Independent EEG Sources Are Dipolar

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).

EEG imaging of toddlers during dyadic turn-taking: Mu-rhythm modulation while producing or observing social actions

“…Also, because cortical sources project tangentially from both gyral and sulcal surfaces, it is difficult to infer a specific vector from scalp to cortex based on channel-level activity. A preferable approach is to utilize all information (frequency, amplitude, and phase) to mathematically separate maximally independent sources of coherent activity in an EEG record (Onton et al, 2006;Delorme et al, 2012). Nyström et al (2011) used this 'un-mixing' approach to increase signal-to-noise ratio in infant mu-suppression data, but the authors did not conduct source localization.…”

Spatio-temporal reconstruction of brain dynamics from EEG with a Markov prior

“…Evidence was presented, in recent work (Delorme et al, 2012) that the instantaneous independent components of EEG 25 signals are dipolar and localized. In particular it was shown that the residual variance after a dipole fit to the component scalp maps is less than 5% for large fractions of the independent components.…”

“…More short ranging connections exist in the brain than long ranging, arguing for spatially focal sources. Additionally, recent work (Delorme et al, 2012) argues that EEG can be decomposed into components having sparse source distributions. On the temporal side both short and long term stationarity of brain activation are seen.…”

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“…These include methods like the General Linear Model [8], linear or non-linear low-pass filtering [9], Independent Component Analysis (ICA) [7], [8], [12], parallel factor analysis (PARAFAC) [13], [14], Adaptive Mixture of Independent Component Analyzers (AMICA) [15] or blind source separation -canonical correlation analysis (BSS-CCA) [16].…”