MNE-Python: Signal space separation for denoising neural MEG data

by Mark Wronkiewicz for Python Software Foundation

Signal source separation (SSS) is a powerful algorithm used to denoise neural signals acquired with magnetoenchaphalography (MEG). It uses Maxwell’s equations and spherical harmonics to separate measurements that originate from a space occupied by the MEG sensors (containing neural signals) from those originating outside this space (environmental noise). SSS has had a positive impact despite its purely proprietary implementation, so I plan to incorporate it into the open MNE-Python library.