[Eeglablist] Pipeline questions

Tarik S Bel-Bahar tarikbelbahar at gmail.com
Tue Nov 17 21:17:44 PST 2015


Hello Alexandre, You can compute these on your own in matlab with not much
code once you've got loaded data and things set up right, perhaps you'll
get some specific suggestions from the list.
​These are examples of some tools that should have PLI that you can use or
hack
: SIFT toolbox within eeglab which should work with channels and ICs,
Fieldtrip, TAPEEG, EEGNET from Hassan et al., HERMES toolbox, Stam's
Brainwave. You might also benefit form M. X, Cohen's book Analyzing Neural
Time Series which has good explanations and matlab codes for PLI, the
matlab codes are online at his site for viewing. Last, the review article I
referred to
​ is
the one listed below, and it overlaps with the one you pointed out, thanks!

Opportunities and methodological challenges in EEG and MEG resting state
functional brain network research
​. ​

Clinical Neurophysiology
​ ​
2015
​ ​
Diessen, et al,
​ ​
C.J. Stam










On Tue, Nov 17, 2015 at 12:03 PM, Alexandre Lehmann <
alexandre.lehmann at gmail.com> wrote:
>
> Hello,
>
> Is anyone aware of a toolbox / software to compute phase lag index and
related measures ? I haven't seen that in EEGLAB ?
> Thank you for this insightful discussion, is the paper you mention ?
> Numan, T., Stam, C. J., Slooter, A. J. C., & van Dellen, E. (2015). Being
Conscious of Methodological Pitfalls in Functional Brain Network Analysis:
Anesthesiology, 123(2), 484‑485. http://doi.org/10.1097/ALN.0000000000000750
>
> Best
> Alexandre
>
>
>
> On Thu, Oct 22, 2015 at 6:50 PM, Tarik S Bel-Bahar <
tarikbelbahar at gmail.com> wrote:
>>
>> 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
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>> The information in this transmission is intended for official use of
the Kessler Foundation. It is intended for the exclusive use of the persons
or entities to which it is addressed. If you are not an intended recipient
or the employee or agent responsible for delivering this transmission to an
intended recipient, be aware that any disclosure, dissemination,
distribution or copying of this communication, or the use of its contents,
is strictly prohibited. If you received this transmission in error, please
notify the sender by return e-mail and delete the material from any
computer.   ­­
>>>
>>> _______________________________________________
>>> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
>>> To unsubscribe, send an empty email to
eeglablist-unsubscribe at sccn.ucsd.edu
>>> For digest mode, send an email with the subject "set digest mime" to
eeglablist-request at sccn.ucsd.edu
>>
>>
>>
>> _______________________________________________
>> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
>> To unsubscribe, send an empty email to
eeglablist-unsubscribe at sccn.ucsd.edu
>> For digest mode, send an email with the subject "set digest mime" to
eeglablist-request at sccn.ucsd.edu
>
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://sccn.ucsd.edu/pipermail/eeglablist/attachments/20151118/2226a6ae/attachment-0001.html>


More information about the eeglablist mailing list