[Eeglablist] Independent EEG Sources Are Dipolar

[Scott Makeig]

Some SCCN and EEGLAB news:

- SCCN recently celebrated our 10th anniversary with an impromptu cake and
talk at tea time <http://sccn.ucsd.edu/events/>. Arno put his thoughts
about the 10th anniversary of EEGLAB itself in the EEGLAB
wiki<http://sccn.ucsd.edu/wiki/The_first_decade_of_EEGLAB>last fall.

- Our paper on the 'dipolarity' of independent EEG component processes is
finally out and freely available in PLoS
ONE<http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0030135>.
This paper shows, roughly put, that the more mutual information a linear
decomposition 'squeezes out of' the single channel time courses in EEG
multi-channel data (upon transforming the data from channels to
components), the more of those components it returns have a 'dipolar' scalp
map, i.e. one near-precisely matching the projection of a single current
dipole, most likely the 'equivalent dipole' of the cortical patch source of
the component process ...

This result supports the strategy implemented in EEGLAB to first decompose
EEG data using ICA (in particular, Infomax ICA or Amica), then to estimate
the location of the cortical generator area by fitting an equivalent dipole
to the component scalp map. Most spatial EEG measures, for example the
scalp map at the peak of a cognitive ERP, cannot be well fit with a single
dipole. This fact leads many who know it to question why single-dipole
models are valid for ICA. The new paper indicates that this model is quite
often valid -- though 'ground-truth' testing and confirmation of this claim
will still require sufficient, model-based analysis of ultra-high density
cortical field recordings....

- We are now preparing the renewal proposal for the EEGLAB project funding
from the US National Institutes of Health (NIH). We will soon be posting a
request for letters of support from users both in the US and abroad. Such
user support will be appreciated by us and by the funding sponsors...

Scott Makeig
*

Citation: *Delorme A, Palmer J, Onton J, Oostenveld R, Makeig S (2012)
*Independent
EEG Sources Are Dipolar*. PLoS ONE 7(2): e30135.
doi:10.1371/journal.pone.0030135Abstract
Top<http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0030135#top>

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).

-- 
Scott Makeig, Research Scientist and Director, Swartz Center for
Computational Neuroscience, Institute for Neural Computation; Prof. of
Neurosciences (Adj.), University of California San Diego, La Jolla CA
92093-0559, http://sccn.ucsd.edu/~scott
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