[Eeglablist] Splitting the data for ICA

[Scott Makeig]

Jan -

The number of ICs needed to capture /account for eye movements can be 2
(vert , horiz) - though in free-ranging visual (+movement) tasks it may be
larger. Activity with these (2) maps will be channeled into activities of
these 2 ICs when using the above-1Hz trained IC unmixing matrix to
decompose the full-range data. But yes, other sources of <1-Hz activity
(sweat-based?, movement-based?, ...) will spread their activities into the
learned ICs, as you say. It is also possible to separately decompose the
lowpass_data (= data - highpass_data), but it can't be guaranteed that ICA
is as suitable for this, (particularly if the lowpas activity is dominated
by moving-potentials (from scalp or from cortex); ICA assumes source
spatial stationarity, and is much less efficient
encapsulating spatially-moving sources (basically, separating it into a sum
of a series of 'overlapping movie frames' - requiring multiple deg of
freedom (DoF)).
But this is easy to try - though perhaps not as easy to judge / interpret...

Scott


On Tue, Aug 29, 2023 at 12:30 PM Jan Karsten via eeglablist <
eeglablist at sccn.ucsd.edu> wrote:

> Dear list members,
>
> I am currently working on a preprocessing procedure for EEG data with the
> aim to analyse the movement-cortical potential. My main purpose for the
> preprocessing  is to reduce the amount of artefact contamination
> (especially eye blinks) using ICA. Since we are interested in the MRCP, I
> am following the suggestion to split the data before the ICA to apply a
> high-pass filter (1 Hz) to the data I am running the ICA algorithm on and
> attach the solution to the second dataset I am using for the analysis.
>
>
> Now my question is:
>
> Because the ICA is trained with data that does not contain low frequency
> content (due to the high pass filter at 1 Hz), I am concerned, that this
> low frequency content will be randomly distributed across the ICA
> components when applied to the second dataset (in my case with a band pass
> filter of 0.05 - 5 Hz). Hence, in the worst case, an ICA component that
> contains mostly eye blinks will also contains more valuable information
> than usual in the low frequency domain. Do you think that the low-frequency
> data within the ICA sources is problematic or can it be ignored? What are
> your suggestions on how to best deal with such data?
>
>
>
> I attached a picture to the mail for clarification (I hope it can be seen
> through the list), showing an ICA component containing eye blinks acquired
> from the split datasets.
>
> Thanks in advance for your help and I am looking forward for your replies,
>
> Jan
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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