Evaluation of skull conductivity using SCALE head tissue conductivity estimation using EEG.


Journal

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
ISSN: 2694-0604
Titre abrégé: Annu Int Conf IEEE Eng Med Biol Soc
Pays: United States
ID NLM: 101763872

Informations de publication

Date de publication:
07 2022
Historique:
entrez: 10 9 2022
pubmed: 11 9 2022
medline: 14 9 2022
Statut: ppublish

Résumé

Inaccurate estimation of skull conductivity is the largest impediment to high-resolution EEG source imaging because of its strong influence and wide variability across individuals. Nonetheless, there is yet no widely applied method for noninvasively measuring individual skull conductivity. We presented a skull conductivity and source location estimation algorithm (SCALE) for simultaneously estimating skull conductivity and the cortical distributions of 18-20 effective sources derived from the EEG data by independent component analysis (ICA). SCALE combines a realistic Finite Element Method (FEM) head model built from a magnetic resonance (MR) head image with the effective source scalp maps to estimate brain-to-skull conductivity ratio (BSCR) and to map the effective sources on the cortical surface. To estimate the robustness of SCALE BSCR estimates, we applied SCALE to MR image and high-density EEG data from ten participants, five having data from 2-3 different tasks and sessions. As expected, across participants SCALE BSCR estimates differed widely (mean 32.8, range 18-78). Within-participant SCALE BSCR estimates were far more consistent than between participants. By incorporating SCALE-optimized distributed EEG source localization, stable functional imaging of cortical EEG effective sources can become routine, giving relatively low-cost EEG imaging a spatial resolution compatible with other brain imaging results and uniquely capable for studying brain dynamics supporting thought and action in laboratory, virtual, and natural environments.

Identifiants

pubmed: 36086241
doi: 10.1109/EMBC48229.2022.9872004
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

4826-4829

Subventions

Organisme : NINDS NIH HHS
ID : R01 NS047293
Pays : United States
Organisme : NIBIB NIH HHS
ID : R01 EB023297
Pays : United States

Auteurs

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Classifications MeSH