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

[Makoto Miyakoshi]

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

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
> 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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