate the efficacy of different EEG artifact removal techniques (AAS, OBS, ICA, and hybrid methods) in the context of simultaneous EEG-fMRI.
  • To assess the impact of these methods on both signal quality and graph-theoretical measures of brain connectivity.
  • To investigate the frequency-specific effects of artifact removal on static and dynamic brain network structures.

Main Methods:

  • Comparison of Average Artifact Subtraction (AAS), Optimal Basis Set (OBS), Independent Component Analysis (ICA), and hybrid approaches (AAS + ICA, OBS + ICA).
  • Evaluation using a combination of signal quality metrics and graph-theoretical network analysis (static and dynamic).
  • Analysis of topological properties and frequency-specific patterns in EEG-fMRI connectivity.

Main Results:

  • AAS yielded the best signal quality, while OBS preserved structural similarity.
  • ICA showed sensitivity to frequency-specific patterns in dynamic connectivity.
  • The OBS + ICA hybrid method demonstrated significant improvements, particularly in dynamic graph analysis across various frequency band pairs.
  • Artifact removal methods substantially altered network topology, with dynamic analyses revealing more pronounced frequency-specific effects.

Conclusions:

  • The choice of artifact removal method critically influences EEG-fMRI connectivity results.
  • Hybrid methods, especially OBS + ICA, offer advantages for dynamic brain network analysis.
  • Multimodal and frequency-sensitive evaluation strategies are essential for robust EEG-fMRI research.
  • Findings provide guidance for preprocessing decisions in EEG-fMRI studies, impacting the interpretation of brain connectivity.