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Hi Edward.<br>
<br>
For ICA it is irrelevant whether the data is continuous or not. ICA
(infomax ICA) does not take into account the temporal order of the time
samples, so you could perfectly well shuffle all the samples and the
result would be the same. The important thing is that you try to keep
the data as clean as possible and that the data you inject into ICA
span those time periods in which the cognitive processes you are
interested in were more likely to be active. So I wouldn't be worried
because the data is discontinuous.<br>
<br>
Other algorithms, as SOBI ICA, do take into account the temporal
sequence of the time samples. However, you can always segment the data
into 90s epochs before calling runica so that eeglab knows where the
discontinuities are, and apply then whatever ICA algorithm you prefer.<br>
<br>
David.<br>
<br>
Edward Justin Modestino, M.Phil. wrote:
<blockquote
cite="mid:1249.76.108.254.179.1203357971.squirrel@www.ccs.fau.edu"
type="cite">
<pre wrap="">Hello,
I have data that I wished to import into EEGLAB to run ICA in an attempt
to remove eye movements and artifacts. All of my subjects are recorded in
90 second blocks with 82 EEG channels recorded at 256 hz. The majority
have 48 blocks, i.e. 48 X 90 second, thus approximately 72 minutes total
of recording. However, as stated earlier, they are in separate 90 second
blocks.
As the subjects had brief breaks between these 90 second recordings to
move and/or rest their eyes, the recorder was shut off. Thus the
recordings are NOT continuous for the whole experiment, but only for each
separate 90 second block.
Is it possible to run ICA on this data with the 90 second blocks? Or is
it not possible as the data is not continuous?
Could I concatenate the data, all 48 90 second blocks, into one large
matrix, and import it into EEGLAB to run ICA? Or would this not work as
it really was not continuous data in the first place?
There are 10 different conditions and only two require responses. This is
all done with markers for the reaction times, conditions, false alarms,
etc. Is there any protocol to deal with markers in EEGLAB?
Thanks,
Ed
</pre>
<blockquote type="cite">
<pre wrap="">Frederica -
You've misinterpreted -- ICA learns a (channels,channels) unmixing
matrix,
so the number of data frames (time points) needed to separate as many
components increases as the square of the number of channels. The faq is a
bit out of date -- in our work with 72-256 channel data in the lab, we
have
found that as the channel density becomes high, good ICA solutions
typically
require a considerable multiple of the channel number squared (up to 30 or
more for 256-channel data).
For 148-channel data I would like to collect 30*128^2 ~ 650k (~40 min of
data at 256 Hz) ... though it could well be that smaller data sets could
also give useful solutions. I will update the faq to better reflect this,
and will try to do a numerical study to get a more detailed heuristic
understanding.
Scott Makeig
On Feb 16, 2008 2:36 AM, Federica Di Grazia <a class="moz-txt-link-rfc2396E" href="mailto:federicadigrazia@hotmail.com"><federicadigrazia@hotmail.com></a>
wrote:
</pre>
<blockquote type="cite">
<pre wrap="">----- Original Message ----- *From:* Federica Di
Grazia<a class="moz-txt-link-rfc2396E" href="mailto:federicadigrazia@hotmail.com"><federicadigrazia@hotmail.com></a>
*To:* <a class="moz-txt-link-abbreviated" href="mailto:eeglablist@sccn.ucsd.edu">eeglablist@sccn.ucsd.edu</a>
*Sent:* Tuesday, January 29, 2008 10:13 PM
*Subject:* question on SICA approach
I saw the faq on Independent Component Analysis but I couldn't
understand
how many samples(in time) I need to analyze 148 channels with SICA
approach?
I saw that it's necessary a number of samples(in time) equal to the
square
of the channels, but for SICA the sample are represented by the 148
channels, so I need the square root of 148 as samples(in time)?
Best Regards,
____________________________________________________________________
Federica Di Grazia
Ph. D. Student in Electronics, Automation and Complex Systems
Engineering
Dipartimento di Ingegneria Elettrica Elettronica e dei Sistemi
Università degli Studi di Catania
v.le A.Doria 6 - 95125 Catania, Italy
Tel. +39-095-7382342
Fax +39-095-330793
e-mail: <a class="moz-txt-link-abbreviated" href="mailto:fdigra@diees.ing.unict.it">fdigra@diees.ing.unict.it</a>
<a class="moz-txt-link-abbreviated" href="mailto:federicadigrazia@hotmail.com">federicadigrazia@hotmail.com</a>
____________________________________________________________________
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</pre>
</blockquote>
<pre wrap="">
--
Scott Makeig, Research Scientist and Director, Swartz Center for
Computational Neuroscience, Institute for Neural Computation, University
of
California San Diego, La Jolla CA 92093-0961, <a class="moz-txt-link-freetext" href="http://sccn.ucsd.edu/~scott">http://sccn.ucsd.edu/~scott</a>
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</pre>
</blockquote>
<pre wrap=""><!---->
---------------------------------------------------
Edward Justin Modestino, M.Phil.
Ph.D. Candidate in Complex Systems and Brain Sciences
Cognitive Neurodynamics Laboratory
Center for Complex Systems and Brain Sciences
Florida Atlantic University
(561) 297-2238
Fax: (561) 297-3634
<a class="moz-txt-link-abbreviated" href="mailto:modestino@ccs.fau.edu">modestino@ccs.fau.edu</a>
<a class="moz-txt-link-freetext" href="http://www.ccs.fau.edu/~modestino/">http://www.ccs.fau.edu/~modestino/</a>
Lab: <a class="moz-txt-link-freetext" href="http://www.ccs.fau.edu/~bressler/CNL_CCSBS/CNL_CCSBS_FAU.html">http://www.ccs.fau.edu/~bressler/CNL_CCSBS/CNL_CCSBS_FAU.html</a>
---------------------------------------------------
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</pre>
</blockquote>
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