[FieldTrip] sliding window for ICA

Russell G Port russgport at gmail.com
Sat Feb 22 00:27:54 CET 2014

Hi All,

I have come across a problem and was hoping that someone had
experience/knew things that could be useful.I have some MEG data, and the
effort currently is to remove noise and artifact. I have the line noise
reduction working fine, the question that faces me is the following. When I
first read in my data I have the option of one of two methods. I can read
it in as 1 second epoch for the whole length of the experiment (contiguous
time bins, with no regard to events), or create 1 second epochs centered
around a certain trigger (the data contains 4 different triggers).
Initially I wanted to read in 1 second epochs (that spanned the entire
dataset), clean it of artifacts (jumps, muscle, EOG,ECG, anything else),
which would remove bad trials and components. I had then hoped to redefined
the epoch, so that I could create 4 separate subsets of data, each centered
around a separate trigger. It appears that I cannot do this (fieldtrip
actually refuses this specifically). Is this because of issues with
phase/jitter when putting the samples back together in different time sets?
Second, it has been suggested that we perform ICA analysis to detect EOG
and ECG artifacts on the data parsed just by timing (i.e. 1second bins
contiguous for the entire length of the data but no relation to triggers),
and have the component rejection scan my trials that are actually binned
the based on triggers to remove the components; even though this is not the
exact epochs used to classify the components. Is this possible?

While I think it would be better to do the artifact correction on the whole
dataset (all time/triggers included), would it actually be better to first
define trials by triggers and then repeat everything (artifact
rejection-wise)? This is very easy to do (and I have), the only issue comes
when trying to compare the using ft_timelockanalysis/ft_freqanalysis (as in
I have the issue that when I used ft_artifact_ecg to detect peaks in the
ECG channel (via z scores), zeros and NaNs are returned, and it fails to
graph; since often the ECG timing occurs with such timing that the full
pre/post time extends past what is actually in the trial. Is it proper just
to remove all ECG events that cross the boundaries of the trigger based
trials from the trial definitions  used in next step. Here the sections of
data contaminated with ECG are parsed and then tested to see coherence to
the ICA components? I would fear that this would produce erroneous results,
since the data for the ica would include the partial ECG event that I threw

Russ Port
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