Literature DB >> 33038537

Spatial fidelity of MEG/EEG source estimates: A general evaluation approach.

John G Samuelsson1, Noam Peled2, Fahimeh Mamashli2, Jyrki Ahveninen2, Matti S Hämäläinen2.   

Abstract

Low spatial resolution is often cited as the most critical limitation of magneto- and electroencephalography (MEG and EEG), but a unifying framework for quantifying the spatial fidelity of M/EEG source estimates has yet to be established; previous studies have focused on linear estimation methods under ideal scenarios without noise. Here we present an approach that quantifies the spatial fidelity of M/EEG estimates from simulated patch activations over the entire neocortex superposed on measured resting-state data. This approach grants more generalizability in the evaluation process that allows for, e.g., comparing linear and non-linear estimates in the whole brain for different signal-to-noise ratios (SNR), number of active sources and activation waveforms. Using this framework, we evaluated the MNE, dSPM, sLORETA, eLORETA, and MxNE methods and found that the spatial fidelity varies significantly with SNR, following a largely sigmoidal curve whose shape varies depending on which aspect of spatial fidelity that is being quantified and the source estimation method. We believe that these methods and results will be useful when interpreting M/EEG source estimates as well as in methods development.
Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  EEG; MEG; Patch analysis; SNR; Source estimation; Spatial resolution

Mesh:

Year:  2020        PMID: 33038537      PMCID: PMC7793168          DOI: 10.1016/j.neuroimage.2020.117430

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  40 in total

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4.  Mapping the signal-to-noise-ratios of cortical sources in magnetoencephalography and electroencephalography.

Authors:  Daniel M Goldenholz; Seppo P Ahlfors; Matti S Hämäläinen; Dahlia Sharon; Mamiko Ishitobi; Lucia M Vaina; Steven M Stufflebeam
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5.  Signal-space projection method for separating MEG or EEG into components.

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6.  EEG and MEG: sensitivity to epileptic spike activity as function of source orientation and depth.

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7.  Time-frequency mixed-norm estimates: sparse M/EEG imaging with non-stationary source activations.

Authors:  A Gramfort; D Strohmeier; J Haueisen; M S Hämäläinen; M Kowalski
Journal:  Neuroimage       Date:  2013-01-04       Impact factor: 6.556

8.  Cortical Signal Suppression (CSS) for Detection of Subcortical Activity Using MEG and EEG.

Authors:  John G Samuelsson; Sheraz Khan; Padmavathi Sundaram; Noam Peled; Matti S Hämäläinen
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Journal:  PLoS One       Date:  2013-03-20       Impact factor: 3.240

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5.  Contextual MEG and EEG Source Estimates Using Spatiotemporal LSTM Networks.

Authors:  Christoph Dinh; John G Samuelsson; Alexander Hunold; Matti S Hämäläinen; Sheraz Khan
Journal:  Front Neurosci       Date:  2021-03-09       Impact factor: 4.677

6.  Spatial accuracy evaluation of magnetic source imaging methods on OPM-based MEG.

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Review 7.  Presurgical Evaluation of Epilepsy Using Resting-State MEG Functional Connectivity.

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8.  Imaging somatosensory cortex responses measured by OPM-MEG: Variational free energy-based spatial smoothing estimation approach.

Authors:  Nan An; Fuzhi Cao; Wen Li; Wenli Wang; Weinan Xu; Chunhui Wang; Min Xiang; Yang Gao; Binbin Sui; Aimin Liang; Xiaolin Ning
Journal:  iScience       Date:  2022-01-07
  8 in total

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