ArticleBrain topography2015
Real-Time MEG Source Localization Using Regional Clustering.
Article in Brain topography, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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.
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Who cites it
11 citing papers in PubMed.
- Review
- Decoding visual object recognition from EEG signals.PloS one · 2026Article
- Cortical parcellation optimized for magnetoencephalography with a clustering technique.Scientific reports · 2025Article
- Online functional connectivity analysis of large all-to-all networks in MNE Scan.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Magnetoencephalography for epileptic focus localization based on Tucker decomposition with ripple window.CNS neuroscience & therapeutics · 2021Article
- RT-NET: real-time reconstruction of neural activity using high-density electroencephalography.Neuroinformatics · 2021Article
- Contextual MEG and EEG Source Estimates Using Spatiotemporal LSTM Networks.Frontiers in neuroscience · 2021Article
- Computationally Efficient Implementation of Aperture Domain Model Image Reconstruction.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2019Article
- MNE Scan: Software for real-time processing of electrophysiological data.Journal of neuroscience methods · 2018Article
- Real-Time Clustered Multiple Signal Classification (RTC-MUSIC).Brain topography · 2018Article
- The Iterative Reweighted Mixed-Norm Estimate for Spatio-Temporal MEG/EEG Source Reconstruction.IEEE transactions on medical imaging · 2016Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
With its millisecond temporal resolution, Magnetoencephalography (MEG) is well suited for real-time monitoring of brain activity. Real-time feedback allows the adaption of the experiment to the subject's reaction and increases time efficiency by shortening acquisition and off-line analysis. Two formidable challenges exist in real-time analysis: the low signal-to-noise ratio (SNR) and the limited time available for computations. Since the low SNR reduces the number of distinguishable sources, we propose an approach which downsizes the source space based on a cortical atlas and allows to discern the sources in the presence of noise. Each cortical region is represented by a small set of dipoles, which is obtained by a clustering algorithm. Using this approach, we adapted dynamic statistical parametric mapping for real-time source localization. In terms of point spread and crosstalk between regions the proposed clustering technique performs better than selecting spatially evenly distributed dipoles. We conducted real-time source localization on MEG data from an auditory experiment. The results demonstrate that the proposed real-time method localizes sources reliably in the superior temporal gyrus. We conclude that real-time source estimation based on MEG is a feasible, useful addition to the standard on-line processing methods, and enables feedback based on neural activity during the measurements.
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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.