254982
20190507143840.0
doi
10.1016/j.neuroimage.2018.03.016
10.1016/j.neuroimage.2018.03.016
DOI
ARTICLE
Applying dimension reduction to EEG data by Principal Component Analysis reduces the quality of its subsequent Independent Component decomposition
2018-04-10
2018-04-10
Journal Articles
Independent Component Analysis (ICA) has proven to be an effective data driven method for analyzing EEG data, separating signals from temporally and functionally independent brain and non-brain source processes and thereby increasing their definition. Dimension reduction by Principal Component Analysis (PCA) has often been recommended before ICA decomposition of EEG data, both to minimize the amount of required data and computation time. Here we compared ICA decompositions of fourteen 72-channel single subject EEG data sets obtained (i) after applying preliminary dimension reduction by PCA, (ii) after applying no such dimension reduction, or else (iii) applying PCA only. Reducing the data rank by PCA (even to remove only 1% of data variance) adversely affected both the numbers of dipolar independent components (ICs) and their stability under repeated decomposition. For example, decomposing a principal subspace retaining 95% of original data variance reduced the mean number of recovered â€˜dipolarâ€™ ICs from 30 to 10 per data set and reduced median IC stability from 90% to 76%. PCA rank reduction also decreased the numbers of near-equivalent ICs across subjects. For instance, decomposing a principal subspace retaining 95% of data variance reduced the number of subjects represented in an IC cluster accounting for frontal midline theta activity from 11 to 5. PCA rank reduction also increased uncertainty in the equivalent dipole positions and spectra of the IC brain effective sources. These results suggest that when applying ICA decomposition to EEG data, PCA rank reduction should best be avoided.
H2020
BIREHAB
Artoni, Fiorenzo
249777
268982
Delorme, Arnaud
Makeig, Scott
NeuroImage
175
176-187
fiorenzo.artoni@epfl.ch
4323445
http://infoscience.epfl.ch/record/254982/files/v4_Revised_Article_Submission.pdf
pdfa
3154557
http://infoscience.epfl.ch/record/254982/files/v4_Revised_Article_Submission.pdf?subformat=pdfa
252419
TNE
silvestro.micera@epfl.ch
U12522
oai:infoscience.epfl.ch:254982
article
STI
GLOBAL_SET
fiorenzo.artoni@epfl.ch
laurence.gauvin@epfl.ch
EPFL
PUBLISHED
REVIEWED
ARTICLE
overwrite