using reference gradiometers with preproc_denoise

[jan-mathijs schoffelen]

Dear Mark,

preproc_denoise has been specifically designed to remove the very high
amplitude coil signals generated by the 4D system when continuous head
localization is switched on. It is regressing out the signals on the
reference coils on a trial by trial basis. The signal to noise of this
artifact is huge (in fact, you don't see any brain signal when the
coils are switched on), and as a consequence of this the regression
coefficients are relatively stable across trials. In order to remove
lower amplitude environmental noise, a single trial estimate of the
regression coefficients is probably not what you want. The 4D-software
comes with the command line utitilties cfw and afw, which computes a
set of balancing coefficients given some input data (typically across
a whole dataset). Did you try to use this? I have a matlab
implementation which achieves the same thing, but it is not yet part
of general FieldTrip.

Best,

Jan-Mathijs


On Nov 4, 2010, at 7:11 PM, Mark Drakesmith wrote:

> Hi
>
> I am just looking over some MEG data and was wondering what is the
> best way of dealing with noise. the 4D MEG scanner includes 11
> reference coils for detecting noise within the MSR. What is the best
> way of using this for data-cleaning? I am currently using
> preproc_denoise to do this, but the description suggests it should
> be used when continuous head motion channels are used, which is not
> the case here. Is it appropriate to use preproc_denoise or should I
> be using an ICA-type approach?
>
> Any help clearing up this confusion would be greatly appreciated.
>
> Regards
>
> Mark
>
> --
>
> Mark Drakesmith
> PhD Student
>
> Neuroscience and Aphasia Research Unit (NARU)
> University of Manchester
>
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Dr. J.M. (Jan-Mathijs) Schoffelen
Donders Institute for Brain, Cognition and Behaviour,
Centre for Cognitive Neuroimaging,
Radboud University Nijmegen, The Netherlands
J.Schoffelen at donders.ru.nl
Telephone: 0031-24-3614793

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and to discuss  new ideas for MEG and EEG analysis.
See also http://listserv.surfnet.nl/archives/fieldtrip.html
and http://www.ru.nl/neuroimaging/fieldtrip.
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