[FieldTrip] LCMV and SAM

Johanna Zumer johanna.zumer at donders.ru.nl
Fri Jul 22 08:26:04 CEST 2011

Hi all,

As of June 6, 2011 'beamformer_sam' was put back into the Fieldtrip toolbox
(after it had gone missing for some time), although, a warning that it has
been awhile since the code was tested.

I think there are other differences than just the orientation selection
(between the original-CTF SAM and LCMV in FT), although that is a large part
of it.
Note the 'fixedori' option in both FT functions (beamformer_sam and
beamformer_lcmv).   If set to 'yes' in beamformer_lcmv, it will give a
scalar output (similar, maybe equivalent, to fixedori='robert' output in
beamformer_sam), both which use SVD to find the orientation. (Default for
'fixedori' in beamformer_lcmv is 'no' so will give a vector output by
default).  The default option for 'fixedori' in beamformer_sam is 'spinning'
which is the method described by Jed, and I think is what matches the CTF


On 21 July 2011 23:21, Jed Meltzer <jedmeltzer at yahoo.com> wrote:

> Without getting into software implementation questions (basically fieldtrip
> has LCMV, and CTF software has SAM) I think the main difference is that LCMV
> is a "vector" beamformer.  At any given location, it estimates a virtual
> signal in all three spatial dimensions (or only two if you constrain it to
> tangential sources, which makes sense for MEG but not for EEG). So you have
> three virtual signals at each point, and how you combine them is up to you -
> take the biggest one, or the magnitude of the whole vector, or any other
> combination.  I'm not sure what the default for power mapping is in
> fieldtrip (I have mainly used SAM for beamforming so far, but I use
> fieldtrip for other purposes).
> SAM has an extra step involved where it "optimizes" a dipole orientation at
> each location to maximize the signal, so you only get one signal at each
> location.  In this sense it's nonlinear.  The calculation is more complex
> due to the optimization, but the result is simpler to deal with.  This is
> called a "scalar" beamformer.  For pros and cons, you might look up papers
> on vector vs. scalar beamformers in general.  Here's one recent one that I
> saw that compared them and has further references:
> Quraan, M. A., S. N. Moses, et al. (2011). "Detection and localization of
> hippocampal activity using beamformers with MEG: a detailed investigation
> using simulations and empirical data." Hum Brain Mapp *32*(5): 812-827.
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