<div dir="ltr">Makoto, thanks for this detailed response. Best, Brian</div><div class="gmail_extra"><br><br><div class="gmail_quote">On Tue, Apr 15, 2014 at 1:02 PM, Makoto Miyakoshi <span dir="ltr"><<a href="mailto:mmiyakoshi@ucsd.edu" target="_blank">mmiyakoshi@ucsd.edu</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">Dear Brian,<div><br></div><div><div class=""><div>> Do you know if there has been a systematic investigation of this issue of combining data between sessions?</div>
<div><br></div></div><div>There is no systematic investigation as far as I know, partly because it's more like theoretically evident issue. ICA produces a static spatial filter, and if you change the relative positions between electrodes and sources then you'll need another filter, which violates the stationarity assumptions...</div>
<div class="">
<div><br></div><div>> We are very precise in our electrode placement.</div><div><br></div></div><div>But as I told you even re-gelling can screw it up... so we don't recommend re-gelling.</div><div class=""><div><br>
</div><div>> Our purpose in this study is to remove artifactual components and then return to the scalp map space - not to continue on to dipole modeling, etc. (though I intend to work on dipole modeling methods in the future).<br>
</div><div><br></div></div><div>I see.</div><div class=""><div><br></div><div>> You mention the problem of probable creation of multiple artifact ICs in the concatenation method I described. We do see e.g. multiple blink components in some resting state files I have concatenated this way, but not for others. And in fact, I often see such duplicate artifactual ICs in longer datasets that are all from one session, so it seems there are other generators of this multiple IC problem?</div>
<div><br></div></div><div>There are two issues. One is the subspace issue. For example, you may find multiple alpha, mu, EOG, and EMG components. These subspace are intradependent but inter-independent. This is a normal behavior of ICA, and actually applying ICA means applying independent subspace analysis. I'm not a specialist in this topic so if you need I can ask Jason Palmer, who wrote AMICA, to take over this query. The other issue is the cap/electrode shift by accident, lapse of time (since cap is elastic and tightened with rubber band etc...) which is a problem for ICA. </div>
<div><br></div><div><div class=""><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">> If the evidence really suggests that reapplication of the cap (multiple sessions) makes concatenation impossible,</div>
</blockquote><div><br></div></div><div>Yes, as I said it is theoretically clear.</div><div class=""><div><br></div><div> > I see a few ways I could go forward. The first is to do PCA as you mentioned. This reduces the dimensionality of the data and thus the number of time points necessary for a good decomposition.</div>
<div><br></div></div><div>Yes, although Scott Makeig seems to have changed his preference on it over this years, and now he is more reluctant to use PCA and I have never heard he recommend PCA before ICA even in seemingly appropriate situations (more optimism in minimum datapoint estimate for ICA).</div>
<div class="">
<div><br></div><div>> Another method for reducing dimensionality would be to simply down-sample. I calculate that we would achieve the recommended 30 datapoints per channel^2 necessary for good ICA decomposition if we down-sampled to 50 channels from our current 84. Could you comment on the merits of this method vs. PCA? </div>
<div><br></div></div><div>If you downsample channels, you'll have problems in recovering the scalp topos with full channels, although just discarding channels is the most straightforward solution in a sense. I would say applying PCA is better.</div>
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<div><br></div><div>> Particularly, what is the nature of any distortion caused by running PCA? Since it drops variance.</div><div><br></div></div><div>After PCA, when you recover channel signals from ICA activations you'll see different data (of course, since it's a lossy compression). Though you want to compare before and after PCA application though, since the difference should be fairly small.</div>
<div><br></div><div>I appreciate you consider this process seriously and your carefulness.</div><div><br></div><div>Makoto</div></div><div><br></div><div>2014-04-09 7:49 GMT-07:00 Erickson <span dir="ltr"><<a href="mailto:ericksonb.eng@gmail.com" target="_blank">ericksonb.eng@gmail.com</a>></span>:<br>
</div></div><div class="gmail_extra"><div class="gmail_quote"><div class=""><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">Makoto,<div><br></div><div>Thanks for your response. Do you know if there has been a systematic investigation of this issue of combining data between sessions? We are very precise in our electrode placement. Our purpose in this study is to remove artifactual components and then return to the scalp map space - not to continue on to dipole modeling, etc. (though I intend to work on dipole modeling methods in the future).</div>
<div><br></div><div>You mention the problem of probable creation of multiple artifact ICs in the concatenation method I described. We do see e.g. multiple blink components in some resting state files I have concatenated this way, but not for others. And in fact, I often see such duplicate artifactual ICs in longer datasets that are all from one session, so it seems there are other generators of this multiple IC problem?</div>
<div><br></div><div><br></div></div></blockquote></div><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><br></div><div><div class="h5"><div>Another option I see would be to split the file into two sets with 84/2 = 42 channels each (evenly distributed across the scalp). I would run ICA on both of these datasets independently, remove artifacts via ADJUST, and then recombine the corrected waveforms. Does this violate the assumptions of ICA? Again, I am only interested in removing artifacts, not dipole modeling etc. at this time. I appreciate your comments!</div>
<div><br></div><div>- Brian</div><div><div>
<div><br></div><div><br></div><div class="gmail_extra"><br><br><div class="gmail_quote">On Fri, Apr 4, 2014 at 10:36 PM, Makoto Miyakoshi <span dir="ltr"><<a href="mailto:mmiyakoshi@ucsd.edu" target="_blank">mmiyakoshi@ucsd.edu</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">Dear Erickson,<div><div><br></div><div>> However, since the setup is identical between sessions, we are merging the datasets from each subject's 4 sessions into a large dataset,<br>
</div><div class="gmail_extra">
<br></div></div><div class="gmail_extra">Sorry to point this out, but you can't do this because electrode cap applications were different for each of 4 recordings, right?</div><div class="gmail_extra"><br></div><div class="gmail_extra">
Even a re-gelling can move a channel and create 'another' IC which shows 'blocked' ERPimages...</div><div class="gmail_extra"><br></div><div class="gmail_extra">Run ICA separately for each of 4 recording sessions. If you don't have enough datapoints, you can run pca to reduce dimensions. For infomax, after 'extended', 1, continue 'pca', 20 for example.</div>
<div class="gmail_extra"><br></div><div class="gmail_extra">Makoto</div><div class="gmail_extra"><br><div class="gmail_quote">2014-04-03 13:02 GMT-07:00 Erickson <span dir="ltr"><<a href="mailto:ericksonb.eng@gmail.com" target="_blank">ericksonb.eng@gmail.com</a>></span>:<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><div><div dir="ltr">List,<div>I am creating a data pipeline to process resting state eeg with ADJUST, and I've run into a conceptual problem with bad channels.</div>
<div><br></div><div>Our study involved the collection of resting state data across several days. Individually these resting state files are not long enough to meet the data requirements of ICA (datapoints/channels^2 > 30 or 40). However, since the setup is identical between sessions, we are merging the datasets from each subject's 4 sessions into a large dataset, running ICA on this dataset, and then applying the ICA weights back to the 4 individual datasets. We have no reason to believe that the EEG signature of a blink would be any different between sessions, nor is the cognitive task different (resting state) so this merge seems to be a nice way to take care of the problem.</div>
<div><br></div><div>However, my issue is that if there is a bad channel in one of these 4 datasets, and I remove it, the dimensionality of the datasets is different and they can't be merged, much less used for ICA. Normally I would interpolate to get those channels back, but it's not correct to interpolate before ICA.</div>
<div><br></div><div>Currently, my solution is just to accept the loss of data. If a channel is bad in any of the 4 original datasets, I have to remove it from all 4 original datasets. Then I can merge them and run ICA on the merged file. Then I apply those ICA weights to the 4 original datasets individually and run ADJUST. However, I'm obviously throwing away a lot of data here so I would like to know if there is a better way.</div>
<div><br></div><div>Can anyone suggest an option I am not thinking of to solve this issue?</div><div><br></div><div>Thanks for your time! - Brian</div></div>
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-- <br><div dir="ltr">Makoto Miyakoshi<br>Swartz Center for Computational Neuroscience<br>Institute for Neural Computation, University of California San Diego<br></div>
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</blockquote></div><div><div class="h5"><br><br clear="all"><div><br></div>-- <br><div dir="ltr">Makoto Miyakoshi<br>Swartz Center for Computational Neuroscience<br>Institute for Neural Computation, University of California San Diego<br>
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