ArticleBrain communications2023
Validating EEG source imaging using intracranial electrical stimulation.
Article in Brain communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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
14 citing papers in PubMed.
- Assessing Strengths and Limitations of Magnetoencephalography Source Imaging With Intracerebral EEG.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A multi-frequency whole-brain neural mass model with homeostatic feedback inhibition.PLoS computational biology · 2026Article
- A geometry aware framework enhances noninvasive mapping of whole human brain dynamics.Nature biomedical engineering · 2026Article
- Differential neural responses to rhythmic and patterned TMS protocols: Insights from EEG spectral analysis.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Article
- Inferring neural sources from electroencephalography: foundations and frontiers.Journal of neural engineering · 2026Review
- 3D printed pediatric head phantom for assessing deep epileptic sources localization.Computers in biology and medicine · 2026Article
- A practical preprocessing pipeline for concurrent TMS-iEEG: Critical steps and methodological considerations.NeuroImage · 2026Article
- Multimodal Imaging in Epilepsy Surgery for Personalized Neurosurgical Planning.Journal of personalized medicine · 2025Review
- Depth-to-scalp spatiotemporal dynamics for stereo-EEG.Epilepsy & behavior reports · 2025Article
- A Practical Preprocessing Pipeline for Concurrent TMS-iEEG: Critical Steps and Methodological Considerations.bioRxiv : the preprint server for biology · 2025Article
- Validating MEG estimated resting-state connectome with intracranial EEG.Network neuroscience (Cambridge, Mass.) · 2025Article
- EEG/MEG source imaging of deep brain activity within the maximum entropy on the mean framework: Simulations and validation in epilepsy.Human brain mapping · 2024Article
- How to assess the accuracy of volume conduction models? A validation study with stereotactic EEG data.Frontiers in human neuroscience · 2024Article
- From surface to depth: Using deep learning to predict striatal fMRI reward signaling from EEG.Imaging neuroscience (Cambridge, Mass.)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
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Electrical source imaging is used in presurgical epilepsy evaluation and in cognitive neurosciences to localize neuronal sources of brain potentials recorded on EEG. This study evaluates the spatial accuracy of electrical source imaging for known sources, using electrical stimulation potentials recorded on simultaneous stereo-EEG and 37-electrode scalp EEG, and identifies factors determining the localization error. In 11 patients undergoing simultaneous stereo-EEG and 37-electrode scalp EEG recordings, sequential series of 99-110 biphasic pulses (2 ms pulse width) were applied by bipolar electrical stimulation on adjacent contacts of implanted stereo-EEG electrodes. The scalp EEG correlates of stimulation potentials were recorded with a sampling rate of 30 kHz. Electrical source imaging of averaged stimulation potentials was calculated utilizing a dipole source model of peak stimulation potentials based on individual four-compartment finite element method head models with various skull conductivities (range from 0.0413 to 0.001 S/m). Fitted dipoles with a goodness of fit of ≥80% were included in the analysis. The localization error was calculated using the Euclidean distance between the estimated dipoles and the centre point of adjacent stimulating contacts. A total of 3619 stimulation locations, respectively, dipole localizations, were included in the evaluation. Mean localization errors ranged from 10.3 to 26 mm, depending on source depth and selected skull conductivity. The mean localization error increased with an increase in source depth (
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.