Beamspace dual signal space projection (bDSSP): a method for selective detection of deep sources in MEG measurements

Abstract

Objective. Magnetoencephalography (MEG) has a well-recognized weakness at detecting deeper brain activities. This paper proposes a novel algorithm for selective detection of deep sources by suppressing interference signals from superficial sources in MEG measurements. Approach. The proposed algorithm combines the beamspace preprocessing method with the dual signal space projection (DSSP) interference suppression method. A prerequisite of the proposed algorithm is prior knowledge of the location of the deep sources. The proposed algorithm first derives the basis vectors that span a local region just covering the locations of the deep sources. It then estimates the time-domain signal subspace of the superficial sources by using the projector composed of these basis vectors. Signals from the deep sources are extracted by projecting the row space of the data matrix onto the direction orthogonal to the signal subspace of the superficial sources. Main results. Compared with the previously proposed beamspace signal space separation (SSS) method, the proposed algorithm is capable of suppressing much stronger interference from superficial sources. This capability is demonstrated in our computer simulation as well as experiments using phantom data. Significance. The proposed bDSSP algorithm can be a powerful tool in studies of physiological functions of midbrain and deep brain structures.

Spatiotemporal signal space separation for regions of interest: Application for extracting neuromagnetic responses evoked by deep brain stimulation

“…In contrast, previous studies addressing the issue of source leakage have proposed post‐hoc time series orthogonalization approaches that are employed following source reconstruction (Brookes et al, 2012 ; Colclough et al, 2015 ). Of interest, although ROI‐tSSS is independent of forward model specification, there are related ROI‐based spatial filtering techniques, which rely on eigendecomposition of source leadfields within an ROI (Oswal et al, 2014 ; Rodríguez‐Rivera et al, 2006 ; Sekihara et al, 2018 ). It would be interesting to compare such leadfield‐based approaches to ROI‐tSSS in future studies.…”

“…Temporal projection is subsequently applied to remove instantaneously correlated (at zero‐lag) components representing leakage between these two subspaces. Interestingly, our approach bears resemblance to leadfield‐based spatial filtering (beamspace) algorithms (Cai et al, 2019 ; Oswal et al, 2014 ; Rodríguez‐Rivera et al, 2006 ; Sekihara et al, 2016 ; Sekihara et al, 2018 ), but a key difference is that it is independent of the computation of a forward model.…”