Cited by 192EEG and Brain-Computer InterfacesNumerical methods in inverse problemsDirection-of-Arrival Estimation Techniques

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Quantification of the benefit from integrating MEG and EEG data in minimum ℓ2-norm estimation

Molins, A., Stufflebeam, S.M., Brown, E.N., Hämäläinen, M.S.·2008·NeuroImage

DOI 10.1016/j.neuroimage.2008.05.064

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Abstract

Source current estimation from electromagnetic (MEG and EEG) signals is an ill-posed problem that often produces blurry or inaccurately positioned estimates. The two modalities have distinct factors limiting the resolution, e.g., MEG cannot detect radially oriented sources, while EEG is sensitive to accuracy of the head model. This makes combined EEG+MEG estimation techniques desirable, but different acquisition noise statistics, complexity of the head models, and lack of pertinent metrics all complicate the assessment of the resulting improvements. We investigated analytically the effect of including EEG recordings in MEG studies versus the addition of new MEG channels when computing noise-normalized minimum l(2)-norm estimates. Three-compartment boundary-element forward models were constructed using structural MRI scans for four subjects. Singular value analysis of the resulting forward models predicted better performance of the EEG+MEG case in the form of higher matrix rank. MNE inverse operators for EEG, MEG and EEG+MEG were constructed using the sensor noise covariance estimated from data. Metrics derived from the resolution matrices predicted higher spatial resolution in EEG+MEG as compared to MEG due to decreased spread (lower spatial dispersion, higher resolution index) with no reduction in dipole localization error. The effect was apparent in all source locations, with increased magnitude for deep areas such as the cingulate cortex. We were also able to corroborate the results for the somatosensory cortex using median nerve responses.

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Menke, W. H. (1989) · Cited by 2,843
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Authors: Molins, A., Stufflebeam, S. M., Brown, E. N., & Hämäläinen, M. S.
Affiliations: Massachusetts General Hospital (US)
Harvard University (US)
Corresponding author: Molins, A.
ORCID: Stufflebeam, S.M.
Brown, E.N.
Hämäläinen, M.S.
Venue: NeuroImage
Publisher: Elsevier BV
Published: 2008-09
Volume: 42
Issue: 3
Pages: 1069-1077
ISSN: 1053-8119 (ISSN-L)
DOI: 10.1016/j.neuroimage.2008.05.064
OpenAlex: W2007891950
Cited by: 192
References: 29
Citations by year: 2026: 4 · 2025: 11 · 2024: 14 · 2023: 12 · 2022: 9 · 2021: 11 · 2020: 10 · 2019: 15 · 2018: 14 · 2017: 9 · 2016: 13 · 2015: 8 · 2014: 9 · 2013: 17 · 2012: 16
FWCI: 3.46
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: NeuroImage (publishedVersion)
PubMed (publishedVersion)
Funders: National Institutes of Health (DP1OD003646)
National Institute of Mental Health (K08MH067966)
National Institute on Drug Abuse (R01DA015644)
National Institute of Biomedical Imaging and Bioengineering (R01 EB06385, R01EB006385)
National Center for Research Resources (P41-RR-14075, P41RR014075)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
Physical Sciences › Mathematics › Mathematical Physics › Numerical methods in inverse problems
Physical Sciences › Computer Science › Signal Processing › Direction-of-Arrival Estimation Techniques
Keywords: inverse problems, forward model, singular value analysis, resolution matrix, spatial resolution, dipole source localization, cingulate cortex, somatosensory cortex, boundary element model
MeSH terms: Adolescent, Adult, Brain, Brain Mapping, Electroencephalography, Epilepsy, Humans, Signal Processing, Computer-Assisted, Magnetoencephalography

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Quantification of the benefit from integrating MEG and EEG data in minimum ℓ2-norm estimation

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