[Eeglablist] Uniform epoching my EEG data

[Tarik S Bel-Bahar]

Greetings Arnaldo,

You should be able to use the function: eeg_regepochs
documentation available on EEGLAB functions list
http://sccn.ucsd.edu/eeglab/allfunctions/

alternatively, you can make an event text file, load up the events into
matlab, and then epoch on those.




On Mon, Apr 11, 2011 at 11:47 AM, Arnaldo Batista <agb at fct.unl.pt> wrote:

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