ArticlePloS one2017
Sparse EEG/MEG source estimation via a group lasso.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 24 citations in OpenAlex.
- Dictionary Learning Methods for Brain Activity Mapping with MEG Data.Brain topography · 2026Article
- Improving source estimation of retinotopic MEG responses by combining data from multiple subjects.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Connectome spectrum electromagnetic tomography: A method to reconstruct electrical brain source networks at high-spatial resolution.Human brain mapping · 2024Article
- Exploring an EM-algorithm for banded regression in computational neuroscience.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Estimating neural activity from visual areas using functionally defined EEG templates.Human brain mapping · 2023Article
- EEGSourceSim: A framework for realistic simulation of EEG scalp data using MRI-based forward models and biologically plausible signals and noise.Journal of neuroscience methods · 2019Article
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Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
Abstract
Non-invasive recordings of human brain activity through electroencephalography (EEG) or magnetoencelphalography (MEG) are of value for both basic science and clinical applications in sensory, cognitive, and affective neuroscience. Here we introduce a new approach to estimating the intra-cranial sources of EEG/MEG activity measured from extra-cranial sensors. The approach is based on the group lasso, a sparse-prior inverse that has been adapted to take advantage of functionally-defined regions of interest for the definition of physiologically meaningful groups within a functionally-based common space. Detailed simulations using realistic source-geometries and data from a human Visual Evoked Potential experiment demonstrate that the group-lasso method has improved performance over traditional ℓ2 minimum-norm methods. In addition, we show that pooling source estimates across subjects over functionally defined regions of interest results in improvements in the accuracy of source estimates for both the group-lasso and minimum-norm approaches.
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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.