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Objective In functional electrophysiological imaging, signals are often contaminated by interference that can be of considerable magnitude compared to the signals of interest. This paper proposes a novel algorithm for removing such interferences that does not require separate noise measurements. Approach The algorithm is based on a dual definition of the signal subspace in the spatial- and time-domains. Since the algorithm makes use of this duality, it is named the dual signal subspace projection (DSSP). The DSSP algorithm first projects the columns of the measured data matrix onto the inside and outside of the spatial-domain signal subspace, creating a set of two preprocessed data matrices. The intersection of the row spans of these two matrices is estimated as the time-domain interference subspace. The original data matrix is projected onto the subspace that is orthogonal to the interference subspace Main results The DSSP algorithm is validated first by using the computer generated data, and then by using two sets of real biomagnetic data: SCEF data measured from a healthy volunteer and MEG data from a patient with a vagus nerve stimulator. Significance The proposed DSSP algorithm is effective for removing overlapped interference in a wide variety of biomagnetic measurements.

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Diagnosis of nervous system disease is greatly aided by functional assessments and imaging techniques that localize neural activity abnormalities. Electrophysiological methods are helpful but often insufficient to locate neural lesions precisely. One proposed noninvasive alternative is magnetoneurography (MNG); we have developed MNG of the spinal cord (magnetospinography, MSG). Using a 120-channel superconducting quantum interference device biomagnetometer system in a magnetically shielded room, cervical spinal cord evoked magnetic fields (SCEFs) were recorded after stimulation of the lower thoracic cord in healthy subjects and a patient with cervical spondylotic myelopathy and after median nerve stimulation in healthy subjects. Electrophysiological activities in the spinal cord were reconstructed from SCEFs and visualized by a spatial filter, a recursive null-steering beamformer. Here, we show for the first time that MSG with high spatial and temporal resolution can be used to map electrophysiological activities in the cervical spinal cord and spinal nerve.

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