[FieldTrip] Beamformers: SAM vs LCMV vs LCMV fixedori

[Max Cantor]

Hi Fieldtrip,

We are currently using the SAM beamformer for source localization, but are
thinking of switching to LCMV. Given the research I've read, the vector
beamformer approach should, for our purposes, be more efficient and be as,
if not more accurate than scalar.

However, other than the vector/scalar difference, I don't have a great
understanding of what other differences exist between the two beamformers.
To test the differences, I've run SAM, LCMV, and LCMV with fixed
orientation (making it scalar), with both our real data and with simulated
data, and while SAM and LCMV fixedori are more similar to each other than
either are when compared to LCMV without fixedori (particularly with the
simulation, less so with our real data), they are still visibly different
from each other. This suggests to me that there are other potentially
meaningful differences between SAM and LCMV besides the scalar/vector
difference, and I want to make sure I have at least some idea of what those
differences are before I commit to the change.

That being said, I get the feeling that these differences may be more
nuanced than I can decipher on my own, so if anyone can explain to me what
these differences are and if they are important, I would greatly appreciate
it.

Thanks,

Max

-- 
Max Cantor
Lab Manager
Computational Neurolinguistics Lab
University of Michigan
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