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

External noise removed from magnetoencephalographic signal using Independent Component Analyses of reference channels