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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='font-size:11.0pt;font-family:"Calibri","sans-serif";color:black'>Hi Bala,<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:black'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:black'>Responding to your original question as I interpreted it … yes, in my experience, it is often straightforward with ICA to just sort out the mu components by taking the ones with (often matching contralateral) maps in the central motor region.<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:black'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:black'>Motor mu ICs can be further identified as descynchronizing (i.e. going away) before motor response. And occipetal and occipito-frontal alpha ICs can typically be clearly distinguished by location and power.<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:black'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:black'>Using ICA to separate and compare mu components across subjects, say relative mu desynchronization times is a good idea in my opinion.<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:black'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:black'>Best,<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:black'>Jason<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:black'><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"'> eeglablist-bounces@sccn.ucsd.edu [mailto:eeglablist-bounces@sccn.ucsd.edu] <b>On Behalf Of </b>B L<br><b>Sent:</b> Tuesday, March 25, 2014 2:41 PM<br><b>To:</b> Chuck Telestel<br><b>Cc:</b> eeglablist@sccn.ucsd.edu<br><b>Subject:</b> Re: [Eeglablist] Separating out Alpha from mu oscillations<o:p></o:p></span></p><p class=MsoNormal><o:p> </o:p></p><div><p class=MsoNormal>Hi Matthew(and others),<o:p></o:p></p><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal>Thanks for your suggestions.<o:p></o:p></p></div><div><p class=MsoNormal>Just few clarifications. So I assume the CSD(& Hjorth transform) method you are suggesting is to remove the effect of volume conduction, right?<o:p></o:p></p></div><div><p class=MsoNormal>And yes, I always baseline correct the data using pre-stim data(with no movements) as baseline in time and frequency(while calculating ERSP) domain. <o:p></o:p></p></div><div><p class=MsoNormal>So if I perform the above two steps, can I be sure that the effect of alpha is very much minimized in the central region? In other words, can I simply ignore the effect of alpha when I compare central regions in the alpha freq. band(7-13hz or 8-13hz) between 2 groups of interest?<o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal>Thanks again,<o:p></o:p></p></div><div><p class=MsoNormal>Bala<o:p></o:p></p><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div></div></div><div><p class=MsoNormal style='margin-bottom:12.0pt'><o:p> </o:p></p><div><p class=MsoNormal>On Tue, Mar 25, 2014 at 2:47 PM, Chuck Telestel <<a href="mailto:sunshineafterdusk@gmail.com" target="_blank">sunshineafterdusk@gmail.com</a>> wrote:<o:p></o:p></p><div><div><p class=MsoNormal>Hi, <o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal>One way, not the only way, would be to use current source densities or apply a Hjorth transform to the data. The effect is that more global alpha rhythms won't mask your mu rhythms. Use the electrodes over the motor regions contra-lateral to movement (C3 & C4: 10/20 system). <o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal>It also a good idea to have pre-movement data for a baseline correction. You could use time-domain and frequency domain representations. Your mu-desychronisation will be clearly visible in the upper alpha band (approx. 10-12hz). Bear in mind that the alpha band varies across individuals so not everyones upper alpha will neatly fall into this band.<o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal>There are a couple of ways, but the above gives decent results. Good luck.<o:p></o:p></p></div><div><p class=MsoNormal><o:p> </o:p></p></div><div><p class=MsoNormal>Matthew<o:p></o:p></p></div><div><div><div><p class=MsoNormal style='margin-bottom:12.0pt'><br>On 25 Mar 2014, at 3:58 PM, B L <<a href="mailto:thirstyforknowledge123@gmail.com" target="_blank">thirstyforknowledge123@gmail.com</a>> wrote:<o:p></o:p></p></div><blockquote style='margin-top:5.0pt;margin-bottom:5.0pt'><div><div><p class=MsoNormal><span style='font-size:10.0pt;font-family:"Arial","sans-serif"'>Hi,</span><o:p></o:p></p><div><p class=MsoNormal><span style='font-size:10.0pt;font-family:"Arial","sans-serif"'><o:p> </o:p></span></p><div><p class=MsoNormal><span style='font-size:10.0pt;font-family:"Arial","sans-serif"'>I guess this topic has been discussed in the past but I cant find an effective solution.<o:p></o:p></span></p></div><div><p class=MsoNormal><span style='font-size:10.0pt;font-family:"Arial","sans-serif"'><o:p> </o:p></span></p></div><div><p class=MsoNormal><span style='font-size:10.0pt;font-family:"Arial","sans-serif"'>We know that, it is very common for the alpha oscillation to have influence up to almost the frontal electrodes. My question is - If we are interested only in mu rhythm in a visual-motor task, what is the best way to tease out the alpha activity from the motor regions so the comparison across groups is more effective?<o:p></o:p></span></p></div><div><p class=MsoNormal><span style='font-size:10.0pt;font-family:"Arial","sans-serif"'>Will ICA work better for this(just identifying the alpha component and removing it)? Is there any other method researchers commonly use for this purpose?<o:p></o:p></span></p></div><div><p class=MsoNormal><span style='font-size:10.0pt;font-family:"Arial","sans-serif"'>Any help is much appreciated.<o:p></o:p></span></p></div><div><p class=MsoNormal><span style='font-size:10.0pt;font-family:"Arial","sans-serif"'><o:p> </o:p></span></p></div><div><p class=MsoNormal><span style='font-size:10.0pt;font-family:"Arial","sans-serif"'>Thanks!<o:p></o:p></span></p></div><div><p class=MsoNormal><span style='font-size:10.0pt;font-family:"Arial","sans-serif"'>Bala <o:p></o:p></span></p></div></div></div></div></blockquote></div></div><blockquote style='margin-top:5.0pt;margin-bottom:5.0pt'><div><p class=MsoNormal>_______________________________________________<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><o:p></o:p></p></div></blockquote></div></div><p class=MsoNormal><o:p> </o:p></p></div></div></body></html>