Highlights: In this study, we systematically investigated the effects of different BCG artifact removal methods on EEG signal quality and functional connectivity during simultaneous EEG–fMRI recordings. The analysis demonstrated how various methods impact signal fidelity, structural similarity, and dynamic graph metrics across EEG frequency bands. The results revealed method-specific differences in network topology, highlighting the importance of choosing an appropriate artifact removal approach for accurate brain network interpretation. What are the main findings? AAS achieved the best signal fidelity (MSE = 0.0038, PSNR = 26.34 dB), and OBS yielded the highest structural similarity (SSIM = 0.72), whereas ICA showed greater sensitivity in dynamic graph metrics. Connectivity graphs after artifact removal displayed distinct frequency-specific patterns, particularly in the beta and gamma bands. What are the implications of the main findings? Artifact removal affects both EEG signal preprocessing and the topological interpretation of the functional brain networks. Method selection is critical for reliable connectivity and graph-based analyses in EEG–fMRI studies. Simultaneous Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) provide a powerful framework for investigating brain dynamics; however, ballistocardiogram (BCG) artifacts in EEG compromise signal quality and limit the assessment of brain connectivity. This study evaluated three widely used artifact removal methods—Average Artifact Subtraction (AAS), Optimal Basis Set (OBS), and Independent Component Analysis (ICA)—together with two hybrid approaches (AAS + ICA and OBS + ICA). Unlike previous studies that focused solely on signal-level metrics, we adopted a holistic framework that combined signal quality indicators with graph-theoretical analysis of EEG-fMRI connectivity in static and dynamic contexts. The results show that AAS provides the best signal quality, whereas OBS better preserves structural similarity. ICA, although weaker in terms of signal metrics, demonstrates sensitivity to frequency-specific patterns in dynamic graphs. Hybrid methods yield benefits, with OBS + ICA producing the lowest p-values across frequency band pairs (e.g., theta–beta and delta–gamma), particularly in dynamic graphs. Topological analyses revealed that artifact removal significantly affected network structure, with dynamic analyses showing more pronounced frequency-specific effects than static analyses. High-frequency bands, such as beta and gamma, exhibit stronger differentiation under dynamic conditions. Overall, this study offers new insights into the relationship between artifact removal and brain network integrity, emphasizing the need for multimodal and frequency-sensitive evaluation strategies. The findings guide preprocessing decisions in EEG-fMRI studies and clarify how methodological choices shape the interpretation of brain connectivity.