A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is
We present a new discriminant analysis (DA) method called Multiple Subject Barycentric Discriminant Analysis (MUSUBADA) suited for analyzing fMRI data because it handles datasets with multiple participants ... Like DA, MUSUBADA (1) assigns observations to predefined categories, (2) gives factorial maps displaying observations and categories, and (3) optimally assigns observations to categories. ... Conclusion Multiple subject barycentric discriminant analysis is particularly well suited for the analysis of neuroimaging data because it does not require brains to be spatially normalized. ...doi:10.1155/2012/634165 pmid:22548125 pmcid:PMC3328164 fatcat:vmtqh4iztzbuhmq2gpczkykdlm
For personal use only. ... A major challenge for systems neuroscience is to break the neural code. ... Multiple Subject Barycentric Discriminant Analysis (MUSUBADA): how to assign scans to categories without using spatial normalization. Comp. Math. ...doi:10.1146/annurev-neuro-062012-170325 pmid:25002277 fatcat:ah6sfup2mrct7bkg2kel37z3w4