[Eeglablist] Transferring ICA weights from pruned data to original (moderately cleaned) dataset results in non-smooth activity power spectrum

[Roi, la, A.]

Dear list,

When preprocessing my EEG data, I run ICA on a heavily cleaned dataset.
Subsequently, I transfer the ICA weights to the original dataset, which is
only moderately cleaned. The number of channels in the two datasets are the
same and both are re-referenced to average reference (therefore, I use the
'pca', [number of channels in dataset - 1] settings when running ica).

Component 1 and 6 probably reflects blinks and horizontal eye movements,
respectively. When I plot the components in the pruned dataset, the
activity power spectrum looks fine (see
attachment: ptc5_component1_pruneddata.png
and ptc5_component6_pruneddata.png). However, when I plot the components in
the original dataset, the activity power spectrum of both components
contains bumps, while the scalp plot remain the same (see
attachment: ptc5_component1_originaldata.png
and ptc5_component6_originaldata.png). Does anyone know what's going wrong
here? The component activations when plotted in the original dataset still
indicate that component 1 and 6 reflect blinks and horizontal eye
movements, respectively, although the component activation plot does
contain a bit more noise than the component activation plot of the pruned
data.

Any comments are welcome!

Kind regards,
Amélie la Roi

-- 
Amélie la Roi, MA
PhD student CLCG, Semantics and Cognition
University of Groningen, the Netherlands
Harmony building, room 1311.0412 | Phone: +31 50 363 6683
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