ArticleNeuroImage2020
Comparison of beamformer implementations for MEG source localization.
Article in NeuroImage, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Somatosensory training: a systematic review and meta-analysis with methodological considerations and clinical insights.Journal of neuroengineering and rehabilitation · 2025Pooled it
- Alpha oscillatory activity reveals focused-attentional disparity between cochlear implant users and normal hearing listeners.Scientific reports · 2026Article
- Spatial Filters-In Search of the Virtual Electrode.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- 3D printed pediatric head phantom for assessing deep epileptic sources localization.Computers in biology and medicine · 2026Article
- Neuroimaging of Ictal MEG: An Evaluation of Magnetic Source Imaging Techniques.Brain topography · 2025Article
- FREQ-NESS Reveals the Dynamic Reconfiguration of Frequency-Resolved Brain Networks During Auditory Stimulation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Wearable Magnetoencephalography in a Lightly Shielded Environment.IEEE transactions on bio-medical engineering · 2025Article
- Pseudo-MRI Engine for MRI-Free Electromagnetic Source Imaging.Human brain mapping · 2025Article
- Joint estimation of source dynamics and interactions from MEG data.Network neuroscience (Cambridge, Mass.) · 2025Article
- Deep source transfer learning for the estimation of internal brain dynamics using scalp EEG.Cognitive neurodynamics · 2024Article
- Comparing the performance of beamformer algorithms in estimating orientations of neural sources.iScience · 2024Article
- Application of deconvolutional networks for feature interpretability in epilepsy detection.Frontiers in neuroscience · 2024Article
- Effective connectivity of working memory performance: a DCM study of MEG data.Frontiers in human neuroscience · 2024Article
- Accelerated algorithms for source orientation detection and spatiotemporal LCMV beamforming in EEG source localization.Frontiers in neuroscience · 2024Article
- Determining the Optimal Stimulation Sessions for TMS-Induced Recovery of Upper Extremity Motor Function Post Stroke: A Randomized Controlled Trial.Brain sciences · 2023Article
- Identifying good practices for detecting inter-regional linear functional connectivity from EEG.NeuroImage · 2023Article
- Spatiotemporal Alterations in Working Memory-Related Beta Band Neuromagnetic Activity of Patients With Schizophrenia On and Off Antipsychotic Medication: Investigation With MEG.Schizophrenia bulletin · 2023Article
- On electromagnetic head digitization in MEG and EEG.Scientific reports · 2023Article
- Brainstorm-DUNEuro: An integrated and user-friendly Finite Element Method for modeling electromagnetic brain activity.NeuroImage · 2023Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
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 (MNE-Python, FieldTrip, DAiSS (SPM12), and Brainstorm) 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 the signal-to-noise ratio (SNR) in all four toolboxes. When applied carefully to MEG data with a typical SNR (3-15 dB), all four toolboxes localized the sources reliably; however, they differed in their sensitivity to preprocessing parameters. As expected, localizations were highly unreliable at very low SNR, but we found high localization error also at very high SNRs for the first three toolboxes while Brainstorm showed greater robustness but with lower spatial resolution. We also found that the SNR improvement offered by SSS led to more accurate localization.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.