title: Evaluation of Electroencephalography Source Localization Algorithms with Multiple Cortical Sources.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
authors.affiliation: Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States of America.|Department of Otolaryngology, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States of America.
authors.forename: Allison
authors.initials: A
authors.lastname: Bradley
authors.affiliation: Department of Physical Therapy and Human Movement Science, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States of America.
authors.forename: Jun
authors.initials: J
authors.lastname: Yao
authors.affiliation: Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States of America.|Department of Physical Therapy and Human Movement Science, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States of America.|Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States of America.
authors.forename: Jules
authors.initials: J
authors.lastname: Dewald
authors.affiliation: Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States of America.|Department of Otolaryngology, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States of America.|Hugh Knowles Center, Department of Communication Sciences and Disorders, Northwestern University, Evanston, IL, United States of America.
authors.forename: Claus-Peter
authors.initials: CP
authors.lastname: Richter
mesh_terms: D000465:Algorithms; D001931:Brain Mapping; D004569:Electroencephalography / Q000379:methods*; D006801:Humans
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