An Iterative Implementation of the Signal Space Separation Method for Magnetoencephalography Systems with Low Channel Counts.

ABSTRACT: The signal space separation (SSS) method is routinely employed in the analysis of multichannel magnetic field recordings (such as magnetoencephalography (MEG) data). In the SSS method, signal vectors are posed as a multipole expansion of the magnetic field, allowing contributions from sources internal and external to a sensor array to be separated via computation of the pseudo-inverse of a matrix of the basis vectors. Although powerful, the standard implementation of the SSS method on MEG systems based on optically pumped magnetometers (OPMs) is unstable due to the approximate parity of the required number of dimensions of the SSS basis and the number of channels in the data. Here we exploit the hierarchical nature of the multipole expansion to perform a stable, iterative implementation of

SUBMITTER: Holmes N 

PROVIDER: S-EPMC10385807 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature