[Eeglablist] how to select channels for back-projection of ERP

Mon Aug 10 08:48:49 PDT 2015

Elizabeth and all -

Makoto is correct that the term 'back projection' of an IC to the scalp can
be viewed also (and more fundamentally)  as a *forward* projection from
cortical source to the scalp sensors.

I blieve the only EEG phenomena of interest are the source data activities
(e.g., IC activations) -- the source projections to the scalp channels
(whose sum we record with scalp electrodes) are essentially epiphenomena
(more or less arbitrary source sums and differences, in themselves of no
fundamental interest).

Now, the most correct unit of measurement for cortical sources is source*
current density per cortical area *(for example, per mm^2).  However, to
convert scalp measurements into that unit one needs a high-resolution
electrical forward head model (sources to scalp electrodes) and an ideal
inverse model (source scalp projection map to cortical source patch).  The
former must include accurate values for tissue conductivities within the
individual subject, not something we currently have access to (though see
below).

Therefore, the most reliable unit of measurement we have for the activity
of an identified source in scalp-recorded data is  'RMS uV' -- the root
mean-square average projection from the source to the scalp. Even this has
ambiguity -- To the *whole* scalp, even places where we did not place
electrodes??   A simple alternative is to measure IC activity (unaveraged
or averaged) as the RMS (mean) projection of the source to the actual scalp
electrodes:

e.g., If IC is the index of an IC in an EEG data structure.

 * >> RMS_uv(IC) = EEG.icaact(IC ) / RMS(EEG.icawinv(IC));*  % divide the
IC activation by

                       % the RMS value of its scalp map

Unfortunately, the function *RMS()* I used in the meta-code above is not a
Matlab function (by that name)... I am away from a Matlab installation at
present and forget the actual function call.

It may be that this normalization (making *RMS(EEG.icawinv) = ones()* ) is
already performed in the default output of runica / binica / AMICA - To be
safe, however, run the meta-code above explicitly or test the equality
above ...

All units of measurement for IC activations (activities) should be labelled
'RMS uV/chan' -- I will work with Arno and Ramon to see that this is
corrected in the current functions.

Note: This removes the problem, 'Which channel projection is the best one
to use to measure the contribution of a source to the scalp (ERP or raw)
data?'

Scott Makeig

p.s. To see which ICs contribute most strongly, learn to use and interpret
*envtopo()* -- called in the EEGLAB GUI by '*Plot > Component ERPs > With
Scalp Maps*'.  Makoto has recently supervised building of a plug-in to
perform this function at the STUDY level (here the question is, 'Which IC
clusters contribute most strongly to the grand mean ERP or ERP difference?')

p.p.s  Zeynep Akalin Acar and I have developed a new method for estimating
skull conductivity from the EEG data (given a subject MR head image
allowing development of an individual electrical forward head model). Our
report on this should be accepted soon - more details then or at the
upcoming EEGLAB workshop in UK...

On Mon, Aug 3, 2015 at 6:09 PM, Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
wrote:

> Dear Elizabeth,
>
> A projection from ICs to channels is a 'forward projection'. We've been
> using the word 'backprojection' in the wrong way, so I corrected it (at
> least in my functions).
>
> In ICA, the polarity of map and time-series signal can be flipped by
> applying -1 to the both. This is a fundamental uncertainty.
>
> > If I want to use this, or any other, IC to measure the amplitude /
> latency of particular ERP deflections it is necessary to know which is the
> correct channel to choose for back-projection. However, I am unsure as to
> how to make this decision objectively.
>
> I wrote pvaftopo() plugin for this purpose
> http://sccn.ucsd.edu/wiki/Plugin_list_process
> pvaf is a new concept Scott proposed nearly 20 years ago which is
>
> percent variance accounted for (pvaf) = 100-100*var(all_data -
> selected_ICs)/var(all_data)
>
> We'll publish IEEE EMBC conference proceedings soon for this year about
> the study-level envtopo and pvaf, so you may want to refer to the paper
> once it's published (Lee et al., IEEE EMBC conf proc 2015)
>
> Pick up the channel that is closest to the peak in the scalp maps and you
> can choose the 'most accounted' channel objectively.
>
> I'm happy that there is finally a user asking for this plugin!
>
> Makoto
>
> On Fri, Jul 31, 2015 at 2:13 PM, Elizabeth Milne <e.milne at sheffield.ac.uk>
> wrote:
>
>> Dear EEGLAB team,
>> I have a question about resolving the "true" polarity of IC ERPs by
>> back-projecting to channel data. Using erpimage, I have obtained the ERP of
>> an IC, but the polarity is different (orthogonal) when back-projecting to
>> different channels. In another example from a different IC, the polarity of
>> the ERP does not change, but the amplitude of the signal is different
>> depending on which channel is selected for back-projection.
>>
>> If I want to use this, or any other, IC to measure the amplitude /
>> latency of particular ERP deflections it is necessary to know which is the
>> correct channel to choose for back-projection. However, I am unsure as to
>> how to make this decision objectively. Are there particular features that I
>> should look at to make this decision more systematic, i.e. dipole
>> projection? % power accounted for?
>>
>> If anyone can offer advice, or point out errors in my understanding I'd
>> be very grateful.
>>
>> Many thanks,
>> Elizabeth
>>
>> --
>> Elizabeth Milne
>> Reader in Cognitive Neuroscience
>> University of Sheffield
>> Western Bank
>> Sheffield
>> S10 2TN
>>
>> +44 (0) 114 222 6558
>>
>> http://www.autismresearchlab.group.shef.ac.uk/
>>
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>
>
>
> --
> Makoto Miyakoshi
> Swartz Center for Computational Neuroscience
> Institute for Neural Computation, University of California San Diego
>
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-- 
Scott Makeig, Research Scientist and Director, Swartz Center for
Computational Neuroscience, Institute for Neural Computation, University of
California San Diego, La Jolla CA 92093-0961, http://sccn.ucsd.edu/~scott
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