Keywords: fMRI Analysis, Artifacts, EEG, deep learning
Motivation: EEG recordings inside MRI scanners are corrupted by BCG artifacts, which obscure genuine neural activity.
Goal(s): Develop BRNet, a deep learning method to effectively reduce BCG artifacts while preserving essential neural signals.
Approach: BRNet uses ECG input to estimate and remove BCG artifacts from EEG data using a 1D U-Net architecture, compared to the OBS method.
Results: BRNet significantly reduces BCG artifacts and preserves alpha band differences between eyes-open and eyes-closed states, outperforming OBS.
Impact: Enhances EEG data quality in MRI environments, improving reliability in neuroscience research and clinical diagnostics.
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