Preprint Article Version 1 This version is not peer-reviewed

The Impact of the Choice of Magnetometers and Gradiometers in Source Reconstruction after Signal Space Separation

Version 1 : Received: 26 October 2017 / Approved: 27 October 2017 / Online: 27 October 2017 (03:50:25 CEST)

A peer-reviewed article of this Preprint also exists.

Garcés, P.; López-Sanz, D.; Maestú, F.; Pereda, E. Choice of Magnetometers and Gradiometers after Signal Space Separation. Sensors 2017, 17, 2926. Garcés, P.; López-Sanz, D.; Maestú, F.; Pereda, E. Choice of Magnetometers and Gradiometers after Signal Space Separation. Sensors 2017, 17, 2926.

Journal reference: Sensors 2017, 17, 2926
DOI: 10.3390/s17122926

Abstract

Background: Modern MEG devices include 102 sensor triplets containing one magnetometer and two planar gradiometers. The first processing step is often a signal space separation (SSS), which provides a powerful noise reduction. A question commonly raised by researchers and reviewers is which data should be employed in source reconstruction: (1) magnetometers only, (2) gradiometers only, (3) magnetometers and gradiometers together. The MEG community is currently divided about the proper answer and strong arguments in favor and against these three approaches often expressed. Methods: First, we provide theoretical evidence that both gradiometers and magnetometers contain the same information after SSS, and argue that they both result from the backprojection of the same SSS components. Then, we compare beamforming source reconstructions from magnetometers and gradiometers in real MEG recordings before and after SSS. Results: Without SSS, the correlation between source time series extracted from magnetometers and gradiometers was high, with Pearson correlation coefficient r=0.5-0.8. After SSS, these correlation values increased dramatically, reaching over 0.90 across all cortical areas. Conclusions: After SSS, almost identical source reconstructions (r>0.9) can be obtained with magnetometers and gradiometers, as long as regularization is selected appropriately to account for the different properties in magnetometers and gradiometers covariance matrices.

Subject Areas

magnetoencephalography; signal space separation; magnetometer; gradiometer; beamforming; regularization

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