<html><head><meta http-equiv="content-type" content="text/html; charset=utf-8"></head><body dir="auto"><div>Hi, </div><div><br></div><div>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). </div><div><br></div><div>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.</div><div><br></div><div>There are a couple of ways, but the above gives decent results. Good luck.</div><div><br></div><div>Matthew</div><div><br>On 25 Mar 2014, at 3:58 PM, B L <<a href="mailto:thirstyforknowledge123@gmail.com">thirstyforknowledge123@gmail.com</a>> wrote:<br><br></div><blockquote type="cite"><div><div dir="ltr"><span style="font-family:arial,sans-serif;font-size:13px">Hi,</span><div style="font-family:arial,sans-serif;font-size:13px"><br><div>I guess this topic has been discussed in the past but I cant find an effective solution.</div>
<div><br></div><div>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?</div>
<div>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?</div><div>Any help is much appreciated.</div><div><br></div><div>
Thanks!</div><div>Bala </div></div></div>
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