[Eeglablist] Pipeline questions

[Tarik S Bel-Bahar]

Hello Amanda, a few notes below, hope they are useful ! Cheers!

For beta band coherence and the length of epochs, you may want to review
the methods from several recent articles you can find on Google Scholar in
major journals. I think a rule of thumb is you would want at least 3 or
more cycles of beta within your time period/epoch/segment. half a second
seems to . There may be some assumptions about stationarity that need to be
considered here too. See a recent review from this year of methods and
pitfalls in  connectivity from Stamm and colleagues.

 Overall, the settings on artifact detection need to be played with, and
you need to double check that the settings are working as expected for you.
Try exploring other eeglab-related tools for more "automatic" or different
methods for cleaning up your data, of which there are more and more of in
the field. You can also check the settings in published reports, but I
don't think many articles include specifics at that level of detail,
unfortunately. you might especially like ASR and PREP tools from Kothe and
colleagues for noisy channel detection.See also SCADS and fieldtrip based
cleaning techniques.

Average reference on 64 channels is ok. you may want to remove channels on
the face, neck, from the average reference. Some groups average reference
after ICA. See past eeglablist discussions regarding not leaving bad,
weird, or uninformative channels in for re-referencing.

Your're fine if you don't include non-EEG channels for ICA, it will pick up
eye movement and muscle artifacts anyway

Try SASICA and other IC rejection toolboxes too, competitive runoffs
between these toolboxes have not yet been done. there are ICs that are not
pure artifact, and not pure brain dynamics that should likely not be
rejected.

I don't think you need to run ICA again, unless you're getting much better
results from re-running ICA, which should not be the case. Read Onton &
Makeig chapter in Luck Handbook of ERP components. See also ICA video
tutorials at EEGLAB summer school online.

I'd recommend you stay with the ~75% data you have, and not re-apply it to
the raw data. You're actually not dropping a lot, so it seems like you have
clean data. I would double-check that you are cleaning well-enough. If you
IC decompositions have several known ICs, and few or none that are
dominated by single-trial activity, then you've likely cleaned enough.

It won't hurt to apply the ICs to the raw data and have a look at things
that way too. this would allow for near-continuous analysis of the ICs at
least over the whole session, although your periods of interest are likely
only at specific times or trials.

I think there are two camps (at least) in EEG-ICA land. One camp rejects
just eye-artifacts and perhaps muscle artifacts. The other camp removes
everything except the really cognitive-brain ICs.
>From the perspective of EEGLAB, look at it this way. You've decomposed the
data into ICs, which should reflect discrete brain activity with distinct
spatial-electrode maps.
Other camps choose to recombine their ICs after a little or a lot of
cleaning, and they analyze this reconstituted and very clean (perhaps too
clean sometimes) data.

Other camps argue that decomposition techniques are the proper way to
analyze EEG data, and the correct data to analyze is the ICs over time.
>From this view, these are the "real" components, the ICs.
In other words, why not take your good ICs and do beta-band coherence
between the ICs themselves.










On Thu, Oct 22, 2015 at 9:58 AM, Armand Hoxha - Volunteer <
AHoxha at kesslerfoundation.org> wrote:

> Dear EEGLAB community,
>
>
>
> I am currently working on a dataset from which I need to get Beta band
> coherence (motor cortex to muscle coupling). My processing pipeline for the
> dataset so far has been:
>
> 1-     Import data
>
> 2-     Add channels, Optimize Center, Set channel types
>
> 3-     Cleanline (default settings, to EEG and EMG)
>
> 4-     FIR 1-250Hz (pop_eegfiltnew, to EEG and EMG)
>
> 5-     Segment data in epochs (0.5 seconds of epochs, for coherence
> purposes I noticed in most studies the general number of windows is usually
> above 170, thus to achieve the same statistical relevance I needed to have
> windows of 0.5s rather than 1second. I am a little confused about which
> window length should be preferred for beta band coherence)
>
> 6-     Remove noisy channel (if more than 10% of data is “bad”, then I
> remove channel)
> 6a)pop_eegthresh on EEG channels, with limits of -50 to 50 uV
> 6b)pop_jointprob on EEG channels, single-channel std:6, All-channel std:2
> if a channel is generally responsible for about 10% of rejected epochs, I
> reject channel
>
> 7-     Interpolate rejected channels, pop_interp ;Spherical method
>
> 8-     Average reference EEG channels(for ICA and coherence calculations,
> our actual reference is between FCZ and CZ)
>
> 9-     Run ICA on EEG channels only ( I have read that EOG and ECG
> channels can be included, but they are bipolar readings with a different
> reference from EEG, should I include them regardless?
>
> 10-  Use MARA toolbox to get a general sense of how I should feel about
> some IC’s , also because I prefer the MARA spectrum plot over the default
> GUI
>
> 11-  Reject IC components related to EMG artifacts (facial movements,
> neck, jaw, eyes), from 64 components I reject ~20
>
> 12-  Reject epochs based on the Components (jointprob 6,2)
>
> 13-  Run ICA again, reject EMG components again
>
> 14-  Most of the time by this point I have retained ~75-85% of dataset,
> and I use this dataset to do coherence analysis. I have read that some have
> suggested instead to export the ICA matrix of the processed data, and apply
> it to the raw data?
>
>
>
>
>
> Am I rejecting too many IC’s from the data, or would this data produce
> reliable results?
>
>
>
> Thanks in advance,
>
>
>
> Armand Hoxha
>
> Biomedical Engineer, HPEL
>
> Kessler Foundation
>
> 1199 Pleasant Valley Way, West Orange, NJ 07052
>
>
>
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