Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile
Articles by William A. Wartman
Improving EEG Forward Modeling Using High-Resolution Five-Layer BEM-FMM Head Models: Effect on Source Reconstruction Accuracy
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
Fast and Accurate EEG/MEG BEM-Based Forward Problem Solution for High-Resolution Head Models
Abstract A BEM (boundary element method) based approach is developed to accurately solve an EEG/MEG forward problem for a modern high-resolution head model in approximately 60 seconds using a common workstation. The method utilizes a charge-based BEM with fast multipole acceleration (BEM-FMM) and a "smart" mesh pre-refinement (called b-refinement) close to the singular source(s).
Accuracy of dipole source reconstruction in the 3-layer BEM model against the 5-layer BEM-FMM model
Abstract Objective: To compare cortical dipole fitting spatial accuracy between the widely used yet highly simplified 3-layer and modern more realistic 5-layer BEM-FMM models with and without adaptive mesh refinement (AMR) methods. Methods: We generate simulated noiseless 256-channel EEG data from 5-layer (7-compartment) meshes of 15 subjects from the Connectome Young Adult dataset.
Software Package for Transcranial Magnetic Stimulation Coil and Coil Array Analysis and Design
Abstract Objective: This study aims to describe a MATLAB software package for transcranial magnetic stimulation (TMS) coil analysis and design. Approach: Electric and magnetic fields of the coils as well as their self- and mutual (for coil arrays) inductances are computed, with or without a magnetic core. Solid and stranded (Litz wire) conductors are also taken into consideration. The starting point is the centerline of a coil conductor(s), which is a 3D curve defined by the user.
An Adaptive H-Refinement Method for the Boundary Element Fast Multipole Method for Quasi-static Electromagnetic Modeling
Abstract Objective: In our recent work pertinent to modeling of brain stimulation and neurophysiological recordings, substantial modeling errors in the computed electric field and potential have sometimes been observed for standard multi-compartment head models. The goal of this study is to quantify those errors and, further, eliminate them through an adaptive mesh refinement (AMR) algorithm.
The effect of meninges on the electric fields in TES and TMS. Numerical modeling with adaptive mesh refinement
Highlights • The impact of the meningeal layers on the electric field in TES and TMS is investigated. • The boundary element fast multipole method with adaptive mesh refinement is used. • TES fields in the cortex increase by 30% when the meninges are included. • TMS fields decrease by 5% on average when the meninges are included. • The solver, the mesh generator, and an example data set is made publicly available.
Show More
loading
Actions
Is this you?
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.Get in touch with William A.
Contact William A., search articles and posts on X, monitor coverage, and track replies from one place.
Learn more about Muck Rack