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</o:shapelayout></xml><![endif]--></head><body lang=EN-US link=blue vlink=purple><div class=WordSection1><p class=MsoNormal><span style='color:#1F497D'>Dr. Makeig,<o:p></o:p></span></p><p class=MsoNormal><span style='color:#1F497D'><o:p> </o:p></span></p><p class=MsoNormal>You mention the following formula below to determine how much data is needed to optimally use ICA. <o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>#timepoints/(#channels)^2<o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>So, if I have the following for each condition: (data was recorded at 1,000 Hz and I have about 186 one second epochs)<o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>(1,000 time points *186 epochs)/(53 channels)^2<o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>186,000/2,<span style='color:#1F497D'>8</span>0<span style='color:#1F497D'>9</span> =<b> 6<span style='color:#1F497D'>6</span>.2<span style='color:#1F497D'>157351</span><o:p></o:p></b></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>Below you mention it needs to be much more than 1 or even as high as 30 for 256 channels. <b><u>What exactly is the cut off for optimal ICA using this formula?</u></b> I want to be able to use this formula to determine for each dataset I have, ranging up to 128 channels and recorded at different sampling rates, what is enough data to run ICA optimally.<o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>Thanks,<br>Dr. Modestino<span style='color:#1F497D'><o:p></o:p></span></p><p class=MsoNormal><span style='color:#1F497D'><o:p> </o:p></span></p><p class=MsoNormal><span style='color:#1F497D'>Edward Justin Modestino, Ph.D.<o:p></o:p></span></p><p class=MsoNormal><span style='color:#1F497D'>Postdoctoral Research Associate<o:p></o:p></span></p><p class=MsoNormal><span style='color:#1F497D'>Ray Westphal Neuroimaging Laboratory<o:p></o:p></span></p><p class=MsoNormal><span style='color:#1F497D'>Division of Perceptual Studies<o:p></o:p></span></p><p class=MsoNormal><span style='color:#1F497D'>Department of Psychiatry and Neurobehavioral Sciences <o:p></o:p></span></p><p class=MsoNormal><span style='color:#1F497D'>University of Virginia<o:p></o:p></span></p><p class=MsoNormal><span style='color:#1F497D'>Email: <a href="mailto:ejm9f@virginia.edu">ejm9f@virginia.edu</a><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><p class=MsoNormal><b><span style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'>From:</span></b><span style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'> Scott Makeig <a href="mailto:[mailto:smakeig@gmail.com]">[mailto:smakeig@gmail.com]</a> <br><b>Sent:</b> Thursday, February 23, 2012 11:34 PM<br><b>To:</b> Arnaud Delorme<br><b>Cc:</b> Modestino, Edward J *HS; <a href="mailto:eeglablist@sccn.ucsd.edu">eeglablist@sccn.ucsd.edu</a>; Kelly, Edward *HS; Ross Dunseath<br><b>Subject:</b> Re: [Eeglablist] Pipeline of processing to optimize ICA for artrifact removal<o:p></o:p></span></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal style='margin-bottom:12.0pt'>Some additional >> comments, Scott Makeig<o:p></o:p></p><div><p class=MsoNormal>On Thu, Feb 23, 2012 at 9:04 AM, Arnaud Delorme <<a href="mailto:arno@ucsd.edu">arno@ucsd.edu</a>> wrote:<o:p></o:p></p><div><p class=MsoNormal>Dear Mosdestino,<o:p></o:p></p><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal>1. Difficulties with ICA. When removing ICA components, one of the main concern is the quality of your decomposition. We are currently working on tools to assess this quality although this can be tricky because of the large inter-subject variability. In the meantime, if you have multiple components for each type of artifacts, this is usually a sign that the quality of your decomposition is poor. One of the main factor to increase quality is to increase the amount of data and also high pass filtering if you have large offset in your data channels.<o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div></div><div><p class=MsoNormal>>> Modestino, you do not mention the data length he is using. An important value is #timepoints/(#channels)^2 ... this should be much more than 1 (even as high as 30 for 256 channels, in our (limited) experience -- I hope we will run a numerical experiment on this soon... Running ICA on insufficient length data is the most common problem in applying ICA successfully.<br> <o:p></o:p></p></div></div><p class=MsoNormal><o:p> </o:p></p></div></body></html>