J
John C. Mosher
Researcher at University of Texas Health Science Center at Houston
Publications - 161
Citations - 12751
John C. Mosher is an academic researcher from University of Texas Health Science Center at Houston. The author has contributed to research in topics: Magnetoencephalography & Ictal. The author has an hindex of 41, co-authored 154 publications receiving 11071 citations. Previous affiliations of John C. Mosher include University of Southern California & Los Alamos National Laboratory.
Papers
More filters
Journal ArticleDOI
Brainstorm: a user-friendly application for MEG/EEG analysis
TL;DR: Brainstorm as discussed by the authors is a collaborative open-source application dedicated to magnetoencephalography (MEG) and EEG data visualization and processing, with an emphasis on cortical source estimation techniques and their integration with anatomical magnetic resonance imaging (MRI) data.
Journal ArticleDOI
Electromagnetic brain mapping
TL;DR: The underlying models currently used in MEG/EEG source estimation are described and the various signal processing steps required to compute these sources are described.
Journal ArticleDOI
Multiple dipole modeling and localization from spatio-temporal MEG data
TL;DR: The authors present general descriptive models for spatiotemporal MEG (magnetoencephalogram) data and show the separability of the linear moment parameters and nonlinear location parameters in the MEG problem and present a subspace methodology and computational approach to solving the conventional least-squares problem.
Journal ArticleDOI
EEG and MEG: forward solutions for inverse methods
TL;DR: Novel reformulations of the basic EEG and MEG kernels that dispel the myth that EEG is inherently more complicated to calculate than MEG are presented and evidence that improvements over currently published BEM methods can be realized using alternative error-weighting methods is presented.
Journal ArticleDOI
A sensor-weighted overlapping-sphere head model and exhaustive head model comparison for MEG.
TL;DR: A sensor-weighted overlapping-sphere (OS) head model for rapid calculation of more realistic head shapes and a novel comparison technique that is based on a generalized eigenvalue decomposition and accounts for the presence of noise in the MEG data is introduced.