Evidence map›Paper›PMID 36406740›Full record

ArticleCurrent opinion in biomedical engineering2021

Exploring the extent of source imaging: Recent advances in noninvasive electromagnetic brain imaging.

Abbas Sohrabpour, Bin He

Abstract read
In one paragraph

Article in Current opinion in biomedical engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. [A time-frequency transform and Riemannian manifold-based domain adaptation method for motor imagery in brain source space].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026
    Article
  2. Physiologically Guided Modeling for EEG Multichannel Signals.Bioengineering (Basel, Switzerland) · 2026
    Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Non-invasive brain imaging to advance the understanding of human balance.Current opinion in biomedical engineering · 2023
    Article
  9. Article
  10. Article
  11. Article
  12. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Abbas SohrabpourDepartment of Biomedical Engineering, Carnegie Mellon University, United States.
Bin HeDepartment of Biomedical Engineering, Carnegie Mellon University, United States.

Funding

Electrophysiological Source Imaging of Partial EpilepsyR01NS096761 · NINDS · UNIVERSITY OF MINNESOTA · PI HE, BIN · 2016 to 2025
$3.6M
Electrophysiological source imaging guided transcranial focused ultrasoundRF1MH114233 · NIMH · UNIVERSITY OF MINNESOTA · PI HE, BIN · 2017 to 2017
$2.4M
Treating pain in sickle cell disease by means of focused ultrasound neuromodulationU18EB029354 · NIBIB · CARNEGIE-MELLON UNIVERSITY · PI HE, BIN · 2019 to 2019
$2.0M
Mind-body awareness training and brain-computer interfaceR01AT009263 · NCCIH · UNIVERSITY OF MINNESOTA · PI HE, BIN · 2016 to 2020
$2.0M
Spatio-temporal Dynamic Imaging of Seizure SourcesR01EB021027 · NIBIB · UNIVERSITY OF MINNESOTA · PI HE, BIN · 2016 to 2020
$1.3M
NCCIH NIH HHS R01 AT009263NIBIB NIH HHS R01 EB021027NIBIB NIH HHS U18 EB029354NIMH NIH HHS RF1 MH114233NINDS NIH HHS R01 NS096761
6 · The paper itself

Abstract

Electrophysiological source imaging (ESI) has been successfully employed in many brain imaging applications during the last 20 years. ESI estimates of underlying brain networks provide millisecond resolution of dynamic brain processes; yet, it remains to be a challenge to further improve the spatial resolution of ESI modality, in particular on its capability of imaging the extent of underlying brain sources. In this review, we discuss the recent developments in signal processing and machine learning that have made it possible to image the extent, i.e. size, of underlying brain sources noninvasively, using scalp electromagnetic measurements from electroencephalogram (EEG) and magnetoencephalogram (MEG) recordings.

Indexed as

Bayesian frameworkEEGElectrophysiological source imagingExtent imagingMEGSparsity

Identifiers

PMID36406740
PMCPMC9674028

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.