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<p>Abby, </p>
<p>As we chatted eariler, I think the CSD method works well in high-density EEG; so, your proposed method sounds good. </p>
-Iman<br>
<br>
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<div id="divRplyFwdMsg" dir="ltr"><font face="Calibri, sans-serif" color="#000000" style="font-size:11pt"><b>From:</b> eeglablist-bounces@sccn.ucsd.edu <eeglablist-bounces@sccn.ucsd.edu> on behalf of Dickinson, Abigail <ADickinson@mednet.ucla.edu><br>
<b>Sent:</b> Thursday, January 19, 2017 10:16 AM<br>
<b>To:</b> Sean Fitzgibbon; mmiyakoshi@ucsd.edu<br>
<b>Cc:</b> eeglablist@sccn.ucsd.edu<br>
<b>Subject:</b> Re: [Eeglablist] Using a laplacian filter with ICA</font>
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<p>Thanks for your helpful responses Makoto & Sean. </p>
<p><br>
</p>
<p>I've been comparing using laplacian and ICA together, and in different orders on both my high density data, and then the downsampled montage. </p>
<p><br>
</p>
<p>So far (I'm going to try a few more datasets) - the results don't look strikingly different. But I do find that applying laplacian to 128 channels, downsampling to the 25 channel montage, and then running ICA (in order to remove EOG/EMG components) works
really nicely. From what I udnerstand this is probably because the laplacian is helping with the persistent muscle contamination, and the ICA with the more stereotyped artifacts. </p>
<p><br>
</p>
<p>Thanks again for your help. </p>
<p><br>
</p>
<p>Best, </p>
<p><br>
</p>
<p>Abby </p>
<br>
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<div id="divRplyFwdMsg" dir="ltr"><font face="Calibri, sans-serif" color="#000000" style="font-size:11pt"><b>From:</b> Sean Fitzgibbon <sean.fitzgibbon@ndcn.ox.ac.uk><br>
<b>Sent:</b> Thursday, January 12, 2017 11:43 PM<br>
<b>To:</b> mmiyakoshi@ucsd.edu<br>
<b>Cc:</b> Dickinson, Abigail; eeglablist@sccn.ucsd.edu<br>
<b>Subject:</b> Re: [Eeglablist] Using a laplacian filter with ICA</font>
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<div>Dear Abby
<div class=""><br class="">
</div>
<div class="">The first thing to note is that this method is not designed to deal with
<b class="">transient</b> muscle activity defined as “<i class="">transient contractions of facial, cranial, and neck muscles [that] generate high amplitude electrical signals... Typically [transient] EMG contamination is of sufficient amplitude that it can
be easily identified (visually or algorithmically) and it is common practice to excise and discard contaminated periods of EEG</i>”. We would still advocate excising these segments. Rather, the method seeks to rectify
<b class="">persistent</b> muscle contamination defined as "<i class="">sustained non-forceful contractions (e.g. tone) of these muscles [that] generate persistent low amplitude contamination</i>” that is very difficult to identify in the scalp EEG. The motivation
for our interest in persistent EMG is “<i class="">our experiments in paralysed conscious humans have demonstrated that persistent EMG strength exceeds EEG strength from frequencies as low as 10–20 Hz, more so peripherally than centrally, and that by 100 Hz, EMG
can be more than 200 times larger than EEG</i>”.</div>
<div class=""><br class="">
</div>
<div class="">The method, in brief, is to decompose the EEG using ICA, identify and remove EMG components, project back to scalp space, and then apply the laplacian. The average reference is not applied. We have shown in earlier papers, consistent with Makato’s
comment below, that ICA cannot completely decompose EMG, although it can improve SNR. However, using paralysed (muscle-free) EEG as the gold standard, we have demonstrated empirically that “<i class="">the combination of the ICA procedure and the surface
Laplacian results in a strong suppression of [persistent] EMG contamination at all scalp sites and frequencies</i>” and "<i class="">does not impair the detection of well-known, cerebral responses</i>”.</div>
<div class=""><br class="">
</div>
<div class="">Anyway, to actually respond to your question, it is commonly stated that high spatial density EEG (~128 channel) is required for Lacplacian. However this is challenged by Kayser and Tenke (2006; full ref below), at least for ERPs. Our own experience
is that we get reasonable approximations at lower densities, consistent with Kayser and Tenke. The 25-channels you have is quite low, but might still be worth trying. I cannot comment on whether is is useful to reverse the order and do Laplacian before ICA
as we have not ever tried that. However, "<i class="">Tenke and Kayser (2005) proposed a method called fPCA, which … demonstrate[s] compelling removal of EMG contamination … [and] requires application of the surface Laplacian to the EEG to estimate CSD, calculation
of the amplitude spectra from the CSD, and then application of principal component analysis (PCA) to decompose the spectra</i>” which sounds similar’ish to what you are proposing and may be a good starting point for you…</div>
<div class=""><br class="">
</div>
<div class="">Cheers, Sean</div>
<div class=""><br class="">
</div>
<div class="">
<div class="" style="margin:0px 0px 0px 20px; text-indent:-20px; line-height:normal">
Kayser, J., & Tenke, C. E. (2006). Principal components analysis of Laplacian waveforms as a generic method for identifying ERP generator patterns: II. Adequacy of low-density estimates.
<i class="">Clinical Neurophysiology</i>, <i class="">117</i>(2), 369–380. <a href="https://na01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fdoi.org%2F10.1016%2Fj.clinph.2005.08.033&data=01%7C01%7CADickinson%40mednet.ucla.edu%7Ca5dae3461d4145d0999a08d43b87ea2d%7C39c3716b64714fd5ac04a7dbaa32782b%7C0&sdata=N4Bsi%2BAGWhgKlxKsslrlZ7l%2BmoZj4o6Kq%2FHBAowASXk%3D&reserved=0" class="">
http://doi.org/10.1016/j.clinph.2005.08.033</a></div>
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<br class="">
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<div class="" style="margin:0px 0px 0px 20px; text-indent:-20px; line-height:normal">
Tenke, C. E., & KAYSER, J. (2005). Reference-free quantification of EEG spectra: Combining current source density (CSD) and frequency principal components analysis (fPCA).
<i class="">Clinical Neurophysiology</i>, <i class="">116</i>(12), 2826–2846. <a href="https://na01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fdoi.org%2F10.1016%2Fj.clinph.2005.08.007&data=01%7C01%7CADickinson%40mednet.ucla.edu%7Ca5dae3461d4145d0999a08d43b87ea2d%7C39c3716b64714fd5ac04a7dbaa32782b%7C0&sdata=WM4gZturJKtE9zFoUZ3Y2m4TWY25OPumDfS7t2k06cw%3D&reserved=0" class="">
http://doi.org/10.1016/j.clinph.2005.08.007</a></div>
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<div class="">_______________________________________________</div>
<div class=""><font color="#7a7a7a" class=""><br class="">
</font></div>
<div class=""><font color="#7a7a7a" class=""><b class="">Dr Sean Fitzgibbon </b> BSc PhD</font></div>
<div class=""><font color="#7a7a7a" class="">Postdoctoral Researcher</font></div>
<div class=""><font color="#7a7a7a" class=""><a href="https://na01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.fmrib.ox.ac.uk%2F&data=01%7C01%7CADickinson%40mednet.ucla.edu%7Ca5dae3461d4145d0999a08d43b87ea2d%7C39c3716b64714fd5ac04a7dbaa32782b%7C0&sdata=kW2pSDQA7z%2B%2BmPeYy2B2i2iJ8JsGoy47ZIhhX8ckkj8%3D&reserved=0" class="">FMRIB</a> Centre,
University of Oxford</font></div>
<div class=""><font color="#7a7a7a" class="">Oxford, United Kingdom</font></div>
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<blockquote type="cite" class="">
<div class="">On 13 Jan 2017, at 06:55, Makoto Miyakoshi <<a href="mailto:mmiyakoshi@ucsd.edu" class="">mmiyakoshi@ucsd.edu</a>> wrote:</div>
<br class="Apple-interchange-newline">
<div class="">
<div dir="ltr" class="">Dear Abby,
<div class=""><br class="">
</div>
<div class="">That's a new method for me and I don't know how it works. From the abstract, it seems you apply ICA, reject muscle components, then Laplacian? If so, the question is when you want to perform the average reference. Probably you want to average
reference after Laplacian so that the computed average reference is EMG free (not exactly free, but very much removed?)</div>
<div class=""><br class="">
</div>
<div class="">By the way, ICA cannot decompose EMG becaues EMG source spreads along with muscle fibers, so my former colleague told me. Empirically, I also experienced that muscle is harder to decomposed compared with other typical artifacts such as eyes, which
makes sense.</div>
<div class=""><br class="">
</div>
<div class="">From Lufthansa455 to Frankfurt,</div>
<div class=""><br class="">
</div>
<div class="">Makoto</div>
<div class=""><br class="">
</div>
<div class=""><br class="">
</div>
</div>
<div class="gmail_extra"><br class="">
<div class="gmail_quote">On Tue, Jan 10, 2017 at 10:28 AM, Dickinson, Abigail <span dir="ltr" class="">
<<a href="mailto:ADickinson@mednet.ucla.edu" target="_blank" class="">ADickinson@mednet.ucla.edu</a>></span> wrote:<br class="">
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex; border-left:1px #ccc solid; padding-left:1ex">
<div dir="ltr" class="">
<div id="m_9143504346431692209divtagdefaultwrapper" dir="ltr" class="" style="font-size:12pt; font-family:Calibri,Arial,Helvetica,sans-serif">
<p class="">Hi everyone, </p>
<p class=""><br class="">
</p>
<p class="">I jwondered if anyone could help me with a question I had regarding using a laplacian filter, and at which stage I should implement the filter. I currently have a processing pipeline which effectively cleans spontaneous data collected from low-functioning
children (EGI 128 channel, 500Hz). Due to the nature of our sample I am working with a few constraints, including a high amount of EMG and relatively short recording lengths (1-2 minutes). </p>
<p class=""><br class="">
</p>
<p class="">The processing pipeline I currently use involves:</p>
<p class="">-FIR filter (high pass:1Hz, low pass:100Hz)</p>
<p class="">-remove bad channels</p>
<p class="">-down-sampling to the 10-20 system 25 channel montage (<span class="" style="font-size:12pt">in order to have an adequate k-factor to </span><span class="" style="font-size:12pt">run ICA)
</span></p>
<p class="">-remove bad segments of data </p>
<p class="">-Run ICA</p>
<p class="">-Remove artifactual components</p>
<p class="">-Re-reference to average</p>
<p class=""><br class="">
</p>
<p class="">I recently saw a paper which advocated the use of a laplacian filter along with ICA to remove EMG (<a href="https://na01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.ncbi.nlm.nih.gov%2Fpubmed%2F25455426&data=01%7C01%7CADickinson%40mednet.ucla.edu%7Ca5dae3461d4145d0999a08d43b87ea2d%7C39c3716b64714fd5ac04a7dbaa32782b%7C0&sdata=BSvWDr41xp9VU0z2u3z7kaIdDi634EfFQDv5xrEvQ1U%3D&reserved=0" target="_blank" class="">https://www.ncbi.nlm.nih.gov/<wbr class="">pubmed/25455426</a>),
and also I would like to be able to run cohernece analyses at a channel level, so wanted to re-process this data with a laplacian filter. </p>
<p class=""><br class="">
</p>
<p class="">However, I cannot apply the laplacian after ICA, as at that point I only have 25 channels. I wondered if anyone could comment on whether it would be appropriate to apply the laplacian filter either on all 128 channels, or after removing bad channels,
and then continuing with the rest of the processing pipeline detailed above?</p>
<p class=""><br class="">
</p>
<p class="">Thanks in advance!</p>
<p class=""><br class="">
</p>
<p class="">Best wishes, </p>
<p class=""><br class="">
</p>
<p class="">Abby </p>
<p class=""><br class="">
</p>
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<div dir="ltr" class="">Makoto Miyakoshi<br class="">
Swartz Center for Computational Neuroscience<br class="">
Institute for Neural Computation, University of California San Diego<br class="">
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