Artifact reduction in magnetoneurography based on time-delayed second order correlations

Artifacts in magnetoneurography (MNG) data due to endogenous biological noise sources, e.g. heart signal, can be four orders of magnitude higher than the signal of interest. Therefore it is important to establish effective artifact reduction methods. We propose a blind source separation algorithm using only second order temporal correlations for cleaning bio-magnetic measurements of evoked responses in the peripheral nervous system. The algorithm showed its efficiency by eliminating disturbances originating from biological and technical noise sources and successfully extracting the signal of interest. This yields a significant improvement of the neuromagnetic source analysis.