<div dir="ltr"><div><div><div>Hi Jumana and others,<br><br></div>To be clear, my question isn't about implementation (I know it is relatively easy to calculate ICA weights from one dataset and apply them to another). My question was about how this effects the data.<br><br></div>If I calculate the ICA weights using data filtered at 1 Hz, by definition this solution will <i>not</i> give maximally independent components when it is applied to 0.1 Hz filtered data. In the 0.1 Hz filtered data there is low frequency information in the data that ICA was not trained on and therefore doesn't "know" anything about. That low frequency information must, mathematically, end up somewhere in the IC activations when I multiply the unmixing matrix with the 0.1 Hz filtered data. What are the consequences and pitfalls of this? Is it something to worry about? Under what circumstances?<br><br>My guess is that the low frequency information gets divided across the ICs in a somewhat a priori unpredictable way depending on the scalp distribution of the low frequency information and the scalp distribution of the ICs. If I then remove one of the ICs, I may remove some of this low frequency information with it. If so:<br><ol><li>If the low frequency information in question is purely noise, this could lead to some pattern of noise/artifact in the data that is hard to interpret or move noise to electrodes that didn't originally include it.</li><li>One of the reasons for using the 0.1 Hz filter is that part of the effects I am interested in (e.g., later ERP components) contain information below 1 Hz (see Tanner et al., 2015). Can I be confident that ICA does a good job of isolating artifact from neural activity of interest if part of that activity of interest was not present in the training dataset?</li></ol><br></div><div>Have these issues been addressed anywhere in the literature or does anyone have recommendations?<br></div><div><br></div><div>Eric<br></div><div class="gmail_extra"><br clear="all"><div><div class="m_-3505749243944693018gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><span>-----<br>Eric Fields, Ph.D.<br>Postdoctoral Fellow<br><a href="https://www2.bc.edu/elizabeth-kensinger/" target="_blank">Cognitive and Affective Neuroscience Laboratory</a>, Boston College<br><a href="http://www.brandeis.edu/gutchess/" target="_blank">Aging, Culture, and Cognition Laboratory</a>, Brandeis University<br><a href="mailto:eric.fields@bc.edu" target="_blank">eric.fields@bc.edu</a></span></div></div></div></div></div></div></div>
<br><div class="gmail_quote">On Wed, Feb 21, 2018 at 1:00 PM, Ahmad, Jumana <span dir="ltr"><<a href="mailto:jumana.ahmad@kcl.ac.uk" target="_blank">jumana.ahmad@kcl.ac.uk</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
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<p style="margin-top:0;margin-bottom:0">It depends if you want to examine component activity below 1Hz. Most artifacts of interest, such as blinks and saccades should be higher frequency etc. </p><span>
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<div id="m_-3505749243944693018m_2974847851183948910divRplyFwdMsg" dir="ltr"><font style="font-size:11pt" face="Calibri, sans-serif" color="#000000"><b>From:</b> eeglablist <<a href="mailto:eeglablist-bounces@sccn.ucsd.edu" target="_blank">eeglablist-bounces@sccn.ucsd.<wbr>edu</a>> on behalf of Eric Fields <<a href="mailto:eric.fields@bc.edu" target="_blank">eric.fields@bc.edu</a>><br>
<b>Sent:</b> 21 February 2018 03:45:30<br>
<b>To:</b> EEGLAB List<br>
<b>Subject:</b> [Eeglablist] Baselining and filtering for ICA with epoched data</font>
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<div dir="ltr">Hi,<br>
<br>
I know there have been other threads related to this, so I apologize if this has been addressed directly and I missed it.<br>
<br>
Groppe et al. (2009) showed that ICA gives more reliable results if you use the full epoch instead of the prestimulus period to baseline. The reason generally given for this is that baseline correction changes the scalp distribution of sources depending on
what is happening in the baseline period. By this logic, using the full epoch should improve ICA (because longer periods are less affected by random variations), but no baseline correction at all should be even better.<br>
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Meanwhile, Winkler et al. (2015) have suggested that ICA works best on data high pass filtered at 1-2 Hz.<br>
<br>
Assuming I prefer to use a 0.1 Hz high pass filter (because of distortions 1 Hz filters can cause in the ERP: Tanner et al., 2015), I have two questions:<br>
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<ol>
<li>Does the removal of additional low frequency noise you get from using a full epoch baseline (vs no baseline) outweigh the downsides of baseline correction for ICA?</li><li>Alternatively, is it appropriate to apply a 1 or 2 Hz filter to the data used for ICA training, and then apply the ICA solution to an EEGset filtered at 0.1 Hz? Winkler et al. suggest this, but what happens to the low frequency information in the data when
the ICA solution that has been learned without it is applied? Can this cause problems?<br>
</li></ol>
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Thanks!<br>
<br>
Eric<br>
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<div dir="ltr"><span>-----<br>
Eric Fields, Ph.D.<br>
Postdoctoral Fellow<br>
<a href="https://emea01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww2.bc.edu%2Felizabeth-kensinger%2F&data=01%7C01%7Cjumana.ahmad%40kcl.ac.uk%7C60790510144d4d17f8f608d579548d03%7C8370cf1416f34c16b83c724071654356%7C0&sdata=Vmoa8t0Q6S95V85lnDxKRWHj4Wt%2B2Pw87O2v5nSZE%2BM%3D&reserved=0" target="_blank">Cognitive
and Affective Neuroscience Laboratory</a>, Boston College<br>
<a href="https://emea01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.brandeis.edu%2Fgutchess%2F&data=01%7C01%7Cjumana.ahmad%40kcl.ac.uk%7C60790510144d4d17f8f608d579548d03%7C8370cf1416f34c16b83c724071654356%7C0&sdata=KkZ5TQyb7BlOduU%2BXU66q2I5qJJXDvjk%2BPguBy3veQI%3D&reserved=0" target="_blank">Aging,
Culture, and Cognition Laboratory</a>, Brandeis University<br>
<a href="mailto:eric.fields@bc.edu" target="_blank">eric.fields@bc.edu</a></span></div>
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