[Eeglablist] ICA question

[Sara Graziadio]

Thanks a lot for your answers.
I wanted just to make sure that I can apply the weights calculated on a shorter dataset to a longer dataset.
And also to make sure that it is better to apply this procedure than to apply the ICA on the longer dataset with the noise.
Best wishes
Sara

From: Tarik S Bel-Bahar [mailto:tarikbelbahar at gmail.com]
Sent: 18 September 2012 04:03
To: Sara Graziadio
Cc: eeglablist at sccn.ucsd.edu
Subject: Re: [Eeglablist] ICA question

A good strategy is to try to clean your data as much
as possible of extreme artifacts
(these extreme artifacts would
attract ICA's attention too much).

Then, if you have enough time points in your data
to match ICA's requisites, you should be able
to achieve a clean and interpretable ICA decomposition.
With ICs that look interpretable in relation to known
EEG "components" expected in your protocol.
You may also find "real" ICs that you did not expect.

The resultant ICA information can then be
transferred to a longer continuous file,
as you have done. You will however still
have to deal with artifactual time periods
and artifactual epochs, one way or another.

ICA does not care if you give it epochs or continuous data. However it is important that you feed it
enough good clean data, data during which
the cognitive behavior you are interested in
is occurring. In addition to eliminating
"artifactual periods" you may also
want to eliminate all periods that are not
"cognitively"-relevant or "task"-relevant.

Good luck with your process and let the list know how
things go for you.



On Mon, Sep 17, 2012 at 4:26 AM, Sara Graziadio <sara.graziadio at newcastle.ac.uk<mailto:sara.graziadio at newcastle.ac.uk>> wrote:
Hello eeglab users,
I have a question about ICA. My data have some noise in some time intervals. I want to remove the noise before using the ICA but I want to have the whole dataset (continuous data, not epoched) to run some more analysis once the data are cleaned with the ICA. Is there a way to do this?
At the moment I am running the ICA on the dataset without the noise and then I am applying the ICA weights calculated on the short dataset to the whole dataset (with the noise). Do you think I can do this? Or is there any better method to obtain the whole dataset cleaned without decreasing the ICA performance?
Thank you

Best wishes

Sara

Sara Graziadio, PhD
Research Associate
Institute of Neuroscience
Newcastle University

Address:
Sir James Spence Institute
Royal Victoria Infirmary
Queen Victoria road, NE1 4LP
Newcastle upon Tyne, UK

Tel:  +44 (0)191 282 1377<tel:%2B44%20%280%29191%20282%201377>




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