Thanks a lot for your help!<div><br></div><div>Davide.<br><br><div class="gmail_quote">On Wed, Sep 26, 2012 at 10:09 PM, Tarik S Bel-Bahar <span dir="ltr"><<a href="mailto:tarikbelbahar@gmail.com" target="_blank">tarikbelbahar@gmail.com</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">My apologies. Sorry to have not been more clear. <div>It is my (simple) understanding that ICA <b>does not care </b></div>
<div><div>whether the data is epoched or continuous.<br></div>In other words, to ICA, the data looks the same whether it is epoched or continuous. </div>
<div><div>ICA is basically trying to find spatial patterns/filters that best explain the time points you give it. </div><div><br></div><div>That being said, there are multiple rules you need to learn about and keep in mind</div>
<div>before "feeding" data to ICA. The data needs to contain enough time points,</div><div>be clean enough, and contain only data from the actual task </div><div>(that is, no periods where "nothing was going on" between experimental blocks).<br>
<br>You may want to do some reading on ICA/EEG fundamentals.<br>For example, search on Google Scholar for these two items:<br><br>1. ERP features and EEG dynamics: an ICA perspective<br>S Makeig, J Onton - Oxford Handbook of Event-Related Potential …, 2011 - <br>
<br></div><div>2. Independent EEG sources are dipolar<br>A Delorme, J Palmer, J Onton, R Oostenveld, S Makeig - PloS one, 2012 - <a href="http://dx.plos.org" target="_blank">dx.plos.org</a><br><br><b>Please also take the time to search the eeglablist archives and</b><div>
<b>inform yourself of past conversations. Search on the "keywords" </b></div><div><b>we have been using, such as epoched, continuous, ICA....</b></div><div>For example, I searched on Google for "ICA continuous or epoched"</div>
<div>and found a few eeglablist conversations, and other pages that</div><div>might help you develop a deeper view of what is going on, </div><div>including the following from the EEGLAB wiki, </div><div>which should be read from start to finish by any new eeglab users.</div>
<div><b><br></b></div><div><b><span style="color:rgb(34,34,34);line-height:15px;font-family:arial,sans-serif;font-weight:normal"><h3 style="font-size:medium;font-weight:normal;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;display:block;overflow-x:hidden;overflow-y:hidden;text-overflow:ellipsis;white-space:nowrap">
<a href="http://sccn.ucsd.edu/wiki/Chapter_09:_Decomposing_Data_Using_ICA" style="color:rgb(17,34,204)" target="_blank">Chapter 09: Decomposing Data Using <em style="font-weight:bold;font-style:normal">ICA</em> - SCCN</a></h3>
<div style="max-width:42em;color:rgb(34,34,34)">
<div style="color:rgb(102,102,102);display:block;margin-bottom:1px"><cite style="color:rgb(0,153,51);font-style:normal;display:inline-block;margin-bottom:1px"><a href="http://sccn.ucsd.edu/wiki/Chapter_09:_Decomposing_Data_Using_" target="_blank">sccn.ucsd.edu/wiki/Chapter_09:_Decomposing_Data_Using_</a><b>ICA</b></cite></div>
</div></span></b></div><div><br></div><div>The advice from Stephen and others is also important to keep in mind.<br><br></div><div><div><div><br></div><div><br></div><div><br></div><div><br></div><div><br></div>
<div><br></div><div>
<br></div><div><br></div><div><br><br><br><br>On Wed, Sep 26, 2012 at 1:04 AM, Davide Baldo <<a href="mailto:davidebaldo84@gmail.com" target="_blank">davidebaldo84@gmail.com</a>> wrote:<br>><br>> Thanks you all for your answer.<br>
><br>> Now I am wondering what does that mean tha ICA is agnostic about continuous/discontinuous data. Could you please explain that concept a bit? I am very interested in the topic.<br>><br>> Thanks again!<br>
><br>> Davide.<br>><br>><br>> On Wed, Sep 26, 2012 at 1:17 AM, Tarik S Bel-Bahar <<a href="mailto:tarikbelbahar@gmail.com" target="_blank">tarikbelbahar@gmail.com</a>> wrote:<br>>><br>>> yes, periods that are not of cognitive interest should be removed,<br>
>> and ICA is generally agnostic about <br>>> whether the data is epoched or continuous.<br>>> yes, there have been various conversations on this topic in the list.<br>>><br>>> On Fri, Sep 21, 2012 at 4:29 AM, Davide Baldo <<a href="mailto:davidebaldo84@gmail.com" target="_blank">davidebaldo84@gmail.com</a>> wrote:<br>
>>><br>>>> Dear all,<br>>>><br>>>> I was wondering about the following problem:<br>>>><br>>>> Assume that your experiment has 2 blocks each with 30 Trials (just a random numbers). The experiment is divided into 2 blocks in order to give a break to the subject at the end of the first block of 30 trials.<br>
>>> The EEG data during the break will probably be full of any kinds of artifacts.<br>>>><br>>>> My question is the following: Regarding ICA, what do you do with the EEG data recording during the break time? I mean, do you use the complete EEG signal to run ICA (including the data recorded during the break time) or do you remove the noise data recording during the break?<br>
>>><br>>>> The point is: if you do not remove that data, then you have a continuous signal as input to ICA. <br>>>> If you do remove that data, you remove a lot of noise, but you add a discontinuity in the data you use to run ICA (because you must cut the data recorded during the break and join together the remaining data)<br>
>>><br>>>> Thus...is it better to remove that part of the data or not?<br>>>><br>>>> Does my question make sense? :) <br>>>><br>>>> Thanks in advance for your kind help,<br>
>>><br>>>><br>>>> Davide.<br>>>><br>>>> _______________________________________________<br>>>> Eeglablist page: <a href="http://sccn.ucsd.edu/eeglab/eeglabmail.html" target="_blank">http://sccn.ucsd.edu/eeglab/eeglabmail.html</a><br>
>>> To unsubscribe, send an empty email to <a href="mailto:eeglablist-unsubscribe@sccn.ucsd.edu" target="_blank">eeglablist-unsubscribe@sccn.ucsd.edu</a><br>>>> For digest mode, send an email with the subject "set digest mime" to <a href="mailto:eeglablist-request@sccn.ucsd.edu" target="_blank">eeglablist-request@sccn.ucsd.edu</a><br>
>><br>>><br>><br></div></div></div></div></div>
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