Independent EEG Sources Are Dipolar

TLDR

ICA and BSS methods are increasingly used to separate individual brain and non‑brain source signals mixed by volume conduction in EEG and other electrophysiological recordings. We compared thirteen 71‑channel scalp EEG datasets using 22 ICA and BSS algorithms, evaluating mutual information reduction, remaining mutual information, and dipolarity defined by the number of component scalp maps fitting a single equivalent dipole with low residual variance. Principal component analysis performed worst, while AMICA and other likelihood/mutual‑information based ICA methods performed best; across 18 algorithms mean dipolarity correlated linearly with mutual information reduction and remaining mutual information, suggesting many independent EEG components are volume‑conducted projections of partially synchronous local cortical activity, and the data and software are publicly available at http://sccn.ucsd.edu/wiki/BSSComparison.

Abstract

Independent component analysis (ICA) and blind source separation (BSS) methods are increasingly used to separate individual brain and non-brain source signals mixed by volume conduction in electroencephalographic (EEG) and other electrophysiological recordings. We compared results of decomposing thirteen 71-channel human scalp EEG datasets by 22 ICA and BSS algorithms, assessing the pairwise mutual information (PMI) in scalp channel pairs, the remaining PMI in component pairs, the overall mutual information reduction (MIR) effected by each decomposition, and decomposition 'dipolarity' defined as the number of component scalp maps matching the projection of a single equivalent dipole with less than a given residual variance. The least well-performing algorithm was principal component analysis (PCA); best performing were AMICA and other likelihood/mutual information based ICA methods. Though these and other commonly-used decomposition methods returned many similar components, across 18 ICA/BSS algorithms mean dipolarity varied linearly with both MIR and with PMI remaining between the resulting component time courses, a result compatible with an interpretation of many maximally independent EEG components as being volume-conducted projections of partially-synchronous local cortical field activity within single compact cortical domains. To encourage further method comparisons, the data and software used to prepare the results have been made available (http://sccn.ucsd.edu/wiki/BSSComparison).