000254982 001__ 254982
000254982 005__ 20190507143840.0
000254982 0247_ $$2doi$$a10.1016/j.neuroimage.2018.03.016
000254982 02470 $$a10.1016/j.neuroimage.2018.03.016$$2DOI
000254982 037__ $$aARTICLE
000254982 245__ $$aApplying dimension reduction to EEG data by Principal Component Analysis reduces the quality of its subsequent Independent Component decomposition
000254982 260__ $$c2018-04-10
000254982 269__ $$a2018-04-10
000254982 336__ $$aJournal Articles
000254982 520__ $$aIndependent 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 ‘dipolar’ 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.
000254982 536__ $$aH2020$$cBIREHAB
000254982 700__ $$g268982$$0249777$$aArtoni, Fiorenzo
000254982 700__ $$aDelorme, Arnaud
000254982 700__ $$aMakeig, Scott
000254982 773__ $$q176-187$$j175$$tNeuroImage
000254982 8560_ $$ffiorenzo.artoni@epfl.ch
000254982 8564_ $$uhttps://infoscience.epfl.ch/record/254982/files/v4_Revised_Article_Submission.pdf$$s4323445
000254982 8564_ $$uhttps://infoscience.epfl.ch/record/254982/files/v4_Revised_Article_Submission.pdf?subformat=pdfa$$s3154557$$xpdfa
000254982 909C0 $$xU12522$$msilvestro.micera@epfl.ch$$pTNE$$0252419
000254982 909CO $$qGLOBAL_SET$$pSTI$$particle$$ooai:infoscience.epfl.ch:254982
000254982 960__ $$afiorenzo.artoni@epfl.ch
000254982 961__ $$alaurence.gauvin@epfl.ch
000254982 973__ $$aEPFL$$sPUBLISHED$$rREVIEWED
000254982 980__ $$aARTICLE
000254982 981__ $$aoverwrite