ArticleNeuroImage2016
Reference-free removal of EEG-fMRI ballistocardiogram artifacts with harmonic regression.
Article in NeuroImage, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artifact Reduction in Simultaneous EEG-fMRI: A Systematic Review of Methods and Contemporary Usage.Frontiers in neurology · 2021Pooled it
- Evaluating cardiac noise correction approaches for non-invasive electrophysiology of the human spinal cord.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- The MotoNet: A 3 Tesla MRI-Conditional EEG Net with Embedded Motion Sensors.Sensors (Basel, Switzerland) · 2023Article
- Ballistocardiogram suppression in concurrent EEG-MRI by dynamic modeling of heartbeats.Human brain mapping · 2022Article
- Simultaneous EEG-fMRI: What Have We Learned and What Does the Future Hold?Sensors (Basel, Switzerland) · 2022Review
- 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
- Ballistocardiogram Artifact Reduction in Simultaneous EEG-fMRI Using Deep Learning.IEEE transactions on bio-medical engineering · 2021Article
- Model-based physiological noise removal in fast fMRI.NeuroImage · 2020Article
- [Study of denoising of simultaneous electroencephalogram-functional magnetic resonance imaging signal based on real-time constrained independent components analysis].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2019Article
- Exploring the relative efficacy of motion artefact correction techniques for EEG data acquired during simultaneous fMRI.Human brain mapping · 2019Article
- Clustering-Constrained ICA for Ballistocardiogram Artifacts Removal in Simultaneous EEG-fMRI.Frontiers in neuroscience · 2018Article
- EEG-Informed fMRI: A Review of Data Analysis Methods.Frontiers in human neuroscience · 2018Review
- Polymer thick film technology for improved simultaneous dEEG/MRI recording: Safety and MRI data quality.Magnetic resonance in medicine · 2017Article
- Unimodal Versus Bimodal EEG-fMRI Neurofeedback of a Motor Imagery Task.Frontiers in human neuroscience · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Combining electroencephalogram (EEG) recording and functional magnetic resonance imaging (fMRI) offers the potential for imaging brain activity with high spatial and temporal resolution. This potential remains limited by the significant ballistocardiogram (BCG) artifacts induced in the EEG by cardiac pulsation-related head movement within the magnetic field. We model the BCG artifact using a harmonic basis, pose the artifact removal problem as a local harmonic regression analysis, and develop an efficient maximum likelihood algorithm to estimate and remove BCG artifacts. Our analysis paradigm accounts for time-frequency overlap between the BCG artifacts and neurophysiologic EEG signals, and tracks the spatiotemporal variations in both the artifact and the signal. We evaluate performance on: simulated oscillatory and evoked responses constructed with realistic artifacts; actual anesthesia-induced oscillatory recordings; and actual visual evoked potential recordings. In each case, the local harmonic regression analysis effectively removes the BCG artifacts, and recovers the neurophysiologic EEG signals. We further show that our algorithm outperforms commonly used reference-based and component analysis techniques, particularly in low SNR conditions, the presence of significant time-frequency overlap between the artifact and the signal, and/or large spatiotemporal variations in the BCG. Because our algorithm does not require reference signals and has low computational complexity, it offers a practical tool for removing BCG artifacts from EEG data recorded in combination with fMRI.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.