Evaluation of Electroencephalography Source Localization Algorithms with Multiple Cortical Sources

PloS One
Allison BradleyClaus-Peter Richter

Abstract

Source localization algorithms often show multiple active cortical areas as the source of electroencephalography (EEG). Yet, there is little data quantifying the accuracy of these results. In this paper, the performance of current source density source localization algorithms for the detection of multiple cortical sources of EEG data has been characterized. EEG data were generated by simulating multiple cortical sources (2-4) with the same strength or two sources with relative strength ratios of 1:1 to 4:1, and adding noise. These data were used to reconstruct the cortical sources using current source density (CSD) algorithms: sLORETA, MNLS, and LORETA using a p-norm with p equal to 1, 1.5 and 2. Precision (percentage of the reconstructed activity corresponding to simulated activity) and Recall (percentage of the simulated sources reconstructed) of each of the CSD algorithms were calculated. While sLORETA has the best performance when only one source is present, when two or more sources are present LORETA with p equal to 1.5 performs better. When the relative strength of one of the sources is decreased, all algorithms have more difficulty reconstructing that source. However, LORETA 1.5 continues to outperform other algorithms. ...Continue Reading

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Citations

Jul 22, 2016·International Journal of Neural Systems·Yuan YangAlfred C Schouten
Dec 5, 2017·Frontiers in Neuroscience·Deirel Paz-LinaresPedro A Valdés-Sosa
Jan 29, 2018·Brain Topography·Eshwar G GhumarePatrick Dupont
Mar 27, 2019·Journal of Cognitive Neuroscience·John PlassDavid Brang
Mar 8, 2020·The Journal of Physiology·Kevin B WilkinsJulius P A Dewald
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Dec 22, 2020·Frontiers in Aging Neuroscience·Ehsan Darestani FarahaniAstrid van Wieringen

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Software Mentioned

Curry V5
MATLAB
MNLS
sLORETA
Curry
Compumedics Neuroscan
LORETA
LAURA

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