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Exploring and Evaluating Novel Algorithm for Interference Rejection in MEG Data

Abstract

Introduction: MEG recordings are prone to interferences. Lots of efforts have been made to deal with interference rejection in MEG data, but still there are some kinds of interferences could not be handled well so far, e.g. large amplitude interferences like those caused by vagus nerve stimulators (VNS) implant in epilepsy patients, and dealing with these interferences well are of clinical importance for these patients. In this study, we illustrated a novel MEG interference rejection algorithm called dual signal subspace projection (DSSP) that could potentially help deal with such interferences, and evaluated its performance through computer simulation data and clinical data.

Method: Computer simulation is performed with several types of interferences varying in localization and signal type, and DSPP-processed data was compared with the true signal for performance evaluation. The performance of DSSP in the clinical data was mainly tested through a retrospective cohort study from epilepsy patients with VNS implant receiving MEG for interictal spike mapping. The evaluation was based on direct MEG recording reading for epileptic spike identification, successful spike localization using the clinical standard dipole fitting model, and spike localization and activity time-series recovery using a more reliable MEG source reconstruction algorithms called Champagne.

Results: DSSP handled various kinds of interference setting in computer simulation well, but also showed preference towards periodic interference outside region of interest. Direct MEG recording reading showed that MEG recordings became more readable after DSSP processing and more epileptic spikes could be identified. More spike could be localized using the dipole fitting method with DSSP-processed data, and localization results differed from that achieved with DSSP- unprocessed data. Evaluation with Champagne algorithm showed that DSSP-processed data had a higher chance to achieve successful spike localization with reasonable location and meaningful recovered activity time-series data. The localization result achieved with Champagne algorithm also differed from the previous spike mapping result using the DSSP-unprocessed data.

Conclusion: DSSP is a valuable novel interference rejection algorithm to be explored. It potentially works better with interference periodic and located outside the region of interest, and could help to recover the distorted MEG recordings from epilepsy patient with VNS impact.

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