A unified view on beamformers for M/EEG source reconstruction
Britta U. Westnera b c d✉, Sarang S. Dalalc e, Alexandre Gramfortf g, Vladimir Litvak Ah i, John C. Mosherj k, Robert Oostenvelda d l m, Jan‐Mathijs Schoffelena d
- aRadboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
- bCenter of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark. Electronic address: britta.westner@donders.ru.nl
- cAarhus University [Aarhus]
- dDonders Institute for Brain, Cognition and Behaviour
- eCenter of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- fUniversité Paris-Saclay, Inria, CEA, Palaiseau, France
- gModelling brain structure, function and variability based on high-field MRI data
- hWellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, UK
- iNMR Research Unit [London]
- jTexas Institute for Restorative Neurotechnologies, McGovern Medical School, University of Texas Health Science Center at Houston, TX USA
- k
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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.