The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Quantification of the benefit from integrating MEG and EEG data in minimum ℓ2-norm estimation
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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“To evaluate the differences between the arrays in linear distributed-source estimation, we used metrics derived from the resolution matrix (Hauk et al., 2011; Liu et al., 2002; Molins et al., 2008; de Peralta Menendez et al., 1997; Stenroos and Hauk, 2013).”
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Metadata
- Authors
- Molins, A., Stufflebeam, S. M., Brown, E. N., & Hämäläinen, M. S.
- Affiliations
- Massachusetts General Hospital (US)
- Harvard University (US)
- Corresponding author
- Molins, A.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2008-09
- Volume
- 42
- Issue
- 3
- Pages
- 1069-1077
- ISSN
- 1053-8119 (ISSN-L)
- 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
- 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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