[Eeglablist] ICA in short duration recordings

[Scott Makeig]

Paul -

You omit a crucial variable ... how many channels are you decomposing? The
complexity of the ICA model of the data is proportional to the square of
the number of channels, since ICA learns a square (channels x channels)
unmixing matrix. As the data length becomes shorter, the number of channels
that can be well-decomposed becomes smaller.  What is k for you? where

k = number_of_time_samples / number_of_channels^2

Scott

On Thu, Jan 20, 2022 at 11:54 AM Beach, Paul via eeglablist <
eeglablist at sccn.ucsd.edu> wrote:

> EEGLAB team – and other EEG’ers,
>
> I am currently working with a number of datasets with relatively short
> durations – 3-5 minutes, generally. My goal is to examine endogenous ERPs
> from heartbeat processing (heartbeat evoked potential) in controls and a
> clinical group. I utilize a processing protocol whose artifact rejection
> mainly involves ASR, epoching to heartbeats, and then use of ICA to reject
> components at the single subject level.
>
> I almost always run into an issue whereby I only get a few components in
> ICA (usually out of 50-60) that are *clearly* neural in origin and maybe a
> few where it’s a more difficult call. My understanding is that, in general,
> the majority of components will not be necessary to keep/won’t be neural
> (particularly ICs after the first 20 or so). However, talking with other
> EEG’ers I find that they will typically have 10 or so.
>
> Notwithstanding that EEGLAB does recommend not rejecting components until
> group analysis, I wanted to query thoughts on the ‘appropriateness’ of even
> using ICA on recordings of such relatively short duration. I’ve read the
> brief discussion in Makoto’s processing page on this and ASR does a great
> job removing lots of things like eye blinks and even clear cardiac field
> artifact
>
> SO: should one just rely on ASR and perhaps use ICA for eye components
> that are missed (and keep everything else)? Should one avoid ICA completely
> for shorter duration recordings? Should I continue what I’m already doing?
> Some other middle ground? Or should I just forget about single subject
> level IC rejection completely and rely on its use in group-level analysis?
>
> Thanks for your time and thoughts.
> --
> Paul Beach DO, PhD
> PGY6, Movement Disorder Fellow
> Department of Neurology
> Emory University School of Medicine
>
>
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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