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Comparison of beamformer implementations for MEG source localization

Abstract : Beamformers are applied for estimating spatiotemporal characteristics of neuronal sources underlying measured MEG/EEG signals. Several MEG analysis toolboxes include an implementation of a linearly constrained minimum-variance (LCMV) beamformer. However, differences in implementations and in their results complicate the selection and application of beamformers and may hinder their wider adoption in research and clinical use. Additionally, combinations of different MEG sensor types (such as magnetometers and planar gradiometers) and application of preprocessing methods for interference suppression, such as signal space separation (SSS), can affect the results in different ways for different implementations. So far, a systematic evaluation of the different implementations has not been performed. Here, we compared the localization performance of the LCMV beamformer pipelines in four widely used open-source toolboxes (FieldTrip, SPM12, Brainstorm, and MNE-Python) using datasets both with and without SSS interference suppression. We analyzed MEG data that were i) simulated, ii) recorded from a static and moving phantom, and iii) recorded from a healthy volunteer receiving auditory, visual, and somatosensory stimulation. We also investigated the effects of SSS and the combination of the magnetometer and gradiometer signals. We quantified how localization error and point-spread volume vary with SNR in all four toolboxes.
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Contributor : Alexandre Gramfort <>
Submitted on : Monday, November 18, 2019 - 9:37:35 PM
Last modification on : Friday, September 25, 2020 - 3:54:01 PM


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Amit Jaiswal, Jukka Nenonen, Matti Stenroos, Alexandre Gramfort, Sarang Dalal, et al.. Comparison of beamformer implementations for MEG source localization. 2019. ⟨hal-02369296⟩



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