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<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D;mso-fareast-language:EN-US">I read that:<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D;mso-fareast-language:EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;margin-bottom:1.2pt;margin-left:19.2pt;text-indent:-18.0pt;line-height:18.0pt;mso-list:l0 level1 lfo1;background:white">
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</span></span></span><![endif]><span style="font-size:9.5pt;font-family:"Arial",sans-serif;color:black">You need minimum of (number of channel)^2 x 20 to 30 data points to perform ICA. The number ’20 to 30’ should (exponentially) increase as the number of channels
increases. This is the recommended length of the data by past publications from SCCN. This is purely an empirical values, and we have not performed a systematic investigation on it.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D;mso-fareast-language:EN-US">Thanks again to Makoto for his helpful pre-processing pipeline!<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D;mso-fareast-language:EN-US">Jumana
<o:p></o:p></span></p>
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<p class="MsoNormal"><b><span lang="EN-US" style="font-size:11.0pt;font-family:"Calibri",sans-serif">From:</span></b><span lang="EN-US" style="font-size:11.0pt;font-family:"Calibri",sans-serif"> Rafał Jończyk [mailto:rafal.jonczyk@gmail.com]
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<b>Sent:</b> 10 February 2017 07:55<br>
<b>To:</b> mmiyakoshi@ucsd.edu; Ahmad, Jumana <jumana.ahmad@kcl.ac.uk><br>
<b>Cc:</b> eeglablist@sccn.ucsd.edu<br>
<b>Subject:</b> Re: [Eeglablist] Slow cortical potential analysis-preprocessing pipeline<o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-family:"Arial",sans-serif">thank you very much Jumana and Makoto for your feedback! This is very helpful. I think I will also downsample to 250Hz to make EEG files lighter and potentially further improve ICA (as suggested
by Makoto in his pipeline). Out of curiosity, is there any risk of making ICA worse rather than better by downsampling the data (given that we don't go lower than 250Hz)?<o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-family:"Arial",sans-serif"><o:p> </o:p></span></p>
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<p class="MsoNormal"><span style="font-family:"Arial",sans-serif">Best,<o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-family:"Arial",sans-serif">Rafal<o:p></o:p></span></p>
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<p class="MsoNormal">2017-02-10 4:30 GMT+01:00 Makoto Miyakoshi <<a href="mailto:mmiyakoshi@ucsd.edu" target="_blank">mmiyakoshi@ucsd.edu</a>>:<o:p></o:p></p>
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<p class="MsoNormal">Dear Rafal,<o:p></o:p></p>
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<p class="MsoNormal">At least, you can highpass filter the data for ICA purpose only. You can copy the weight matrix to the unfiltered data. Please consider doing it.<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal"><span style="font-size:9.5pt;font-family:"Arial",sans-serif">> The problem I have, however, is that ICA is not advised to be run on interpolated channels. Is there a possible compromise/solution to this issue?</span><o:p></o:p></p>
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<p class="MsoNormal"><span style="font-size:9.5pt;font-family:"Arial",sans-serif">In most cases, ICA will pick up the rank deficiency and automatically adjust the rank. So you'll be probably ok.</span><o:p></o:p></p>
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<p class="MsoNormal">Makoto<o:p></o:p></p>
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<p class="MsoNormal">On Tue, Feb 7, 2017 at 6:48 AM, Rafał Jończyk <<a href="mailto:rafal.jonczyk@gmail.com" target="_blank">rafal.jonczyk@gmail.com</a>> wrote:<o:p></o:p></p>
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<p style="margin-bottom:0cm;margin-bottom:.0001pt"><span style="font-family:"Arial",sans-serif">Dear eeglab(list) users,<o:p></o:p></span></p>
<p style="margin-bottom:0cm;margin-bottom:.0001pt"><span style="font-family:"Arial",sans-serif">I’m currently analysing a 64-channel dataset looking at a slow cortical potential – Stimulus Preceding Negativity (each epoch lasts 3800 ms). I'm using eeglab and
erplab. I’ve been thinking which processing steps would be best to get the a reliable dataset, i.e., representative of what’s really going on without unintentionally getting an effect due to incorrect preprocessing analysis.<o:p></o:p></span></p>
<p style="margin-bottom:0cm;margin-bottom:.0001pt"><span style="font-family:"Arial",sans-serif">Below I present the pipeline I’ve been experimenting with so far. Steps marked with „*” are done in erplab. I would really appreciate your feedback, because I’m
still relatively new to EEG analysis. Also, I shortly discuss the issue of channel interpolation/ICA below the pipeline.<o:p></o:p></span></p>
<p style="margin-bottom:0cm;margin-bottom:.0001pt"><span style="font-family:"Arial",sans-serif">1. Re-sample from 1000Hz to 500Hz<o:p></o:p></span></p>
<p style="margin-bottom:0cm;margin-bottom:.0001pt"><span style="font-family:"Arial",sans-serif">2. Edit channel locations + append Cz + re-reference to Cz<o:p></o:p></span></p>
<p style="margin-bottom:0cm;margin-bottom:.0001pt"><span style="font-family:"Arial",sans-serif">3. Filter: lowpass, cut-off at 20Hz, order 2 (12 dB/Octave)*<o:p></o:p></span></p>
<p style="margin-bottom:0cm;margin-bottom:.0001pt"><span style="font-family:"Arial",sans-serif">4. Epoch [-0.105 3.805]<o:p></o:p></span></p>
<p style="margin-bottom:0cm;margin-bottom:.0001pt"><span style="font-family:"Arial",sans-serif">5. Automatic Epoch Rejection (based on probability) + rejection based on visual inspection<o:p></o:p></span></p>
<p style="margin-bottom:0cm;margin-bottom:.0001pt"><span style="font-family:"Arial",sans-serif">6. ICA (runica)<o:p></o:p></span></p>
<p style="margin-bottom:0cm;margin-bottom:.0001pt"><span style="font-family:"Arial",sans-serif">7. Removing ICA components: only components related to eye movements + visual inspection<o:p></o:p></span></p>
<p style="margin-bottom:0cm;margin-bottom:.0001pt"><span style="font-family:"Arial",sans-serif">8. Convert epochs to a continuous dataset*<o:p></o:p></span></p>
<p style="margin-bottom:0cm;margin-bottom:.0001pt"><span style="font-family:"Arial",sans-serif">9. Creating event lists & assigning bins*<o:p></o:p></span></p>
<p style="margin-bottom:0cm;margin-bottom:.0001pt"><span style="font-family:"Arial",sans-serif">10. Epoching: [-100 3800]*<o:p></o:p></span></p>
<p style="margin-bottom:0cm;margin-bottom:.0001pt"><span style="font-family:"Arial",sans-serif">11. Baseline removal [-100 0]*<o:p></o:p></span></p>
<p style="margin-bottom:0cm;margin-bottom:.0001pt"><span style="font-family:"Arial",sans-serif">12. Artefact rejection (peak-to-peak moving window)*<o:p></o:p></span></p>
<p style="margin-bottom:0cm;margin-bottom:.0001pt"><span style="font-family:"Arial",sans-serif">13. Averaging + visual inspection*<o:p></o:p></span></p>
<p style="margin-bottom:0cm;margin-bottom:.0001pt"><span style="font-family:"Arial",sans-serif">14. Global average*<o:p></o:p></span></p>
<p style="margin-bottom:0cm;margin-bottom:.0001pt"><span style="font-family:"Arial",sans-serif">##Comment concerning Step 2: I assume it would be better to remove bad channels after re-sampling, then re-reference to Cz with bad channels removed, and then interpolate
bad channels from previous dataset (one with bad channels included) so as to avoid puting noise into the average. This will be followed by filtering in step 3. The problem I have, however, is that ICA is not advised to be run on interpolated channels. Is there
a possible compromise/solution to this issue?<o:p></o:p></span></p>
<p style="margin-bottom:0cm;margin-bottom:.0001pt"><span style="font-family:"Arial",sans-serif">I would be very eager to follow the preprocessing and data cleaning advice suggested in Mokoto’s pipeline, PREP pipeline, or CleanLine plugin more closely, but their
approach is largely based on high-pass filtering which would eliminate the slow drifts I’m looking at. <o:p></o:p></span></p>
<p style="margin-bottom:0cm;margin-bottom:.0001pt"><span style="font-family:"Arial",sans-serif">Thank you for your insights!<br>
Best,<br>
Rafal<o:p></o:p></span></p>
<p style="margin-bottom:0cm;margin-bottom:.0001pt"><span style="font-family:"Arial",sans-serif;color:#888888">-- <o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:10.0pt;color:#888888">Rafał Jończyk</span><span style="color:#888888"><o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:10.0pt;color:#888888">Assistant Professor</span><span style="color:#888888"><o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:10.0pt;color:#888888">Faculty of English</span><span style="color:#888888"><o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:10.0pt;color:#888888">Adam Mickiewicz University, Poznań | Poland</span><span style="color:#888888"><o:p></o:p></span></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">_______________________________________________<br>
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<p class="MsoNormal"><span class="hoenzb"><span style="color:#888888">-- </span><o:p></o:p></span></p>
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<p class="MsoNormal"><span style="color:#888888">Makoto Miyakoshi<br>
Swartz Center for Computational Neuroscience<br>
Institute for Neural Computation, University of California San Diego</span><o:p></o:p></p>
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<p class="MsoNormal"><span style="font-size:10.0pt">Rafał Jończyk</span><o:p></o:p></p>
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<p class="MsoNormal"><span style="font-size:10.0pt">Assistant Professor</span><o:p></o:p></p>
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<p class="MsoNormal"><span style="font-size:10.0pt">Faculty of English</span><o:p></o:p></p>
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<p class="MsoNormal" style="margin-bottom:12.0pt"><span style="font-size:10.0pt">Adam Mickiewicz University, Poznań | Poland</span><o:p></o:p></p>
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<p class="MsoNormal"><b><span style="font-size:10.0pt"><a href="https://emea01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.springer.com%2Fus%2Fbook%2F9783319476346&data=01%7C01%7Cjumana.ahmad%40kcl.ac.uk%7C0bd2a89d7ed54f34d74308d4518a0f2e%7C8370cf1416f34c16b83c724071654356%7C0&sdata=N2ovkM2D1MY2DmjJWqCSEGk8XcPL0yzeUJqpYaRrzrE%3D&reserved=0" target="_blank"><span style="text-decoration:none"><img border="0" width="131" height="200" id="_x0000_i1025" src="https://docs.google.com/uc?export=download&id=0B-bMuTu3fxATSnhVb0dFTUV0MVU&revid=0B-bMuTu3fxATeElUV05hNmRjVEJZSHBxNzV6bExNemhRYWFzPQ"></span></a></span></b><o:p></o:p></p>
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