Automated source separation algorithms have become a central tool in neuroengineering and neuroscience, where
they are used to decompose neurophysiological signal into its constituent spiking sources. However, in noisy or highly multivariate
recordings these decomposition techniques often make a large
number of errors. Such mistakes degrade online human-machine
interfacing methods and require costly post-hoc manual cleaning
in the offline setting. In this paper we propose an automated error
correction methodology using a deep metric learning (DML)
framework, generating embedding spaces in which spiking events
can be both identified and assigned to their respective sources.
Furthermore, we investigate the relative ability of different DML
techniques to preserve the intra-class semantic structure needed
to identify incorrect class labels in neurophysiological time series.
Motivated by this analysis, we propose locality sensitive mining,
an easily implemented sampling-based augmentation to typical
DML losses which substantially improves the local semantic
structure of the embedding space. We demonstrate the utility
of this method to generate embedding spaces which can be used
to automatically identify incorrectly-labelled spiking events with
high accuracy