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Updated: Jan 30, 2026

PIPEMAT-RS: Development and Validation of a Standardized MATLAB Pipeline for Resting-State EEG Preprocessing
Published on: June 6, 2025
A visual working memory dataset collection with bootstrap Independent Component Analysis for comparison of
Fiorenzo Artoni1,2, Arnaud Delorme3,4, Scott Makeig3
1The Biorobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy.
Abstract:
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 with a T set of 150 Independent Component Analysis (ICA) decompositions by Extended Infomax using RELICA, each on a bootstrap resampling of the data. These data are linked to the paper "Applying dimension reduction to EEG data by Principal Component Analysis reduces the quality of its subsequent Independent Component decomposition" [1]. 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.
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