[Eeglablist] best practices for eyes-closed eog correction?

[Scott Makeig]

Although I have not seen this, ICA decomposition of data with rolling eye
movements
should resolve their artifactual contributions to the scalp data into an IC
subspace of at least (and perhaps) two ICs,
likely with more or less orthogonal scalp maps. The post-AMICA tools
plug-in contains a pairwise mutual information (PMI)
application that finds these IC subspaces and produces an informative plot
showing them.

I recommend consulting this plot in any study using ICA decomposition. An
IC subspace having 1 dimension is ...
   a (true) IC (!).
A subspace with 2 dimensions means ICA has separated their joint activity
into a subspace
for which no truly independent component basis exists - but their joint
activity is thereby removed
from all other IC (subspaces).

Scott

p.s. This function should work even if the ICA decomposition method used
was not AMICA - which is still
to our knowledge (and testing) the most effective ICA method applied to EEG
data.

On Sun, Feb 5, 2023 at 8:58 AM Jack Fogarty (Dr) via eeglablist <
eeglablist at sccn.ucsd.edu> wrote:

> Hi everyone,
>
> I am looking for guidance on using ICA to correct eyes-closed resting eog
> artefact (e.g., eye-roll movement). I am finding it difficult to identify
> meaningful eog components from eyes-closed data using the typical eeglab
> procedures. Are there any specific recommendations or guidelines for this?
> Alternatively, would there be another method to remove eyes-closed EOG
> artefact that others would suggest, particularly for continuous (i.e.,
> prior to epoching) datasets without EOG channels?
>
> From my experience so far, I generally find that ICA does not identify any
> obvious eog components from eyes-closed data given there are minimal (if
> any) blinks or open-eye movement, and perhaps closed 'eye rolling'
> movements do not carry much variance. Any help here would be appreciated.
>
> Thanks,
> Jack Fogarty, PhD.
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-- 
Scott Makeig, Research Scientist and Director, Swartz Center for
Computational Neuroscience, Institute for Neural Computation, University of
California San Diego, La Jolla CA 92093-0559, http://sccn.ucsd.edu/~scott