20 citations · 37 across the 10 of their papers we have counts for
10 papers
Biophysics-informed deep operator learning for inverse problems with application to electrophysiological source reconstruction
Eardi Lila, Erica R. Peterson, Alexis N. Bosseler +2
Electrophysiological brain signals are typically acquired through indirect and noisy measurements, providing transformed representations of the underlying neural activity. Source r…
A Minimum Assumption Approach to MEG Sensor Array Design
Andrey Zhdanov, Jussi Nurminen, Joonas Iivanainen +1
Objective: Our objective is to formulate the problem of the Magnetoencephalographic (MEG) sensor array design as a well-posed engineering problem of accurately measuring the neuron…
The effect of spatial sampling on the resolution of the magnetostatic inverse problem
Jussi Nurminen, Andrey Zhdanov, Wan Jin Yeo +6
In magnetoencephalography, linear minimum norm inverse methods are commonly employed when a solution with minimal a priori assumptions is desirable. These methods typically produce…
On the non-uniqueness of the quasi-static magnetic forward problem and its impact on source current reconstruction
Wan-Jin Yeo, Yao-Rui Yeo, Aaron Miller +1
We introduce a formulation where individual line segments of a current loop have translationally non-invariant contributions to the electro-quasi-static magnetic scalar potential a…
Effects of head modeling errors on the spatial frequency representation of MEG
Wan-Jin Yeo, Eric Larson, Joonas Iivanainen +5
Optically-pumped magnetometers (OPM) -- next-generation magnetoencephalography (MEG) sensors -- may be placed directly on the head, unlike the more commonly used superconducting qu…
Towards analytical calculation of the magnetic flux measured by magnetometers
Wan-Jin Yeo, Yao-Rui Yeo, Samu Taulu
This paper reviews magnetic flux signal calculations through pick-up loops using vector spherical harmonic expansion under the quasi-static approximation, and presents a near-analy…