[Eeglablist] Detrending segmented data before ICA
Tarik S Bel-Bahar
tarikbelbahar at gmail.com
Tue Oct 27 16:04:39 PDT 2015
Hello Yamil, some notes below, hope you find them of use. cheers!
see Groppe et al on removing baseline for better ICs, and search past
eeglablist discussions on Google with your keywords, as similar questions
have come up before.
most would say it's a good thing to remove at least low-frequency drifts.
most would say it's okay to do a 1hz highpass, and/or "remove baseline for
each channel in the continuous data" for better ICs. be wary of a 1hz
highpass if you're doing erps.
yes it does not matter if you give ICA trials or continuous data, spatial
ICA as implemented in eeglab does not care about time for finding stable
scalp/source maps. It does matter what kind of data you give it, but that's
I guess one would not include much more than a 1000 ms or 500 ms baseline.
baselines are usually included for ICA of multiple trials/epochs/segments,
especially in erp protocols. Check the literature if people have done much
baseline-subtraction for epochs before ICA.
You might be interested in recent single-trial denoising and metrics work,
check on Google Scholar.
On Wed, Oct 21, 2015 at 7:50 AM, Yamil Vidal Dos Santos <
hvidaldossantos at gmail.com> wrote:
> Hi all,
> I have a question regarding getting data ready for ICA.
> As one factor that affects the quality of an ICA decomposition is the
> stationarity of the data, I decided to segment and detrend my data before
> running ICA. But I have read that if one would run ICA on segmented data,
> one should have a long enough baseline and/or should not remove baseline.
> This sounds strange to me, because as far as I know, ICA is not concerned
> about time. Furthermore, data is whitened before running ICA. Doesn't this
> imply a baseline removal?
> My concrete question is about the usefulness of detrending to improve data
> stationarity before ICA, but any clarifications about how to improve the
> chances of getting a good ICA decomposition will be appreciated.
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