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<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D">Thanks a lot for your answers.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D">I wanted just to make sure that I can apply the weights calculated on a shorter dataset to a longer dataset.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D">And also to make sure that it is better to apply this procedure than to apply the ICA on the longer dataset with the noise.
<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D">Best wishes<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D">Sara<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D"><o:p> </o:p></span></p>
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<p class="MsoNormal"><b><span lang="EN-US" style="font-size:10.0pt;font-family:"Tahoma","sans-serif"">From:</span></b><span lang="EN-US" style="font-size:10.0pt;font-family:"Tahoma","sans-serif""> Tarik S Bel-Bahar [mailto:tarikbelbahar@gmail.com]
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<b>Sent:</b> 18 September 2012 04:03<br>
<b>To:</b> Sara Graziadio<br>
<b>Cc:</b> eeglablist@sccn.ucsd.edu<br>
<b>Subject:</b> Re: [Eeglablist] ICA question<o:p></o:p></span></p>
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<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">A good strategy is to try to clean your data as much<o:p></o:p></p>
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<p class="MsoNormal">as possible of extreme artifacts <o:p></o:p></p>
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<p class="MsoNormal">(these extreme artifacts would <o:p></o:p></p>
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<p class="MsoNormal">attract ICA's attention too much).<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">Then, if you have enough time points in your data<o:p></o:p></p>
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<p class="MsoNormal">to match ICA's requisites, you should be able<o:p></o:p></p>
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<p class="MsoNormal">to achieve a clean and interpretable ICA decomposition.<o:p></o:p></p>
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<p class="MsoNormal">With ICs that look interpretable in relation to known<o:p></o:p></p>
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<p class="MsoNormal">EEG "components" expected in your protocol.<o:p></o:p></p>
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<p class="MsoNormal">You may also find "real" ICs that you did not expect.<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">The resultant ICA information can then be <o:p></o:p></p>
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<p class="MsoNormal">transferred to a longer continuous file,<o:p></o:p></p>
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<p class="MsoNormal">as you have done. You will however still<o:p></o:p></p>
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<p class="MsoNormal">have to deal with artifactual time periods <o:p></o:p></p>
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<p class="MsoNormal">and artifactual epochs, one way or another.<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">ICA does not care if you give it epochs or continuous data. However it is important that you feed it <o:p></o:p></p>
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<p class="MsoNormal">enough good clean data, data during which <o:p></o:p></p>
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<p class="MsoNormal">the cognitive behavior you are interested in <o:p></o:p></p>
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<p class="MsoNormal">is occurring. In addition to eliminating <o:p></o:p></p>
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<p class="MsoNormal">"artifactual periods" you may also <o:p></o:p></p>
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<p class="MsoNormal">want to eliminate all periods that are not<o:p></o:p></p>
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<p class="MsoNormal">"cognitively"-relevant or "task"-relevant.<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">Good luck with your process and let the list know how <o:p></o:p></p>
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<p class="MsoNormal">things go for you.<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal" style="margin-bottom:12.0pt"><o:p> </o:p></p>
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<p class="MsoNormal">On Mon, Sep 17, 2012 at 4:26 AM, Sara Graziadio <<a href="mailto:sara.graziadio@newcastle.ac.uk" target="_blank">sara.graziadio@newcastle.ac.uk</a>> wrote:<o:p></o:p></p>
<p class="MsoNormal">Hello eeglab users,<br>
I have a question about ICA. My data have some noise in some time intervals. I want to remove the noise before using the ICA but I want to have the whole dataset (continuous data, not epoched) to run some more analysis once the data are cleaned with the ICA.
Is there a way to do this?<br>
At the moment I am running the ICA on the dataset without the noise and then I am applying the ICA weights calculated on the short dataset to the whole dataset (with the noise). Do you think I can do this? Or is there any better method to obtain the whole dataset
cleaned without decreasing the ICA performance?<br>
Thank you<br>
<br>
Best wishes<br>
<br>
Sara<br>
<br>
Sara Graziadio, PhD<br>
Research Associate<br>
Institute of Neuroscience<br>
Newcastle University<br>
<br>
Address:<br>
Sir James Spence Institute<br>
Royal Victoria Infirmary<br>
Queen Victoria road, NE1 4LP<br>
Newcastle upon Tyne, UK<br>
<br>
Tel: <a href="tel:%2B44%20%280%29191%20282%201377">+44 (0)191 282 1377</a><br>
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