[Eeglablist] Separating out Alpha from mu oscillations

Jason Palmer japalmer29 at gmail.com
Tue Mar 25 15:26:26 PDT 2014

Hi Bala,


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.


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.


Using ICA to separate and compare mu components across subjects, say
relative mu desynchronization times is a good idea in my opinion.





From: eeglablist-bounces at sccn.ucsd.edu
[mailto:eeglablist-bounces at sccn.ucsd.edu] On Behalf Of B L
Sent: Tuesday, March 25, 2014 2:41 PM
To: Chuck Telestel
Cc: eeglablist at sccn.ucsd.edu
Subject: Re: [Eeglablist] Separating out Alpha from mu oscillations


Hi Matthew(and others),


Thanks for your suggestions.

Just few clarifications. So I assume the CSD(& Hjorth transform) method you
are suggesting is to remove the effect of volume conduction, right?

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.

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?


Thanks again,





On Tue, Mar 25, 2014 at 2:47 PM, Chuck Telestel
<sunshineafterdusk at gmail.com> wrote:



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). 


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.


There are a couple of ways, but the above gives decent results. Good luck.



On 25 Mar 2014, at 3:58 PM, B L <thirstyforknowledge123 at gmail.com> wrote:



I guess this topic has been discussed in the past but I cant find an
effective solution.


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?

Will ICA work better for this(just identifying the alpha component and
removing it)? Is there any other method researchers commonly use for this

Any help is much appreciated.




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