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

Artifacts in magnetoneurography data due to endogenous biological noise sou rces, like the cardiac signal, can be four orders of magnitude higher than the signal of interest. Therefore, it is important to establish effective a rtifact reduction methods. We propose a blind source separation algorithm u sing only second-order temporal correlations for cleaning biomagnetic measu rements of evoked responses in the peripheral nervous system. The algorithm showed its efficiency by eliminating disturbances originating from biologi cal and technical noise sources and successfully extracting the signal of i nterest. This yields a significant improvement of the neuro-magnetic source analysis.