[Eeglablist] ICA on combined M/EEG

Makoto Miyakoshi mmiyakoshi at ucsd.edu
Tue Dec 1 11:58:15 PST 2015


Dear John and Scott,

Here is a question for your paper. Could you kindly answer when you have
time?

Makoto

On Mon, Nov 30, 2015 at 5:31 AM, Tatu Huovilainen <
tatu.huovilainen at helsinki.fi> wrote:

> Hello eeglab users,
>
> I have a question regarding ICA on combined M/EEG data, and as John
> Iversen and Scott Makeig have written a chapter 'MEG/EEG Data Analysis
> Using EEGLAB' for the book Magnetoencephalography: From Signals to Dynamic
> Cortical Networks which deals with almost exactly the same sort of data, I
> feel this is the right place to ask.
> The data I'm working with is around an hour of 60 channel unipolar EEG and
> 306 channel MEG (Elekta Neuromag, 204 planar gradiometer sensors and 102
> magnetometer sensors) per subject.
>
> As is explained in the above mentioned chapter, before ICA the data from
> different sensor types needs to be sphered individually, so in this case
> separately for EEG, gradiometer and magnetometer sensors. Things would go
> as in the chapter, but with Neuromag preprocessing the MEG data rank is
> reduced because of tSSS method (Taulu & Simola, 2006) and further because
> of continuous head movement correction so that after preprocessing the rank
> of the MEG part is around 70. So before ICA a PCA reduction is also
> necessary. So the fist question is at which order should these steps be
> applied? Do I sphere the gradiometer and magnetometer datas separately,
> concatenate them, do PCA (with the component count determined by rank())
> and then concatenate the sphered EEG data and perform ICA? Secondly, how
> then are the projections to different sensors calculated from the
> IC-weights?
>
> Best regards,
> Tatu
>
> Taulu, S. & Simola, J. (2006). Spatiotemporal signal space separation
> method for rejecting nearby interference in meg measurements. Physics in
> Medicine and Biology, 51,1759–1768.
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-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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