Greetings Arnaldo,<br><br>You should be able to use the function: <a>eeg_regepochs<br>documentation available on EEGLAB functions list http://sccn.ucsd.edu/eeglab/allfunctions/<br><br>alternatively, you can make an event text file, load up the events into matlab, and then epoch on those.<br>
</a><div><br></div><br>
<br><br><div class="gmail_quote">On Mon, Apr 11, 2011 at 11:47 AM, Arnaldo Batista <span dir="ltr"><<a href="mailto:agb@fct.unl.pt">agb@fct.unl.pt</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">
Dear all,<br>
<br>
We are using the physionet EEG epilepsy database which features continues<br>
EEG data (23 channels).<br>
<br>
We would like to analyze this data on fixed successive 4096 (samples) data<br>
chunks, so we would need to divide the continuous signal on epochs with 4096<br>
samples each. We would be left with around 200 epochs (per channel) to<br>
process.<br>
<br>
How can we, under EEGLAB, to epoch our signal in this way?<br>
<br>
Thanks in advance<br>
<br>
<br>
Arnaldo Batista<br>
<br>
<br>
-----Original Message-----<br>
From: <a href="mailto:eeglablist-bounces@sccn.ucsd.edu">eeglablist-bounces@sccn.ucsd.edu</a><br>
[mailto:<a href="mailto:eeglablist-bounces@sccn.ucsd.edu">eeglablist-bounces@sccn.ucsd.edu</a>] On Behalf Of James Desjardins<br>
Sent: sábado, 9 de Abril de 2011 04:09<br>
To: <a href="mailto:eeglablist@sccn.ucsd.edu">eeglablist@sccn.ucsd.edu</a><br>
Subject: Re: [Eeglablist] <no subject><br>
<br>
Hi Guilia,<br>
<br>
While trying to compare the results of multiple EEG systems I wrote a plugin<br>
to interpolate scalp channels to the coordinates found in an *.sfp, *.elp,<br>
etc.. coordinate file (e.g., take files recorded with a<br>
256 channel EGI net, a 128 channel EGI net and a custom 128 channel BioSemi<br>
cap and spatially resample all the files to the coordinates found in a<br>
standard 81 channel extended 10-20 montage coordinate file). This plugin is<br>
basically just a coordination of the readlocs, coregister and eeg_interp<br>
functions in EEGlab, but was designed to be easily implemented into batching<br>
scripts.<br>
<br>
While this works well for the analysis of scalp data in study sets (forces<br>
every file to have an identical scalp montage) I do not know what the<br>
effects would be on subsequent ICA decompositions.<br>
<br>
Let me know if this sounds like something that would be useful to you, if so<br>
I will add some documentation and make it available.<br>
<br>
James Desjardins<br>
Technician, MA Student<br>
Department of Psychology, Behavioural Neuroscience Cognitive and Affective<br>
Neuroscience Lab Brock University<br>
500 Glenridge Ave.<br>
St. Catharines, ON, Canada<br>
L2S 3A1<br>
<a href="tel:905-688-5550" value="+19056885550">905-688-5550</a> x4676<br>
<br>
<br>
Quoting Scott Makeig <<a href="mailto:smakeig@gmail.com">smakeig@gmail.com</a>>:<br>
<br>
> Guilia -<br>
><br>
> To combine datasets with 128 and 64 channels, construct an EEGLAB<br>
> STUDY containing datasets with 64 channels. Does the 128-channel<br>
> montage include the 64-channel montage as a subset? If so, the<br>
> reduction from 128- to 64--channel datasets should be simple.<br>
><br>
> If not, then you may want to spatially resample. My suggested approach<br>
> for that would be to perform ICA (optimally, see the wiki tutorial) on<br>
> each of the 128-channel data sets, then resample the 3-D projection of<br>
> each independent component to the locations of the the 64 channels<br>
> used in your other datasets. There is not an EEGLAB function to do<br>
> just that, but it should not be difficult (for someone) to build. Then<br>
> reconstitute each 128-channel data set by summing the back-projections<br>
> of all the 128 components (perhaps omitting selected 'noise'<br>
> components) to the 64 channels. Note that you will then no longer be<br>
> able to separate all (<=) 128 components when you decompose the new<br>
> data using ICA (since the data now have only 64 degrees of freedom).<br>
><br>
> A possibly simpler those less refinable method would be to sample the<br>
> 64 channels in the interpolated scalp maps of every raw data point -<br>
> requiring the same scalp map resampling function.<br>
><br>
> Resampling the data from 64 to 128 channels is probably less optimal<br>
> (and less efficient). For one, it would still allow recovery of only<br>
> 64 independent components because the rank of the data matrix (>>help<br>
> rank) would remain 64, even when 128 channels have been resampled from<br>
> the scalp maps.<br>
><br>
> Scott Makeig<br>
><br>
> On Tue, Apr 5, 2011 at 8:23 AM, Giulia Righi <<a href="mailto:grighi@gmail.com">grighi@gmail.com</a>> wrote:<br>
><br>
>> HI all<br>
>><br>
>> I was wondering whether there is a way to combine datasets with<br>
>> different numbers of channels (64 and 128 channel nets) to include<br>
>> all participants within a study design to run ERSPs and ITCs?<br>
>><br>
>> thank you<br>
>><br>
>> giulia<br>
>><br>
>><br>
>> _______________________________________________<br>
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>><br>
><br>
><br>
><br>
> --<br>
> Scott Makeig, Research Scientist and Director, Swartz Center for<br>
> Computational Neuroscience, Institute for Neural Computation & Adj.<br>
> Prof. of Neurosciences, University of California San Diego, La Jolla<br>
> CA 92093-0559, <a href="http://sccn.ucsd.edu/%7Escott" target="_blank">http://sccn.ucsd.edu/~scott</a><br>
><br>
<br>
<br>
<br>
<br>
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</blockquote></div><br>