Ballistocardiogram Artifact Removal for Concurrent EEG-fMRI Recordings Using Blind Source Separation Based on Dictionary Learning - Archive ouverte HAL Access content directly
Conference Papers Year : 2020

Ballistocardiogram Artifact Removal for Concurrent EEG-fMRI Recordings Using Blind Source Separation Based on Dictionary Learning

(1) , (1) , (2) , (1)
1
2

Abstract

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.
Fichier principal
Vignette du fichier
498234_1_En_17_Chapter.pdf (1.08 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03456964 , version 1 (30-11-2021)

Licence

Attribution - CC BY 4.0

Identifiers

Cite

Yuxi Liu, Jianhai Zhang, Bohui Zhang, Wanzeng Kong. Ballistocardiogram Artifact Removal for Concurrent EEG-fMRI Recordings Using Blind Source Separation Based on Dictionary Learning. 11th International Conference on Intelligent Information Processing (IIP), Jul 2020, Hangzhou, China. pp.180-191, ⟨10.1007/978-3-030-46931-3_17⟩. ⟨hal-03456964⟩
9 View
0 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More