Evaluation of Electroencephalography Source Localization Algorithms with Multiple Cortical Sources

<div><p>Background</p><p>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.</p><p>Methods</p><p>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.</p><p>Results</p><p>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. 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.</p></div

Area under the Precision vs. Recall (PR) curve for each of the algorithms tested vs. the number of sources in the simulation.

<p>The area under the PR curve serves as a summary of the overall performance of the algorithm. Note that while sLORETA is clearly the best algorithm when there is only one source present, its performance drops off as the number of sources increases. The performance of all the other algorithms appears steady for 1–4 sources, as Precision increases slightly for these algorithms while Recall drops.</p

Precision vs. Recall curves for two sources with varying strength ratios: (a) two sources with the same strength (b) two sources, one with twice the strength as the other (c) two sources, one with three times the strength as the other (d) two sources, one with four times the strength as the other.

<p>Note that while sLORETA has highest peak Precision for all strength ratios, Precision also drops off more steeply with increasing Recall for this algorithm. This pattern is emphasized as the strength ratio increases. All other algorithms have similar performance to each other, with slightly decreasing Precision and Recall as the strength ratio of the sources increases.</p

Area under the Precision vs. Recall curve for each of the algorithms tested vs. strength ratio.

<p>As a summary of the performance of the algorithm, the area under the curve decrease slightly for all algorithms as the strength ratio of the sources increases.</p

Schematic example of Precision and Recall calculation.

<p>Real source locations are represented by “X” marks. Red shading represents a hypothetical source reconstruction. (a) If an algorithm designates a large area as active, it is likely to find all sources, but the Precision of the reconstruction will be low. (b) If an algorithm is more conservative, Precision is high, but Recall is low as not all sources are found.</p