ICA on 275ch MEG... removing artifacts

Stefan Debener s.debener at UKE.UNI-HAMBURG.DE
Fri Mar 27 21:54:28 CET 2009

Hi Suresh,

>   Typically how many components do people normally estimate and then how
> many of these would normally get rejected as containing eye artefact? 270
> components is alot to look through! I see one can limit the number of
> components the function can return....
Be careful with assuming a fixed number of components to explain eye
blinks. In EEG, it largely (but not solely!) depends on the number of
channels/components; so, while most 32 channel decompositions return 1
eye blink component, this cannot always be expected, and in 128 channel
data it can be anything between, roughly, 1 and 6. The problem with ICA
of course is that we have no clue about the number of sources
contributing to the data, so over/underfitting seems the rule rather
than the exception. Therefore (and for other reasons), one should
evaluate the quality of the decomposition, which usually includes
evaluating the time course information as well as the reliability of a

With regard to spatial stationarity, as mentioned by Michael already, it
seems important to recognize that many artefacts violate this assumption
to some more or less relevant extent. Eye blinks, for instance, seem to
be caused primarily by the movement of the eye lids (not so much of the
eye balls; there is a wonderful paper published on this in Clin
Neurophysiol, but I keep forgetting the reference, sorry). So, it is a
moving source which often can be 'perfectly' modelled with ICA. In my
experience, the same holds for lateral eye movements, which also can be
nicely modelled with ICA: these components show a very robust
topography, that is, you need not much data to find such ICs quite
quickly and reliably, and they show up in nearly all datasets. Thus, ICA
seems to tolerate to some extent stationarity violations. My speculation
is that this is related to the relatively poor spatial sampling of these
artefacts in conventional EEG/MEG recordings: imagine decomposing from
100 channels that covered the whole face: I would not be surprised if
those recordings would be more sensitive to stationarity violations
contributed by these artefacts.

A case where the violation of stationarity typically messes up ICA is
for inside scanner EEG recordings. Here, it is my experience that ICA
cannot deal well with the ballistocardiogram, which contributes a very
complex, dynamically moving signal to the EEG (but see plenty other
published papers proposing just the opposite). The reason for this
inconsistency seems that the ballistocardiogram scales with the scanner
B0 field: in the case of 1.5T, ICA sometimes still pulls out a more or
less reasonable decomposition, but at 3T, or even 7T, it performs
poorly, under otherwise identical conditions (Debener et al., 2008, Int
J Psychophysiol).
>  Do people normally reject solely on topography or do they do a frequency
> analysis of the component time-course or perhaps other things?
Depends. As it happens, there is a paper in press (Viola et al., Clin
Neurophysiology, will be available online in a few days) evaluating the
use of topographical information for eye blink and eye movement
component identification and clustering (across datasets). This works
surprisingly well, from 30 to 128 channel EEG recordings. Temporal
correlations of component activations with aux channels have also been
used for component identification (e.g., Srivastava et al., 2005,
Neurimage) but I have mixed experience with this approach and would not
recommend it. However, it really depends on the type of artefact you are
after: there are cases where topographical information is not of much
help, while temporal information gives a detailed picture of which
component reflects artefact and which does not (e.g., Sandmann et al.,in
press in Brain; or Ohla et al., in press in Brain Topography; both
available online).

> Moreoever, I was also wondering if anyone had carried out any kind of
> systematic comparison between this approach for MEG compared to
> traditional
> EEG approaches to this problem (e.g. projecting out the EOG channels)?
Interesting you mention this - that's one of the issues we are very much
interested in.

Hope this helps,


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