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Westner BU, Dalal SS, Gramfort A, et al. A unified view on beamformers for M/EEG source reconstruction. Neuroimage. 2021;246:118789doi: 10.1016/j.neuroimage.2021.118789.
Westner, B. U., Dalal, S. S., Gramfort, A., Litvak, V., Mosher, J. C., Oostenveld, R., & Schoffelen, J. M. (2021). A unified view on beamformers for M/EEG source reconstruction. NeuroImage, 246118789. https://doi.org/10.1016/j.neuroimage.2021.118789
Westner, Britta U, et al. "A unified view on beamformers for M/EEG source reconstruction." NeuroImage vol. 246 (2021): 118789. doi: https://doi.org/10.1016/j.neuroimage.2021.118789
Westner BU, Dalal SS, Gramfort A, Litvak V, Mosher JC, Oostenveld R, Schoffelen JM. A unified view on beamformers for M/EEG source reconstruction. Neuroimage. 2021 Dec 07;246:118789. doi: 10.1016/j.neuroimage.2021.118789. Epub 2021 Dec 07. PMID: 34890794.
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Neuroimage. 2021 Dec 07;246:118789. doi: 10.1016/j.neuroimage.2021.118789. Epub 2021 Dec 07.

A unified view on beamformers for M/EEG source reconstruction.

NeuroImage

Britta U Westner, Sarang S Dalal, Alexandre Gramfort, Vladimir Litvak, John C Mosher, Robert Oostenveld, Jan-Mathijs Schoffelen

Affiliations

  1. Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands; Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark. Electronic address: [email protected].
  2. Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
  3. Université Paris-Saclay, Inria, CEA, Palaiseau, France.
  4. Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, UK.
  5. Texas Institute for Restorative Neurotechnologies, McGovern Medical School, University of Texas Health Science Center at Houston, TX USA.
  6. Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands; NatMEG, Karolinska Institutet, Stockholm, Sweden.
  7. Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands.

PMID: 34890794 DOI: 10.1016/j.neuroimage.2021.118789

Abstract

Beamforming is a popular method for functional source reconstruction using magnetoencephalography (MEG) and electroencephalography (EEG) data. Beamformers, which were first proposed for MEG more than two decades ago, have since been applied in hundreds of studies, demonstrating that they are a versatile and robust tool for neuroscience. However, certain characteristics of beamformers remain somewhat elusive and there currently does not exist a unified documentation of the mathematical underpinnings and computational subtleties of beamformers as implemented in the most widely used academic open source software packages for MEG analysis (Brainstorm, FieldTrip, MNE, and SPM). Here, we provide such documentation that aims at providing the mathematical background of beamforming and unifying the terminology. Beamformer implementations are compared across toolboxes and pitfalls of beamforming analyses are discussed. Specifically, we provide details on handling rank deficient covariance matrices, prewhitening, the rank reduction of forward fields, and on the combination of heterogeneous sensor types, such as magnetometers and gradiometers. The overall aim of this paper is to contribute to contemporary efforts towards higher levels of computational transparency in functional neuroimaging.

Copyright © 2021. Published by Elsevier Inc.

Keywords: Beamforming; Data analysis; EEG; MEG; Source imaging; Source localization; Source reconstruction

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