External noise removed from magnetoencephalographic signal using independent component analyses of reference channels

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.

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.

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.

The proposed method removes significant amounts of noise to which existing LMS-based methods tend to be insensitive.

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.