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Independent Component Analysis (ICA) is a widely applied data-driven method for parsing brain and non-brain EEG source signals, mixed by volume conduction to the scalp electrodes, into a set of maximally temporally and often functionally independent components (ICs). Many ICs may be identified with a precise physiological or non-physiological origin. However, this process is hindered by partial instability in ICA results that can arise from noise in the data. Here we propose RELICA (RELiable ICA), a novel method to characterize IC reliability within subjects. RELICA first computes IC "dipolarity" a measure of physiological plausibility, plus a measure of IC consistency across multiple decompositions of bootstrap versions of the input data. RELICA then uses these two measures to visualize and cluster the separated ICs, providing a within-subject measure of IC reliability that does not involve checking for its occurrence across subjects. We demonstrate the use of RELICA on EEG data recorded from 14 subjects performing a working memory experiment and show that many brain and ocular artifact ICs are correctly classified as "stable" (highly repeatable across decompositions of bootstrapped versions of the input data). Many stable ICs appear to originate in the brain, while other stable ICs account for identifiable non-brain processes such as line noise. RELICA might be used with any linear blind source separation algorithm to reduce the risk of basing conclusions on unstable or physiologically un-interpretable component processes. (C) 2014 Elsevier Inc. All rights reserved.