Impact of reduced-dimensionality independent components analysis on event-related potential measurements

Victor J. Pokorny, Scott R. Sponheim, Eric Rawls

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Independent components analysis (ICA) is an effective and ubiquitous tool for cleaning EEG. To reduce computation time, many analysis pipelines decrease EEG dimensionality prior to ICA. A 2018 report by Artoni and colleagues detailed the deleterious effects of such reduced-dimensionality ICA (rdICA) on the dipolarity and reliability of independent components. Though valuable for researchers interested in directly analyzing independent components, ICA is more commonly used for cleaning EEG. Thus, a direct examination of the impact of artifact removal via rdICA on EEG data quality is needed. We conducted a registered analysis of 128 electrode recordings of 43 healthy subjects performing an active auditory oddball task. We preprocessed each subject's data under the following conditions: (1) ICA without dimension reduction, (2) ICA with only 64 electrodes included, (3) ICA preceded by PCA retaining 99% of the original data variance and (4) ICA preceded by PCA retaining 90% variance. We then quantified ERP data quality by measuring mean-amplitude, standardized measurement error (SME) of the single-trial mean-amplitudes, and split-half reliability of the N1 and P3 components. We then attempted to replicate our findings in an independent validation dataset. We observed statistically and practically significant changes in the mean amplitude of early sensory components for the 90% condition. Unexpectedly, the SME was only larger for the 64 electrode condition. Also unexpectedly, the effect of rdICA on split-half reliability was inconsistent between datasets. Based on the observed data, we argue that PCA-based rdICA is justifiable when used cautiously.

Original languageEnglish (US)
Article numbere14223
Issue number5
StatePublished - May 2023

Bibliographical note

Funding Information:
This work is supported by grants from the Veterans Health Administration (I01CX000227), National Institutes of Mental Health (R24MH069675, R03MH106831) to SRS. ER is supported by a CTSI‐TL1 postdoctoral fellowship. The University of Minnesota's CTSI is supported through the NIH Clinical and Translational Science Award (CTSA) program, grants TL1TR002493 and UL1TR002494. VJP received support from the National Science Foundation (Graduate Research Fellowship Program: #00074041).

Publisher Copyright:
© 2022 The Authors. Psychophysiology published by Wiley Periodicals LLC on behalf of Society for Psychophysiological Research. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.


  • EEG
  • ERP
  • ICA


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