Dear all,

In my MEG recordings (CTF machine) I face a lot of movement (~5mm for
most subjects thoughout most of the recordings and I would like to
somehow correct for it. In a previous mail that I had sent to the list,
I was advised by Vladimir to use the function meg_head_loc:


"There is a function in MEEGtools called  'Use CTF head localization'
(spm_eeg_megheadloc). You can use it to do things like rejecting
trials with excessive head movements or *outlying head locations or to
recompute the sensor locations to better fit the part of the data that
you are actually analysing.*"


In the script header it is mentioned that


"Use head localization of CTF to select/reject trials based on head
position and *(optionally) correct the sensor coordinates to correspond to
the selected trials.*"


I tried to go through the code but I could not get much of it. I can see
that it uses that continuous localization info but I don t exactly know
how. Could somebody give me a hint? Does this function somehow
re-reference the coil positions to a standard space? From the comments
in the file, this does not seem to be the case:


     % Here the idea is to put a 'sphere' or 'hypercylinder' in the
space of trial location whose
     % radius is 'threshold' and which captures as many trials as
possible. For this we first look for the point around which the
     % density of trials is maximal. We put the cylinder there and then
try to
     % optimize its position further to include more trials if possible.

     % The density is compute in PCA space of at most 3 dimensions


Some further questions:

If the algorithm tries to take into account all the trials in a file
(still not clear to me exactly how, even after reading the above
description) then I guess it makes more sense to apply the script on a
concatenated file of all sessions, rather than applying it first for
each session individually and then merge the sessions to a concatenated
file, right?

I compared the D.sensors('meg').pnt and D.sensors('meg').ori fields in
both cases and they are different. (I guess these fields represent only
the initial coil positions, right?

Another thing that I am not sure of, is if and how the inversion
algorithms take advantage of the continuous CTF localization info. Do
they only take into account the initial coil positions? Wouldn't it make
more sense to calculate a mean value for all the coil position within
each trial? Is a mean position value used when only the mean of a
condition is used for inversion?


And finally a silly and naive question, please correct me if I am wrong,
I guess the sensor position correction is only useful for source
localization, i.e. even after applying it it is still not sensible to
draw comparisons between subjects in the sensor space as it is only the
positions of the channels and not the waveforms them selves that are
affected. In any case, could the meeg_headloc algorithm be used to
normalize the coil positions across subjects? Is there a way to bring
all of them to a normal "coil position" space so that analysis in the
sensor space would make sense?

Any extra information on the meg_head_loc script is highly appreciated!

Thank you very much again,
Panagiotis