[Eeglablist] LORETA applied to single-trial EEG analyses?

Eric Rawls elrawls at email.uark.edu
Thu Jul 11 20:37:33 PDT 2019

Thanks, I had assumed as much. Of course there is no reason to apply
physiological electromagnetic constraints to beta weights.
This doesn't bode well for the use of dipole fitting via dipfit either in
the localization of EEG regression results, does it (as dipfit also relies
on Maxwell's equations)?
I guess the only way to estimate the cortical source of a scalp regression
result would be to run the regression on source-level data such as
independent components or voxelwise activity returned from LORETA (or
something similar)?
Thanks for the response

On Thu, Jul 11, 2019 at 6:49 PM Makoto Miyakoshi <mmiyakoshi at ucsd.edu>

> Dear Eric,
> > Are these weights acceptable as input to LORETA?
> Most likely not.
> According to the site you referenced, LORETA calculates dipoles in brain
> voxels. A dipole is a electromagnetic property, so the input value should
> be compatible with that. Thus, if the input data is just linear
> transformation of a scalp measurement, such as re-referencing, it would be
> fine. However, if you use non-EEG data as input, it would loose the
> validity the author claims because the algorithm calculates electric
> behavior of non-electric property. For example, what is the field theory of
> the beta weights? Does it follow Maxwell's equation? Why? I guess the
> assumption of the calculation becomes weird in this way.
> I could be wrong here, I would appreciate if someone knowledgeable can
> confirm my explanation.
> Makoto
> On Mon, Jul 8, 2019 at 4:51 AM Eric Rawls <elrawls at email.uark.edu> wrote:
>> Hi list, question for those who have used or are familiar with LORETA.
>> LORETA accepts either time- or frequency-domain EEG. However, the
>> following
>> resource notes a number of ways input to LORETA could be "cheated" despite
>> seeming reasonable inputs.
>> https://www.uzh.ch/keyinst/NewLORETA/Misuse/Misuse.htm
>> My question relates to the cortical localization of beta weights from
>> single-trial regression analysis of EEG. This type of analysis essentially
>> computes an "erp" using regression rather than averaging.
>> Are these weights acceptable as input to LORETA? This approach was taken
>> in
>> Fischer & Ullsperger (2013) to localize cortical prediction error
>> coefficients, but I confess I'm quite unsure whether this is a correct use
>> of LORETA.
>> What do we think?
>> Eric Rawls
>> PhD Candidate
>> Department of Psychological Sciences
>> University of Arkansas
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> --
> Makoto Miyakoshi
> Assistant Project Scientist, Swartz Center for Computational Neuroscience
> Institute for Neural Computation, University of California San Diego

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