Evaluation of Electroencephalography Source Localization Algorithms with Multiple Cortical Sources

Abstract

BackgroundSource 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.MethodsEEG 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.ResultsWhile 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. If only the strongest source is of interest sLORETA is recommended, while LORETA with p equal to 1.5 is recommended if two or more of the cortical sources are of interest. These results provide guidance for choosing a CSD algorithm to locate multiple cortical sources of EEG and for interpreting the results of these algorithms.

Cortical source imaging of resting-state MEG with a high resolution atlas: An evaluation of methods

“…Another crucial methodological decision was choice of methods used to compare different algorithms. Previous studies have compared algorithms for source localization -identifying the origin of a small number of sources (Bai et al, 2007;Hassan et al, 2014;Bradley et al, 2016;Finger et al, 2016;Barzegaran and Knyazeva, 2017;Hassan et al, 2017;Hincapié et al, 2017;Bonaiuto et al, 2018;Pascual-Marqui et al, 2018;Seeland et al, 2018;Anzolin et al, 2019;Halder et al, 2019), such as known networks during task or simulated dipoles. These methods are not directly generalizable to resting-state data, where activity is not a point source but is distributed widely across the cortex.…”

“…We are not attempting to quantify the difference in peak activation of the estimate given a single 'true' active dipole as in simulation or theoretical studies (Pascual-Marqui, 2007;Barzegaran and Knyazeva, 2017;Pascual-Marqui et al, 2018;Halder et al, 2019), because of the expected cross talk from distributed sources in resting-state data. As a result, 'true' activity at a certain location does not necessarily imply a peak of estimated activity at that location -for example, localization statistics based on peak activation for sLORETA are accurate for single dipole localization, but are imperfect for localizing multiple dipoles (Bradley et al, 2016). The LE statistic used in this study is therefore more properly interpreted as follows: given true activity at a dipole, that activity will most strongly influence dipoles approximately LE mm from the true source on average, regardless of activity at other locations.…”

“…Much effort has been made to assess the quality of source reconstruction algorithms in the literature, which mainly focuses on source localization, i.e. identifying the origin of a small number of sources, for example evoked potentials with ground truth based on known task-relevant activity (Bai et al, 2007;Hassan et al, 2014;Seeland et al, 2018;Halder et al, 2019), or simulated activity at a small number of dipoles (Bradley et al, 2016;Finger et al, 2016;Barzegaran and Knyazeva, 2017;Hassan et al, 2017;Hincapié et al, 2017;Bonaiuto et al, 2018;Pascual-Marqui et al, 2018;Anzolin et al, 2019;Halder et al, 2019).…”

Nonlinear Coupling between Cortical Oscillations and Muscle Activity during Isotonic Wrist Flexion

“…We used Talairach Client toolbox 1 to identify the nearby cortex for each dipole. Afterwards, we visually examined all components to further reject the components with the dipoles located in the deep brain areas, since previous studies indicated the difficulty of localizing a deep source from the scalp EEG (Yao and Dewald, 2005; Bradley et al, 2016). …”

Intervention-Induced Changes in Cortical Connectivity and Activity in Severe Chronic Hemiparetic Stroke

“…EEG is inherently limited in its spatial resolution, particularly in comparison to fMRI. However, using our high-density EEG setup, along with the sLORETA inverse calculation based on subjectspecific boundary element models created with individual's anatomical MRI, we have demonstrated a resolution of roughly 5 mm 79,80 . This is suitable for distinguishing primary sensorimotor cortex and secondary motor areas as investigated here in our topographical analysis and allowed us to investigate multi-joint movements which would have been impractical inside an MRI scanner.…”