4.7 Article

A unified view on beamformers for M/EEG source reconstruction

期刊

NEUROIMAGE
卷 246, 期 -, 页码 -

出版社

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2021.118789

关键词

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

资金

  1. Innovative Training Network, Child-Brain - Marie Curie Actions of the European Commission (H2020-MSCA-ITN-2014)
  2. Wellcome [203147/Z/16/Z]
  3. MRC UKMEG Partnership grant [MR/K005464/1]
  4. European Union [893912]
  5. ERC [ERC-StG-640448, SLAB ERC-StG-676943]
  6. Netherlands Organisation for Scientific Research (NWO Vidi) [864.14.011]
  7. National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health [R01EB026299, U01EB023820]
  8. [ANR-19-DATA-0023]
  9. Marie Curie Actions (MSCA) [893912] Funding Source: Marie Curie Actions (MSCA)

向作者/读者索取更多资源

This paper provides a unified documentation of the mathematical background and terminology for beamforming, compares beamformer implementations across different toolboxes, and discusses pitfalls and solutions in beamforming analysis.
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.

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