[Eeglablist] Pipeline questions
poil.simonshlomo at nbt-analytics.com
Thu Nov 19 11:05:41 PST 2015
Yes. The NBT toolbox includes PLI see www.nbtwiki.net
The function is called nbt_doPLI.m
Simon-Shlomo Poil, Dr.
CTO, NBT Analytics BV
Science Park 402
2015-11-17 18:03 GMT+01:00 Alexandre Lehmann <alexandre.lehmann at gmail.com>:
> 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.
> 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
>> 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
>>> 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]
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>>> Kessler Foundation You Tube]
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