<div dir="ltr"><div>Hello,</div><div><br></div><div>Is anyone aware of a toolbox / software to compute phase lag index and related measures ? I haven't seen that in EEGLAB ?</div><div>Thank you for this insightful discussion, is the paper you mention ? <br></div><div><div style="line-height:2;padding-left:2em" class="">
<div class="">Numan, T., Stam, C. J., Slooter, A. J. C., & van Dellen, E. (2015). Being Conscious of Methodological Pitfalls in Functional Brain Network Analysis: <i>Anesthesiology</i>, <i>123</i>(2), 484‑485. <a href="http://doi.org/10.1097/ALN.0000000000000750">http://doi.org/10.1097/ALN.0000000000000750</a></div><div class=""><br></div><div class="">Best</div><div class=""><span style="text-indent: -2em;">Alexandre</span><br></div>
<span class="" title="url_ver=Z39.88-2004&ctx_ver=Z39.88-2004&rfr_id=info%3Asid%2Fzotero.org%3A2&rft_id=info%3Adoi%2F10.1097%2FALN.0000000000000750&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Being%20Conscious%20of%20Methodological%20Pitfalls%20in%20Functional%20Brain%20Network%20Analysis%3A&rft.jtitle=Anesthesiology&rft.volume=123&rft.issue=2&rft.aufirst=Tianne&rft.aulast=Numan&rft.au=Tianne%20Numan&rft.au=Cornelis%20Jan%20Stam&rft.au=Arjen%20Jacob%20Cornelis%20Slooter&rft.au=Edwin%20van%20Dellen&rft.date=2015-08&rft.pages=484-485&rft.spage=484&rft.epage=485&rft.issn=0003-3022&rft.language=en"></span></div></div><div><br></div><div><br></div></div><div class="gmail_extra"><br><div class="gmail_quote">On Thu, Oct 22, 2015 at 6:50 PM, Tarik S Bel-Bahar <span dir="ltr"><<a href="mailto:tarikbelbahar@gmail.com" target="_blank">tarikbelbahar@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><div class="gmail_default" style="color:#333399">Hello Amanda, a few notes below, hope they are useful ! Cheers!</div><div class="gmail_default" style="color:#333399"><br></div><div class="gmail_default" style="color:#333399">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.</div><div class="gmail_default" style="color:#333399"><br></div><div class="gmail_default" style="color:#333399"> 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.</div><div class="gmail_default" style="color:#333399"><br></div><div class="gmail_default" style="color:#333399">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.</div><div class="gmail_default" style="color:#333399"><br></div><div class="gmail_default" style="color:#333399">Your're fine if you don't include non-EEG channels for ICA, it will pick up eye movement and muscle artifacts anyway</div><div class="gmail_default" style="color:#333399"><br></div><div class="gmail_default" style="color:#333399">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. </div><div class="gmail_default" style="color:#333399"><br></div><div class="gmail_default" style="color:#333399">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.</div><div class="gmail_default" style="color:#333399"><br></div><div class="gmail_default" style="color:#333399">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. </div><div class="gmail_default" style="color:#333399"><br></div><div class="gmail_default" style="color:#333399">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.</div><div class="gmail_default" style="color:#333399"><br></div><div class="gmail_default" style="color:#333399">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.</div><div class="gmail_default" style="color:#333399">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.</div><div class="gmail_default" style="color:#333399">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.</div><div class="gmail_default" style="color:#333399"><br></div><div class="gmail_default" style="color:#333399">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.</div><div class="gmail_default" style="color:#333399">In other words, why not take your good ICs and do beta-band coherence between the ICs themselves.</div><div class="gmail_default" style="color:#333399"><br></div><div class="gmail_default" style="color:#333399"><br></div><div class="gmail_default" style="color:#333399"><br></div><div class="gmail_default" style="color:#333399"><br></div><div class="gmail_default" style="color:#333399"><br></div><div class="gmail_default" style="color:#333399"><br></div><div class="gmail_default" style="color:#333399"><br></div><div class="gmail_default" style="color:#333399"><br></div><div class="gmail_default" style="color:#333399"><br></div></div><div class="gmail_extra"><br><div class="gmail_quote"><div><div class="h5">On Thu, Oct 22, 2015 at 9:58 AM, Armand Hoxha - Volunteer <span dir="ltr"><<a href="mailto:AHoxha@kesslerfoundation.org" target="_blank">AHoxha@kesslerfoundation.org</a>></span> wrote:<br></div></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div><div class="h5">
<div lang="EN-US" link="#0563C1" vlink="#954F72">
<div>
<p class="MsoNormal">Dear EEGLAB community,<u></u><u></u></p>
<p class="MsoNormal"><u></u> <u></u></p>
<p class="MsoNormal">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:<u></u><u></u></p>
<p><u></u><span>1-<span style="font:7.0pt "Times New Roman"">
</span></span><u></u>Import data<u></u><u></u></p>
<p><u></u><span>2-<span style="font:7.0pt "Times New Roman"">
</span></span><u></u>Add channels, Optimize Center, Set channel types<u></u><u></u></p>
<p><u></u><span>3-<span style="font:7.0pt "Times New Roman"">
</span></span><u></u>Cleanline (default settings, to EEG and EMG)<u></u><u></u></p>
<p><u></u><span>4-<span style="font:7.0pt "Times New Roman"">
</span></span><u></u>FIR 1-250Hz (pop_eegfiltnew, to EEG and EMG)<u></u><u></u></p>
<p><u></u><span>5-<span style="font:7.0pt "Times New Roman"">
</span></span><u></u>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)<u></u><u></u></p>
<p><u></u><span>6-<span style="font:7.0pt "Times New Roman"">
</span></span><u></u>Remove noisy channel (if more than 10% of data is “bad”, then I remove channel)<br>
6a)pop_eegthresh on EEG channels, with limits of -50 to 50 uV<br>
6b)pop_jointprob on EEG channels, single-channel std:6, All-channel std:2<br>
if a channel is generally responsible for about 10% of rejected epochs, I reject channel<u></u><u></u></p>
<p><u></u><span>7-<span style="font:7.0pt "Times New Roman"">
</span></span><u></u>Interpolate rejected channels, pop_interp ;Spherical method<u></u><u></u></p>
<p><u></u><span>8-<span style="font:7.0pt "Times New Roman"">
</span></span><u></u>Average reference EEG channels(for ICA and coherence calculations, our actual reference is between FCZ and CZ)<u></u><u></u></p>
<p><u></u><span>9-<span style="font:7.0pt "Times New Roman"">
</span></span><u></u>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?<u></u><u></u></p>
<p><u></u><span>10-<span style="font:7.0pt "Times New Roman"">
</span></span><u></u>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<u></u><u></u></p>
<p><u></u><span>11-<span style="font:7.0pt "Times New Roman"">
</span></span><u></u>Reject IC components related to EMG artifacts (facial movements, neck, jaw, eyes), from 64 components I reject ~20<u></u><u></u></p>
<p><u></u><span>12-<span style="font:7.0pt "Times New Roman"">
</span></span><u></u>Reject epochs based on the Components (jointprob 6,2)<u></u><u></u></p>
<p><u></u><span>13-<span style="font:7.0pt "Times New Roman"">
</span></span><u></u>Run ICA again, reject EMG components again<u></u><u></u></p>
<p><u></u><span>14-<span style="font:7.0pt "Times New Roman"">
</span></span><u></u>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?<u></u><u></u></p>
<p class="MsoNormal"><u></u> <u></u></p>
<p class="MsoNormal"><u></u> <u></u></p>
<p class="MsoNormal">Am I rejecting too many IC’s from the data, or would this data produce reliable results?<u></u><u></u></p>
<p class="MsoNormal"><u></u> <u></u></p>
<p class="MsoNormal">Thanks in advance,<u></u><u></u></p>
<p class="MsoNormal"><u></u> <u></u></p>
<p class="MsoNormal">Armand Hoxha<u></u><u></u></p>
<p class="MsoNormal">Biomedical Engineer, HPEL<u></u><u></u></p>
<p class="MsoNormal">Kessler Foundation<u></u><u></u></p>
<p class="MsoNormal">1199 Pleasant Valley Way, West Orange, NJ 07052<u></u><u></u></p>
<p class="MsoNormal"><u></u> <u></u></p>
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