[Eeglablist] Pipeline questions

Simon-Shlomo Poil poil.simonshlomo at nbt-analytics.com
Thu Nov 19 11:05:41 PST 2015

Dear Alexandre,

Yes. The NBT toolbox includes PLI  see www.nbtwiki.net
The function is called nbt_doPLI.m

Best wishes,

Simon-Shlomo Poil, Dr.
CTO, NBT Analytics BV
Science Park 402
1098XH Amsterdam
The Netherlands
Website: https://www.nbt-analytics.com

2015-11-17 18:03 GMT+01:00 Alexandre Lehmann <alexandre.lehmann at gmail.com>:

> 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
>>> [image: Kessler Foundation Signature]
>>> [image: Kessler Foundation Facebook Page]
>>> <http://www.facebook.com/?ref=home#!/pages/Kessler-Foundation/123330414345741?sk=info> [image:
>>> Kessler Foundation You Tube]
>>> <http://www.youtube.com/user/KesslerFoundation> [image: Kessler
>>> Foundation Research Twitter Page] <http://www.twitter.com/KesslerFound>
>>> 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
> _______________________________________________
> 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

NBT Analytics BV

IMPORTANT: This message and any attachments are intended for the individual 
or entity named above. It may contain confidential, proprietary or legally 
privileged information. No confidentiality or privilege is waived or lost 
by any mistransmission. If you are not the intended recipient, you must not 
read, copy, use or disclose this communication to others; also please 
notify the sender by replying to this message, and then delete it from your 
system. Thank you.

-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://sccn.ucsd.edu/pipermail/eeglablist/attachments/20151119/2d0ea3b7/attachment.html>

More information about the eeglablist mailing list