Ballistocardiogram Artifact Removal for Concurrent EEG-fMRI Recordings Using Blind Source Separation Based on Dictionary Learning
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Simultaneous recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have attracted extensive attention and research owing to their high spatial and temporal resolution. However, EEG data are easily influenced by physiological causes, gradient artifact (GA) and ballistocardiogram (BCG) artifact. In this paper, a new blind source separation technique based on dictionary learning is proposed to remove BCG artifact. The dictionary is learned from original data which represents the features of clean EEG signals and BCG artifact. Then, the dictionary atoms are classified according to a list of standards. Finally, clean EEG signals are obtained from the linear combination of the modified dictionary. The proposed method, ICA, AAS, and OBS are tested and compared using simulated data and real simultaneous EEG–fMRI data. The results suggest the efficacy and advantages of the proposed method in the removal of BCG artifacts.
KeywordsEelectroencephalography (EEG) functional Magnetic Resonance Imaging (fMRI) Ballistocardiogram Dictionary learning Signal processing
This work was supported by NSFC (61633010, 61671193, 61602140), National Key Research & Development Project (2017YFE0116800), Key Research & Development Project of Zhejiang Province (2020C04009, 2018C04012).
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