ArticleFrontiers in neuroscience2018
Clustering-Constrained ICA for Ballistocardiogram Artifacts Removal in Simultaneous EEG-fMRI.
Article in Frontiers in neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- Artifact Reduction in Simultaneous EEG-fMRI: A Systematic Review of Methods and Contemporary Usage.Frontiers in neurology · 2021Pooled it
- Exploring Neural Mechanisms of Reward Processing Using Coupled Matrix Tensor Factorization: A Simultaneous EEG-fMRI Investigation.Brain sciences · 2023Article
- The effects of attention in auditory-visual integration revealed by time-varying networks.Frontiers in neuroscience · 2023Article
- EEG-fMRI: Ballistocardiogram Artifact Reduction by Surrogate Method for Improved Source Localization.Frontiers in neuroscience · 2022Article
- Reference-Free Adaptive Filtering of Extracellular Neural Signals Recording in Ultra-High Field Magnetic Resonance Imaging Scanners: Removal of Periodic Interferences.Biomedical signal processing and control · 2022Article
- Evaluation and comparison of most prevalent artifact reduction methods for EEG acquired simultaneously with fMRI.Frontiers in neuroimaging · 2022Article
- A new evolutionary preprocessing approach for classification of mental arithmetic based EEG signals.Cognitive neurodynamics · 2020Article
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Combination of electroencephalogram (EEG) recording and functional magnetic resonance imaging (fMRI) plays a potential role in neuroimaging due to its high spatial and temporal resolution. However, EEG is easily influenced by ballistocardiogram (BCG) artifacts and may cause false identification of the related EEG features, such as epileptic spikes. There are many related methods to remove them, however, they do not consider the time-varying features of BCG artifacts. In this paper, a novel method using clustering algorithm to catch the BCG artifacts' features and together with the constrained ICA (ccICA) is proposed to remove the BCG artifacts. We first applied this method to the simulated data, which was constructed by adding the BCG artifacts to the EEG signal obtained from the conventional environment. Then, our method was tested to demonstrate the effectiveness during EEG and fMRI experiments on 10 healthy subjects. In simulated data analysis, the value of error in signal amplitude (
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