[Eeglablist] Artifact detection and ICA on data with multiple epoch types

[Becky Prince]

Dear EEGLAB users,

I have a question about the best artifact detection and ICA procedures for
data sets with multiple epoch types of different lengths.

Each trial in my EEG task is made up of 3 non-overlapping epochs of
interest: 1) 4 seconds, 2) either 1.5 or 2.5 seconds, 3) 2.5 seconds.  For
my previous ERP experiments I've used the following data processing
procedure: epoch the data, reject bad epochs, run ICA on remaining epochs,
reject ICA components, analyze data in channel space.  For my current
experiment (with 3 epochs per trial), I haven't been able to run ICA on all
of the epoched data at once because, as soon as I epoch the data, it is
split into a separate data set.  I'm fairly sure that these 3 separate
epoched data sets cannot be merged/appended for a single ICA run because
the epochs are of differing lengths.

I've come up with 2 different protocols for processing the individual data
sets.  I'm not sure which is better or if I'm overlooking other options.

Solution 1: artifact rejection and ICA on continuous data
1) reject artifacts in continuous data
2) run ICA on continuous data
3) reject ICA components
4) epoch the data (creates 3 separate data sets)
5) analyze data in channel space

Solution 2: artifact rejection and ICA on epoched data
1) epoch the data (creates 3 separate data sets)
Then for each of the 3 epoched data sets:
2) reject epochs
3) run ICA on remaining epochs
4) reject ICA components
5) analyze data in channel space

My problem with solution 1 is that it is much more time consuming to detect
the artifactual sections of continuous data than it is to detect and reject
bad epochs.  Also, ICA seems to run more slowly in this case (even after
taking into account the differences in the size of the continuous and
epoched data sets).  My problem with solution 2 is that it doesn't seem
right to run ICA separately for each epoched data set for the same
subject/session.  Because I am doing within-subject comparisons among the 3
trial segments, I'm concerned about producing artificial differences among
these epochs as a result of differences in the ICA decomposition and
component rejection.

Sorry for the long question.  Can anyone tell me what they would recommend?
 Any suggestions and/or criticisms of my current processing steps would be
very helpful!

Many thanks,
Becky
PhD Candidate
Department of Psychology
University of York
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