<div dir="ltr">Dear Raquel,<div><br></div><div><div>> 1.-</div><div>I want to mark channels as bad, and thereby exclude them from the average reference first. Then run ICA, remove components, and only then interpolate the removed channel(s). Does this make sense? In eeglab, once I remove a channel with edit > select data > channel range (remove), I cannot find a way to interpolate it anymore. Is there a way around this issue?</div><div><br></div><div>I updated channel rejection and average reference parts in this wiki page.</div><div><a href="https://sccn.ucsd.edu/wiki/Makoto's_preprocessing_pipeline">https://sccn.ucsd.edu/wiki/Makoto's_preprocessing_pipeline</a><br></div><div><pre style="font-family:monospace,courier;padding:1em;border:1px dashed rgb(47,111,171);color:rgb(0,0,0);line-height:1.1em;font-size:12.7px;background-color:rgb(249,249,249)"> % Keep original EEG.
originalEEG = EEG;</pre></div><div><pre style="font-family:monospace,courier;padding:1em;border:1px dashed rgb(47,111,171);color:rgb(0,0,0);line-height:1.1em;font-size:12.7px;background-color:rgb(249,249,249)"> % Interpolate channels.
EEG = pop_interp(EEG, originalEEG.chanlocs, 'spherical');</pre></div><div>> 2.A<br></div><div>I have 64 channels + 4 externals, so I compute the average reference over the 64 channels. </div><div>Because I have an average reference, the rank of the data goes down by 1, so I use EEG = pop_runica(EEG , 'extended',1,'interupt','on','<b><wbr>pca',67</b>); </div><div>to reduce the number of ICs to match the rank. Is this right?</div><div><br></div><div>Correct.</div><div><br></div><div>> 2.B</div><div>Now, if I eliminate, say, 1 channel, I would have to reduce the rank by one more, right? But, what if I <i>did </i>interpolate before running the ICA, I would still have to reduce the number of IC's to 66, is that correct?</div></div><div><br></div><div>No, if you remove a channel when your data rank is numberOfChannels-1, your data are full rank again and no longer need to perform pca dimension reduction.</div><div><br></div><div>Data rank is a funny thing because it is not explicitly visible, right?</div><div>See also a snippet from that wiki page. Basically, you can ask what data rank is by running Matlab function rank() IF your data is 'double' (you can change it from EEGLAB option, default is 'single' so be careful).</div><div><pre style="font-family:monospace,courier;padding:1em;border:1px dashed rgb(47,111,171);color:rgb(0,0,0);line-height:1.1em;font-size:12.7px;background-color:rgb(249,249,249)"> % Discard channels to make the data full ranked.
dataRank = rank(EEG.data');
channelSubset = loc_subsets(EEG.chanlocs, dataRank);
EEG = pop_select( EEG,'channel', channelSubset{1});
EEG = pop_chanedit(EEG, 'eval','chans = pop_chancenter( chans, [],[]);');</pre></div><div><br></div><div>Makoto</div><div><br></div><div><br></div><div class="gmail_extra"><br><div class="gmail_quote">On Wed, Sep 7, 2016 at 10:41 AM, Raquel London <span dir="ltr"><<a href="mailto:raquel@dragondelapatagonia.com" target="_blank">raquel@dragondelapatagonia.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><div class="gmail-m_-1377286258785937092gmail_signature"><div dir="ltr"><div>Hi all,</div><div><br></div><div>I have a few questions about how to handle bad channels & ICA in eeglab.</div><div><br></div><div>1.-</div><div>I want to mark channels as bad, and thereby exclude them from the average reference first. Then run ICA, remove components, and only then interpolate the removed channel(s). Does this make sense?</div><div>In eeglab, once I remove a channel with edit > select data > channel range (remove), I cannot find a way to interpolate it anymore. Is there a way around this issue?<br></div><div><br></div><div>2.A<br></div><div>I have 64 channels + 4 externals, so I compute the average reference over the 64 channels. </div><div>Because I have an average reference, the rank of the data goes down by 1, so I use EEG = pop_runica(EEG , 'extended',1,'interupt','on','<b><wbr>pca',67</b>); </div><div>to reduce the number of ICs to match the rank. Is this right?</div><div><br></div><div>2.B</div><div>Now, if I eliminate, say, 1 channel, I would have to reduce the rank by one more, right? But, what if I <i>did </i>interpolate before running the ICA, I would still have to reduce the number of IC's to 66, is that correct?</div><div><br></div><div>Thanks so much in advance for your comments!</div><span class="gmail-HOEnZb"><font color="#888888"><div><br></div><div>Raquel</div><div><br></div><div><br></div></font></span></div></div></div>
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