A Particle Swarm Optimised Independence Estimator for Blind Source
Separation of Neurophysiological Time Series
Abstract
The decomposition of neurophysiological recordings into their
constituent neural sources is of major importance to a diverse range of
neuroscientific fields and neuroengineering applications. The advent of
high density electrode probes and arrays has driven a major need for
novel semi-automated and automated blind source separation methodologies
that take advantage of the increased spatial resolution and coverage
these new devices offer. Independent component analysis (ICA) offers a
principled theoretical framework for such algorithms, but implementation
inefficiencies often drive poor performance in practice, particularly
for sparse sources. Here we observe that the use of a single non-linear
optimization function to identify spiking sources with ICA often has a
detrimental effect that precludes the recovery and correct separation of
all spiking sources in the signal. We go on to propose a
projection-pursuit ICA algorithm designed specifically for spiking
sources, which uses a particle swarm methodology to adaptively traverse
a polynomial family of non-linearities approximating the asymmetric
cumulants of the sources. We robustly prove state-of-the-art
decomposition performance on recordings from high density intramuscular
probes and demonstrate how the particle swarm quickly finds optimal
contrast non-linearities across a range of neurophysiological datasets.