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Removal of FMRI environment artifacts from EEG data using optimal basis sets
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Cites
Delorme, A., & Makeig, S. (2004) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2003.10.009 · Cited by 24,371
A Method for Removing Imaging Artifact from Continuous EEG Recorded during Functional MRI
Allen, P. J., Josephs, O., & Turner, R. (2000) · NeuroImage · doi:10.1006/nimg.2000.0599 · Cited by 1,301
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Cortical network functional connectivity in the descent to sleep
Larson-Prior, L. J., Zempel, J. M., Nolan, T. S., Prior, F. W., Snyder, A. Z., & Raichle, M. E. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0900924106 · Cited by 450
Single-trial EEG–fMRI reveals the dynamics of cognitive function
Debener, S., Ullsperger, M., Siegel, M., & Engel, A. K. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.09.010 · Cited by 428
The amplitude of the resting-state fMRI global signal is related to EEG vigilance measures
Wong, C. W., Olafsson, V., Tal, O., & Liu, T. T. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.07.057 · Cited by 318
Ritter, P., & Villringer, A. (2006) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2006.06.008 · Cited by 294
Wessel, J. R., Danielmeier, C., & Ullsperger, M. (2011) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2011.21635 · Cited by 236
Stomach-brain synchrony reveals a novel, delayed-connectivity resting-state network in humans
Rebollo, I., Devauchelle, A.-D., Béranger, B., & Tallon-Baudry, C. (2018) · eLife · doi:10.7554/elife.33321 · Cited by 194
“Heart rate variability analysis We first removed the MRI gradient artefact from the ECG data using the FMRIB plug-in (Iannetti et al., 2005; Niazy et al., 2005, version 1.21) for EEGLAB (Delorme and Makeig, 2004, version 14.1.1), provided by the University of Oxford Centre for Functional MRI of the…”
MethodInfluentialMultimodal Functional Neuroimaging: Integrating Functional MRI and EEG/MEG
He, B., & Liu, Z. (2008) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2008.2008233 · Cited by 171
Atypical intrinsic neural timescale in autism
Watanabe, T., Rees, G., & Masuda, N. (2019) · eLife · doi:10.7554/elife.42256 · Cited by 150
Bastin, J., Deman, P., David, O., Gueguen, M., Benis, D., Minotti, L., Hoffman, D., Combrisson, E., Kujala, J., Perrone-Bertolotti, M., et al. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhv352 · Cited by 127
Statistical feature extraction for artifact removal from concurrent fMRI-EEG recordings
Liu, Z., de Zwart, J. A., van Gelderen, P., Kuo, L.-W., & Duyn, J. H. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.042 · Cited by 97
Affective interoceptive inference: Evidence from heart‐beat evoked brain potentials
Gentsch, A., Sel, A., Marshall, A. C., & Schütz‐Bosbach, S. (2019) · Human Brain Mapping · doi:10.1002/hbm.24352 · Cited by 76
Infraslow Electroencephalographic and Dynamic Resting State Network Activity
Grooms, J. K., Thompson, G. J., Pan, W.-J., Billings, J., Schumacher, E. H., Epstein, C. M., & Keilholz, S. D. (2017) · Brain Connectivity · doi:10.1089/brain.2017.0492 · Cited by 74
The relation of ventromedial prefrontal cortex activity and heart rate fluctuations at rest
Ziegler, G., Dahnke, R., Yeragani, V. K., & Bär, K. (2009) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2009.07008.x · Cited by 66
Signal Artifacts and Techniques for Artifacts and Noise Removal
Islam, M. K., Rastegarnia, A., & Sanei, S. (2021) · Intelligent Systems Reference Library · doi:10.1007/978-3-030-54932-9_2 · Cited by 54
Neuner, I., Arrubla, J., Felder, J., & Shah, N. J. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.06.048 · Cited by 48
Rebollo, I., & Tallon-Baudry, C. (2022) · The Journal of Neuroscience · doi:10.1523/jneurosci.1285-21.2021 · Cited by 46
I feel what I do: Relating interoceptive processes and reward-related behavior
Marshall, A. C., Gentsch, A., Blum, A.-L., Broering, C., & Schütz-Bosbach, S. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.02.032 · Cited by 28
Performance monitoring during a minimal group manipulation
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Metadata
- Authors
- Niazy, R. K., Beckmann, C. F., Iannetti, G. D., Brady, J. M., & Smith, S. M.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2005-11
- Volume
- 28
- Issue
- 3
- Pages
- 720-737
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2052035564
- Cited by
- 567
- References
- 2
- FWCI
- 7.78
- Type
- journal article
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
