[Eeglablist] epochconsist treats files with only one epoch as a continuous file

[Ingram, Richard E - ingramre]

Hi all - I have a question about epoch consistency when using the STUDY feature.  I've seen 6 other posts on this but haven't seen a resolution.  Can anyone help?

I have collected continuous data throughout an experimental session.  I wrote software to display stimuli, capture performance data, and send marker from software to EEG data stream.

The experiment structure has 8 30 second blocks per subject (120 blocks total).  (this is exploratory so subject N is low.)  I used EEGLAB epoch to extract the 30 sec from each block based on unique marker for a particular block.  So, for each subject, I have 15 30 sec blocks of continuous data.  All are 3840 frames (30 x 128 sampling).

I have tried several different iterations of setting up a STUDY but all say that epoch consistency is no.  I double checked allpnts etc as noted by Arno in another post and these parameters check out.  A couple of questions:

Since I have continuous data only (no repeated epochs), does EEGLAB not recognize the data as an epoch and so determine epoch consistency = no based on that?  If so, would putting a copy of the 30 block data in the same file allow STUDY to see determine epoch consistency = yes.

Also, while the data seem to be pretty clean overall, there are some segments identified for rejection.  However, if I reject the data, the epochs will be of different lengths and then would EEGLAB  determine epoch consistency = no because of unequal epoch length?  Sort of a catch 22.

I know that EEGLAB is designed with ERP in mind but I'm trying to figure out how to best use it for analyzing continuous data.

Thanks for any and all advice.

Rich
________________________________
From: eeglablist-bounces at sccn.ucsd.edu [eeglablist-bounces at sccn.ucsd.edu] on behalf of Makoto Miyakoshi [mmiyakoshi at ucsd.edu]
Sent: Friday, March 18, 2016 3:32 AM
To: Whitehead, Kimberley
Cc: eeglablist at sccn.ucsd.edu
Subject: Re: [Eeglablist] epochconsist treats files with only one epoch as a continuous file

Dear Kim,

Thank you for your suggestion. If you wish, would you please share your finding in EEGLAB Bugzilla as enhancement?

https://sccn.ucsd.edu/bugzilla/enter_bug.cgi<https://urldefense.proofpoint.com/v2/url?u=https-3A__sccn.ucsd.edu_bugzilla_enter-5Fbug.cgi&d=BQMFaQ&c=eLbWYnpnzycBCgmb7vCI4uqNEB9RSjOdn_5nBEmmeq0&r=MNemh5cDBpfHugrLsdnWIQ&m=DLYGVpCNw1x2U8nCpCfUyVtrj3scQCzX3-d_6I8-r6w&s=SsINElwl-XLHZ0hl21YqnZj8gw0PVvHYbo6mVlsqkJ4&e=>

By the way I've encountered a similar situation. I learned that one cannot make a single epoch with EEGLAB. Unfortunately I forgot how I work around the issue (probably I repeated the epoch twice so that it is recognized as a one-time repeated two epochs?)

Makoto

On Wed, Jan 20, 2016 at 4:46 PM, Whitehead, Kimberley <k.whitehead at ucl.ac.uk<mailto:k.whitehead at ucl.ac.uk>> wrote:
Hello

I wonder if anybody could please offer some advice on altering the eeg_checkset code so that it doesn’t automatically treat files with one epoch as continuous. I think the issue is here:

In eeg_checkset line 182:
case 'epochconsist', % test epoch consistency
                % ----------------------
                res = 'no';
                datasettype = unique_bc( [ EEG.trials ] );
                if datasettype(1) == 1 & length(datasettype) == 1, return; %
continuous data
                elseif datasettype(1) == 1,                        return; %
continuous and epoch data
                end;

The code says that if there is only 1 epoch, the file is continuous. This means
that if I try to include this file in a study in which most files have several
epochs, it says there is no epoch consistency and there is an error when I try
to do FFT analysis. However, the nature of my analysis means there are many
files with only 1 epoch. (My epochs are spontaneous transients in EEG which
I've zeropadded to make the same duration/same number of data points. I've
split them up by topography so it's common that in 1 file there may be just one
with, for example, a right occipital topography.)

Thanks, Kim




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--
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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