Abstract：
:In brain-imaging research, we are often interested in making quantitative claims about effects across subjects. Given that most imaging data consist of tens to thousands of spatially correlated time series, inter-subject comparisons are typically accomplished with simple combinations of inter-subject data, for example methods relying on group means. Further, these data are frequently taken from reduced channel subsets defined either a priori using anatomical considerations, or functionally using p-value thresholding to choose cluster boundaries. While such methods are effective for data reduction, means are sensitive to outliers, and current methods for subset selection can be somewhat arbitrary. Here, we introduce a novel "partial-ranking" approach to test for inter-subject agreement at the channel level. This non-parametric method effectively tests whether channel concordance is present across subjects, how many channels are necessary for maximum concordance, and which channels are responsible for this agreement. We validate the method on two previously published and two simulated EEG data sets.
journal_name
Neuroimagejournal_title
NeuroImageauthors
Wong DK,Grosenick L,Uy ET,Perreau Guimaraes M,Carvalhaes CG,Desain P,Suppes Pdoi
10.1016/j.neuroimage.2007.07.064subject
Has Abstractpub_date
2008-02-01 00:00:00pages
1051-63issue
3eissn
1053-8119issn
1095-9572pii
S1053-8119(07)00689-1journal_volume
39pub_type
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