Open AccessExamining the Effects of One- and Three-Dimensional Spatial Filtering Analyses in Magnetoencephalography
Author(s) -
Samuel Johnson,
Garreth Prendergast,
Mark Hymers,
Gary Green
Publication year - 2011
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0022251
Subject(s) - beamforming , magnetoencephalography , computer science , spatial filter , filter (signal processing) , ambiguity , series (stratigraphy) , algorithm , orientation (vector space) , field (mathematics) , data mining , pattern recognition (psychology) , artificial intelligence , computer vision , mathematics , telecommunications , psychology , paleontology , geometry , electroencephalography , psychiatry , pure mathematics , biology , programming language
Spatial filtering, or beamforming, is a commonly used data-driven analysis technique in the field of Magnetoencephalography (MEG). Although routinely referred to as a single technique, beamforming in fact encompasses several different methods, both with regard to defining the spatial filters used to reconstruct source-space time series and in terms of the analysis of these time series. This paper evaluates two alternative methods of spatial filter construction and application. It demonstrates how encoding different requirements into the design of these filters has an effect on the results obtained. The analyses presented demonstrate the potential value of implementations which examine the timeseries projections in multiple orientations at a single location by showing that beamforming can reconstruct predominantly radial sources in the case of a multiple-spheres forward model. The accuracy of source reconstruction appears to be more related to depth than source orientation. Furthermore, it is shown that using three 1-dimensional spatial filters can result in inaccurate source-space time series reconstruction. The paper concludes with brief recommendations regarding reporting beamforming methodologies in order to help remove ambiguity about the specifics of the techniques which have been used.