  Background: Many magnetoencephalographs (MEG) contain, in addition to data
channels, a set of reference channels positioned relatively far from the head
that provide information on magnetic fields not originating from the brain.
This information is used to subtract sources of non-neural origin, with either
geometrical or least mean squares (LMS) methods. LMS methods in particular tend
to be biased toward more constant noise sources and are often unable to remove
intermittent noise.
  New Method: To better identify and eliminate external magnetic noise, we
propose performing ICA directly on the MEG reference channels. This in most
cases produces several components which are clear summaries of external noise
sources with distinct spatio-temporal patterns. We present two algorithms for
identifying and removing such noise components from the data which can in many
cases significantly improve data quality.
  Results: We performed simulations using forward models that contained both
brain sources and external noise sources. First, traditional LMS-based methods
were applied. While this removed a large amount of noise, a significant portion
still remained. In many cases, this portion could be removed using the proposed
technique, with little to no false positives.
  Comparison with existing method(s): The proposed method removes significant
amounts of noise to which existing LMS-based methods tend to be insensitive.
  Conclusions: The proposed method complements and extends traditional
reference based noise correction with little extra computational cost and low
chances of false positives. Any MEG system with reference channels could profit
from its use, particularly in labs with intermittent noise sources.
