Partial Least Squares Analysis in Electrical Brain Activity

10.6339/JDS.2009.07(1).434

Aylin Alın；Serdar Kurt；Anthony Randal McIntosh；Adile Öniz；Murat Özgören

Collinearity ； EEG ； partial least squares ； singular value decomposition

Journal of Data Science

7卷1期（2009 / 01 / 01）

99 - 110

英文

Partial least squares (PLS) method has been designed for handling two common problems in the data that are encountered in most of the applied sciences including the neuroimaging data: 1) Collinearity problem among explanatory variables (X) or among dependent variables (Y); 2) Small number of observations with large number of explanatory variables. The idea behind this method is to explain as much as possible covariance between two blocks of X and Y variables by a small number of uncorrelated variables. Apart from the other applied sciences in which PLS are used, in the application of imaging data PLS has been used to identify task dependent changes in activity, changes in the relations between brain and behavior, and to examine functional connectivity of one or more brain regions. The aim of this paper is to give some information about PLS and apply on electroencephalography (EEG) data to identify stimulation dependent changes in EEG activity.