The Signal Space Separation method

TL;DR

The Signal Space Separation (SSS) method uses a mathematical expansion to distinguish and separate internal signals from external disturbances in multichannel vector field measurements, improving data quality in applications like magnetoencephalography.

Contribution

The paper introduces the SSS method, a novel approach that employs functional expansion to effectively separate and remove external noise from multichannel vector field data.

Findings

Successfully separates internal and external signals in MEG data.

Effectively removes external disturbances and artifacts.

Enables virtual sensor configurations and movement compensation.

Abstract

Multichannel measurement with hundreds of channels essentially covers all measurable degrees of freedom of a curl and source free vector field, like the magnetic field in a volume free of current sources (e.g. in magnetoencephalography, MEG). A functional expansion solution of Laplace's equation enables one to separate signals arising from the sphere enclosing the interesting sources, e.g. the currents in the brain, from the rest of the signals. The signal space separation (SSS) is accomplished by calculating individual basis vectors for each term of the functional expansion solution to create a signal basis covering all measurable signal vectors. Any signal vector has a unique SSS decomposition with separate coefficients for the interesting signals and signals coming from outside the interesting volume. Thus, SSS basis provides an elegant method to remove external disturbances, and to transform the interesting signals to virtual sensor configurations. SSS can also be used in compensating the movements of the subject and removing the artefacts caused by magnetized particles in the subject.