  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.
