The effects of EEG data transformations on the solution accuracy of principal component analysis

Principal component analysis (PCA) is a commonly used multivariate procedure that reduces the dimensionality of a data set. When applied to quantitative electroencephalogram (qEEG) data, PCA produces components that may represent functional systems within the brain. Unfortunately, qEEG, like many ot...

Full description

Saved in:
Bibliographic Details
Published in:Psychophysiology 2011-03, Vol.48 (3), p.370-376
Main Authors: Arruda, James E., McGee, Heather A., Zhang, Hongmei, Stanny, Claudia J.
Format: Article
Language:English
Subjects:
Absolute power
Adolescent
Adult
Analysis of Variance
Brain
Computer Simulation
Data Interpretation, Statistical
EEG
Electroencephalogram
Electroencephalography
Electroencephalography - statistics & numerical data
Factor analysis
Female
Humans
Log
Male
Neuropsychology
PCA
Physiological psychology
Principal Component Analysis
Principal components analysis
Relative power
Reproducibility of Results
Transformation
Young Adult
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Principal component analysis (PCA) is a commonly used multivariate procedure that reduces the dimensionality of a data set. When applied to quantitative electroencephalogram (qEEG) data, PCA produces components that may represent functional systems within the brain. Unfortunately, qEEG, like many other physiological measures, produce distributions that are positively skewed. In response, researchers often transform qEEG data prior to conducting a PCA, which does not require univariate or multivariate normality. Despite this, researchers continue to transform qEEG data with limited knowledge of how such transformations will affect the accuracy (precision) of their component solutions. The purpose of the present investigation was to examine the effects of several commonly used data transformation procedures on PCA solution accuracy.
ISSN:0048-5772
1469-8986
1540-5958
DOI:10.1111/j.1469-8986.2010.01067.x