Loading…
Ballistocardiogram artifact removal in simultaneous EEG-fMRI using generative adversarial network
Due to its advantages of high temporal and spatial resolution, the technology of simultaneous electroencephalogram-functional magnetic resonance imaging (EEG-fMRI) acquisition and analysis has attracted much attention, and has been widely used in various research fields of brain science. However, du...
Saved in:
Published in: | Journal of neuroscience methods 2022-04, Vol.371, p.109498-109498, Article 109498 |
---|---|
Main Authors: | , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Summary: | Due to its advantages of high temporal and spatial resolution, the technology of simultaneous electroencephalogram-functional magnetic resonance imaging (EEG-fMRI) acquisition and analysis has attracted much attention, and has been widely used in various research fields of brain science. However, during the fMRI of the brain, ballistocardiogram (BCG) artifacts can seriously contaminate the EEG. As an unpaired problem, BCG artifact removal now remains a considerable challenge. Aiming to provide a solution, this paper proposed a novel modular generative adversarial network (GAN) and corresponding training strategy to improve the network performance by optimizing the parameters of each module. In this manner, we hope to improve the local representation ability of the network model, thereby improving its overall performance and obtaining a reliable generator for BCG artifact removal. Moreover, the proposed method does not rely on additional reference signal or complex hardware equipment. Experimental results show that, compared with multiple methods, the technique presented in this paper can remove the BCG artifact more effectively while retaining essential EEG information.
•A novel GAN-based model is designed (BCGGAN) to remove the BCG artifact in simultaneous EEG-fMRI.•A modular training strategy is proposed to optimize the generator network in the BCGGAN model.•The proposed method does not require additional hardware or reference signal, such as carbon fiber sling or ECG signals.•The proposed method can remove the BCG artifact more effectively while retaining useful physiological information. |
---|---|
ISSN: | 0165-0270 1872-678X |
DOI: | 10.1016/j.jneumeth.2022.109498 |