A visual working memory dataset collection with bootstrap Independent Component Analysis for comparison of electroencephalogram preprocessing pipelines

Published: 4 December 2018| Version 1 | DOI: 10.17632/gvt64rndnd.1
, Arnaud Delorme, Scott Makeig


Here we present an electroencephalographic (EEG) collection of 71-channel datasets recorded from 14 subjects (7 males, 7 females, aged 20-40 years) while performing a visual working memory task. These data were also used in (Delorme, Palmer, Onton, Oostenveld, Makeig. “Independent EEG sources are dipolar.” PLoS One, 2012). Here, each dataset includes a set of 150 Independent Component Analysis (ICA) decompositions by Extended Infomax using RELICA, each on a bootstrap resampling of the data. Independent components (ICs) are clustered within subject and thereby associated with a quality index (QIc) measure of their stability to data resampling. Sets of single ICA decompositions obtained after applying Principal Component Analysis (PCA) to the data to perform dimension reduction retaining (85%, 95%, 99%) of data variance are also included, as are the positions of the best fitting equivalent dipoles for ICs whose scalp projections are compatible with a compact brain source. These bootstrap ICs may be used as benchmarks for different data preprocessing pipelines and/or ICA algorithms, allowing investigation of the effects that noise or insufficient data have on the quality of ICA decompositions.



Independent Component Analysis, Principal Component Analysis, Electroencephalography, Working Memory, Inverse Problem for EEG Source Localization