Keyword search (4,163 papers available)

"sensitivity analysis" Keyword-tagged Publications:

Title Authors PubMed ID
1 Assessment of urban greenhouse gas emissions towards reduction planning and low-carbon city: a case study of Montreal, Canada Shadnoush Pashaei 38638449
ENCS
2 A DiffeRential Evolution Adaptive Metropolis (DREAM)-based inverse model for continuous release source identification in river pollution incidents: Quantitative evaluation and sensitivity analysis Zhu Y; Cao H; Gao Z; Chen Z; 38309421
ENCS
3 Development and performance assessment of a new opensource Bayesian inference R platform for building energy model calibration Hou D; Zhan D; Wang L; Hassan IG; Sezer N; 37936825
ENCS
4 Development of a DREAM-based inverse model for multi-point source identification in river pollution incidents: Model testing and uncertainty analysis Zhu Y; Chen Z; 36191500
ENCS
5 Identification of point source emission in river pollution incidents based on Bayesian inference and genetic algorithm: Inverse modeling, sensitivity, and uncertainty analysis Zhu Y; Chen Z; Asif Z; 34380214
ENCS
6 Assessment of regional greenhouse gas emission from beef cattle production: A case study of Saskatchewan in Canada. Chen Z, An C, Fang H, Zhang Y, Zhou Z, Zhou Y, Zhao S 32217321
ENCS
7 Influence of Head Tissue Conductivity Uncertainties on EEG Dipole Reconstruction. Vorwerk J, Aydin Ü, Wolters CH, Butson CR 31231178
PERFORM

 

Title:Influence of Head Tissue Conductivity Uncertainties on EEG Dipole Reconstruction.
Authors:Vorwerk J,  Aydin Ü,  Wolters CH,  Butson CR
Link:https://www.ncbi.nlm.nih.gov/pubmed/31231178?dopt=Abstract
DOI:10.3389/fnins.2019.00531
Publication:Frontiers in neuroscience
Keywords:EEG dipole reconstruction;  EEG source analysis;  conductivity estimation;  conductivity uncertainty;  finite element method;  generalized polynomial chaos;  head modeling;  sensitivity analysis; 
PMID:31231178 Category:Front Neurosci Date Added:2019-06-25
Dept Affiliation: PERFORM
1 Scientific Computing & Imaging (SCI) Institute, University of Utah, Salt Lake City, UT, United States.
2 Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany.
3 Institute of Electrical and Biomedical Engineering, UMIT - University for Health Sciences, Medical Informatics and Technology, Hall in Tirol, Austria.
4 Multimodal Functional Imaging Lab, Department of Physics and PERFORM Centre, Concordia University, Montreal, QC, Canada.
5 Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany.
6 Departments of Biomedical Engineering, Neurology, and Psychiatry, University of Utah, Salt Lake City, UT, United States.
7 Department of Neurosurgery, Clinical Neurosciences Center, University of Utah, Salt Lake City, UT, United States.

Description:

Influence of Head Tissue Conductivity Uncertainties on EEG Dipole Reconstruction.

Front Neurosci. 2019;13:531

Authors: Vorwerk J, Aydin Ü, Wolters CH, Butson CR

Abstract

Reliable EEG source analysis depends on sufficiently detailed and accurate head models. In this study, we investigate how uncertainties inherent to the experimentally determined conductivity values of the different conductive compartments influence the results of EEG source analysis. In a single source scenario, the superficial and focal somatosensory P20/N20 component, we analyze the influence of varying conductivities on dipole reconstructions using a generalized polynomial chaos (gPC) approach. We find that in particular the conductivity uncertainties for skin and skull have a significant influence on the EEG inverse solution, leading to variations in source localization by several centimeters. The conductivity uncertainties for gray and white matter were found to have little influence on the source localization, but a strong influence on the strength and orientation of the reconstructed source, respectively. As the CSF conductivity is most accurately determined of all conductivities in a realistic head model, CSF conductivity uncertainties had a negligible influence on the source reconstruction. This small uncertainty is a further benefit of distinguishing the CSF in realistic volume conductor models.

PMID: 31231178 [PubMed]




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