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

Background: Simultaneous electroencephalography(EEG) and functional magnetic resonance imaging (fMRI) enable comprehensive investigationsof brain dynamics by combining high temporal and spatial resolutions. However, ballistocardiogram(BCG) artifacts in EEGsseverely affect signal quality and interpretation. This study aims to comparatively evaluate three widely used artifact removal techniques—average artifact subtraction (AAS), optimal basis set (OBS), and independent component analysis(ICA)—together with two hybrid approaches (AAS+ICA and OBS+ICA) within a multimodal and frequency-sensitive evaluation framework. Methods: Simultaneous EEG-fMRI data were preprocessed viafive artifact removal pipelines. Signal quality was assessed via time- and frequency-domain metrics, including power spectral density and correlation measures. Static and dynamic EEG-fMRI connectivity graphs were constructed on the basis of independent component networks (ICNs) and frequency-specific EEG features. Graph-theoretical measures (e.g., the clustering coefficient and global efficiency) were computed, and differences between methods were statistically evaluated via paired t tests with false discovery rate (FDR) correction. Results: AAS achieved the highest overall signal quality, whereasOBS preserved structural similarity more effectively. Although ICA yielded lower performance on traditional signal metrics, it demonstrated higher sensitivity to frequency-specific patterns, particularly in dynamic connectivity graphs. Among hybrid approaches, OBS+ICA provided the lowest p values across several frequency band pairs (e.g., theta–beta and delta–gamma), indicating improved detection of frequency-dependent interactions. Topological analyses revealed that artifact removal techniques substantially influence brain network organization. Dynamic connectivity analyses revealedstronger frequency-specific effects than static analyses did, with the beta and gamma bands showing the most pronounced differences. Conclusions: This study highlights the critical role of artifact removal strategies in shaping both EEG signal quality and EEG-fMRI connectivity outcomes. High-frequency bands, especially beta and gamma bands, exhibit distinctive network reconfigurations under dynamic conditions, underscoring their importance in cognitive and perceptual processes. By integrating signal-level, graph-theoretical, and multimodal evaluations, our findings provide practical guidelines for selecting preprocessing pipelines in simultaneous EEG-fMRI research and deepen our understanding of how methodological choices affect interpretations of brain connectivity.