1. Ballistocardiogram Artifact Removal for Concurrent EEG-fMRI Recordings Using Blind Source Separation Based on Dictionary Learning
- Author
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Liu Yuxi, Jianhai Zhang, Bohui Zhang, Kong Wanzeng, Hangzhou Dianzi University (HDU), University of Southern California (USC), Zhongzhi Shi, Sunil Vadera, Elizabeth Chang, and TC 12
- Subjects
Signal processing ,Computer science ,Electroencephalography ,EEG-fMRI ,Blind signal separation ,050105 experimental psychology ,03 medical and health sciences ,0302 clinical medicine ,Ballistocardiogram ,medicine ,0501 psychology and cognitive sciences ,[INFO]Computer Science [cs] ,Artifact (error) ,Eelectroencephalography (EEG) ,medicine.diagnostic_test ,business.industry ,functional Magnetic Resonance Imaging (fMRI) ,05 social sciences ,Pattern recognition ,Dictionary learning ,Temporal resolution ,Artificial intelligence ,Functional magnetic resonance imaging ,business ,030217 neurology & neurosurgery - Abstract
Part 5: Brain Computer Integration; International audience; 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.
- Published
- 2020
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