LAUSR

ABSTRACT Magnetoencephalography (MEG) data is subject to many sources of environmental noise, and interference rejection is a necessary step in the processing of MEG data. Large amplitude interference caused by sources near the brain have been common in clinical settings and are difficult to reject. Artifact from vagal nerve stimulators (VNS) is a prototypical example. In this study, we describe a novel MEG interference rejection algorithm called dual signal subspace projection (DSSP), and evaluate its performance in clinical MEG data from people with epilepsy and implanted VNS. The performance of DSSP was evaluated in a retrospective cohort study of patients with epilepsy and VNS who had MEG scans for source localization of interictal epileptiform discharges. DSSP was applied to the MEG data and compared with benchmark for performance. We evaluated the clinical impact of interference rejection based on human expert detection and estimation of the location and time‐course of interictal spikes, using an empirical Bayesian source reconstruction algorithm (Champagne). Clinical recordings, after DSSP processing, became more readable and a greater number of interictal epileptic spikes could be clearly identified. Source localization results of interictal spikes also significantly improved from those achieved before DSSP processing, including meaningful estimates of activity time courses. Therefore, DSSP is a valuable novel interference rejection algorithm that can be successfully deployed for the removal of strong artifacts and interferences in MEG. HIGHLIGHTSPerformance evaluation of a novel MEG interference rejection algorithm called dual signal subspace projection (DSSP) in clinical MEG data from patients with epilepsy and implanted vagus nerve stimulators (VNS).DSSP shows significant low‐frequency reduction of artifacts from VNS, and increases the detection and identification of interictal epileptic spikes.DSSP improved the timing and localization of identified epileptic spikes.DSSP is a valuable novel interference rejection algorithm that can be successfully deployed for the removal of strong artifacts and interferences in MEG.