Independent EEG Sources Are Dipolar

By Arnaud Delorme, Jason A. Palmer, Julie Anne Onton, Robert Oostenveld, Scott Makeig. PLoS ONE, 2012

806 citations recorded by the source corpus.

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).

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/0202d3e1f1476150c111b4190fab8f21ce2f2185

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Citations in the corpus

Analysing concurrent transcranial magnetic stimulation and electroencephalographic data: A review and introduction to the open‐source TESA software (2017, neutral): “There is still some debate as to which algorithm is most appropriate for EEG analysis (Delorme et al., 2012); however, this discussion is beyond the scope of the current paper.”
Level of participation in robotic-assisted treadmill walking modulates midline sensorimotor EEG rhythms in able-bodied subjects (2012, methodology): “A best-fitting single equivalent current dipole, was calculated for the scalp projection for each independent source using a standardized three-shell boundary element head model (BEM) implemented in the DIPFIT toolbox of EEGLAB (Delorme et al., 2012; Oostenveld and Oostendorp, 2002).”
ICLabel: An automated electroencephalographic independent component classifier, dataset, and website (2019, neutral): “The second version uses selected spatial features and, while originally intended for short recordings, appears to work better in practice, and is referred to below as the spatial feature set (IC MARCSF).”
On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP (2015, methodology): “As discussed in [6] in detail, this ’dipolarity’ of the ICA decomposition is a very informative, albeit simplistic measure of physiological plausibility.”
Evaluation of Artifact Subspace Reconstruction for Automatic EEG Artifact Removal (2018, background): “These ICs were characterized by spatially homogeneous scalp maps [13].”
Evaluation of Artifact Subspace Reconstruction for Automatic Artifact Components Removal in Multi-Channel EEG Recordings (2020, methodology): “[39] A. Delorme et al., “Independent EEG sources are Dipolar,” PLoS One, vol. 7, 2012, Art. no. e30135.”
Combining independent component analysis and source localization for improving spatial sampling of stereoelectroencephalography in epilepsy (2024, background): “As epileptic sources (corresponding each to a patch of activated cortex) are usually reasonably independent, each ICA topography is likely to correspond to a single source 26 .”
Associations between EEG power and coherence and cognitive and language development across the first months of life (2023, methodology): “Finally, an independent component analysis was conducted to separate the baseline EEG signal into independent components (ICs) originating from the brain source and unwanted artifacts (Delorme et al., 2012).”
Neural oscillations guiding action during effects imagery. (2024, methodology): “An independent component analysis using the AMICA algorithm was conducted using the EEGLAB plugin [43,44] as this algorithm can decompose EEG into independent components (ICs) resembling neural activity [43].”
Unified Coding of Spectral and Temporal Phonetic Cues: Electrophysiological Evidence for Abstract Phonological Features (2021, methodology): “ommendations for satisfactory decomposition (Delorme et al., 2012).”