<div dir="ltr"><div><div><div><div><div><div><div>Hi Tarik,<br><br></div>Thanks a lot for the detailed response. Really helpful. :)<br><br></div>*The data is not PREPed again, it's the same data loaded in step1 of the pipelines. It was not clear earlier.<br><br></div>*It seems reasonable to move downsampling at step2. It will likely reduce the filtering time as well.<br><br></div>*You have suggested to drop a channel, instead of using PCA. Can you please recommend the criteria for selection of channel to be dropped, or it can be any channel? As per Makoto's pipeline, it is reasonable to drop the reference channel, but the data I have is not referenced. Although I can reference it to a particular channel offline in EEGLAB, but I would like to go with average reference, as given by PREP pipeline.<br><br></div>*Do you have any suggestions of using ICA with PREP pipeline (because of the interpolation step)? Here's what I have come up with: Use PREP pipeline to clean_line and identifying the bad channels. For ICA, use the clean_lined data having only the good channels (chans_G) referenced to the average of chans_G. After removing bad ICs (and rejecting any leftover bad epochs), interpolate and average reference the data. However, I think the data will not be referenced to the 'true' average now, one of the targets of PREP, since good channels were already referenced to average of chans_G. Is there a better way of doing ICA with PREP?<br></div><br></div>Thanks.<br></div><div class="gmail_extra"><br><div class="gmail_quote">On Tue, Sep 12, 2017 at 7:22 AM, 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:rgb(51,51,153)">Some followup points here for your Samran:</div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)">1. many researchers use and trust near fully automatic or fully automatic pipelines. See for example TAPEEG. It really depends on their biases (e.g., being programmers/engineers versus EEG researchers) and their needs (e.g., having dozens versus thousands of files). Further, automated pipeleines can be tweaked to your satisfaction, and can also provide "quality metrics" to know when there are issues in the pipeline.</div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)">2. Your "Drop artifactual ICs" step can be complex. Some researchers only keep neural ICs, some researchers only remove artifactual ICs, some researchers fully trust the results of ICA classifcation plugins such as Adjust, MARA, IC-MARA, and SASICA. If you haven't had a chance to use/test the results form those plugins, please be sure to do so.</div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)">3. If you haven't had a chance to yet, make sure to avail yourself of the excellent IC classification site from Luca at the following link. I usually recommend to students and beginners to do at least 500 classifications there. </div><div class="gmail_default"><font color="#333399"><a href="http://reaching.ucsd.edu:8000/tutorial/overview" target="_blank">http://reaching.ucsd.edu:8000/<wbr>tutorial/overview</a></font><br></div><div><div class="h5"><div class="gmail_default"><font color="#333399"><br></font></div><div class="gmail_default"><font color="#333399"><br></font></div><div class="gmail_default"><font color="#333399"><br></font></div><div class="gmail_default"><font color="#333399"><br></font></div><div class="gmail_default"><font color="#333399"><br></font></div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_extra"><br><div class="gmail_quote">On Mon, Sep 11, 2017 at 11:55 AM, 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:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div dir="ltr"><div style="color:rgb(51,51,153)">Hello Samran, here's notes for you below, good luck on your eeg adventures!</div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)">******NOTES FOR SAMRAN********</div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)">*you need to understand/know exactly what PREP is doing, and if you want comments on that, you should list the steps you think PREP is doing.</div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)">*your pipeline 1 seems okay. One can drop channels instead of PCA.</div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)">*Note It's generally not recommended to interpolate channels before ICA.</div><div style="color:rgb(51,51,153)">*Not sure why you are running PREP again (I guess it's okay if it does exactly the same thing as earlier.</div><div style="color:rgb(51,51,153)">*Reject epochs at step 13 after reviewing the data, unless for some reason you trust that A) you have removed all artifactual ICs and B) there are no remaining artifactual periods in the epoched data </div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)">*In your Pipeline 2, it's up to users whether or not they run a second ICA after pruning the data of Bad ICs. I would not recommend that, but you can look in past eeglablist answers, and in Makoto's processing suggestions, and in publications using ICA for EEG.</div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)">*Double check that you don't need to move the resampling to the be first or second step.</div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)">Your question #1</div><div style="color:rgb(51,51,153)">***ICA in eeglab does not care if there are discontinuities in the data, so it does not matter if you give it continuous data with breaks, or epoched data. It mixes up the time points and focuses on spatial patterns (not temporal patterns).</div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)">your question #2</div><div style="color:rgb(51,51,153)">I've specified above that the Data Analyst (you) needs to be sure there is no dirty data going into your averages and metrics. That is regardless of whether or not you already pruned your data by rejecting ICs. Be careful to NOT DO everything automatically until you have checked the results of your pipelines (at the epoch level and averaging level), and your are sure really sure that you don't need to go extra cleaning after bad IC rejection.</div><div style="color:rgb(51,51,153)">In short, there may still be dirt in the data after rejecting artifactual ICs. You need to personally check whether there is or is not remaining dirt in the data, and you need to be careful not to "assume" that things are working, but rather "check fully" that things are working.</div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)">Your question #3b</div><div style="color:rgb(51,51,153)">The PCA correction is correct. Personally I've had better success with "dropping a channel before ICA to account fix average referencing's drop in rank" instead of the PCA flag in runica. In other words, after average referecing, and before ICA, I drop 1 channel rather than use the PCA reduction.</div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)">Your Question $3b</div><div style="color:rgb(51,51,153)">Don't interpolate before ICA.</div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><br></div></div>
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