[Eeglablist] Lack of convergence in AMICA solution

[Scott Makeig]

Farhan - See my >> comment below.  -Scott

On Tue, Sep 29, 2020 at 4:06 AM Farhan Ali (Asst Prof) <
farhan.ali at nie.edu.sg> wrote:

> Thanks Scott for the insightful comments. Some responses below.
>
>
>
 *- Perhaps there were no meaningful state transitions in your data, thus
the 'extra' multiple models latched onto various noise data features.*  * See
Hsu et al. 2018 on interpretation of multiple models in datasets involving
state changes.*

Possible. However, the data is a concatenation of 4 blocks representing 4
different conditions that the subjects underwent and in some (but not all),
we do see different models being dominant in different conditions (somewhat
similar to Hsu et al. 2018, NeuroImage), so we think at least in some
subjects, multiple models may have some basis.

>> Then I might well expect to find some different 'brain' ICs in the
different models...  as we do.  (p.s. How many channels were recorded?)

>> Note: By 'brain' ICs above I mean those with resid. var. to the (single
or dual symmetric) dipole model < ~15%.


*- How did you see that the solutions were 'very different'?  Different
time domains for the 3 models? Different IC maps?  The post-AMICA menu
invokes tools to plot the domains of the different models.*

Different IC scalp maps by visual inspection. The differences across runs
were quite obvious, so I didn’t even bother doing statistical comparisons.

>> Wouldn't this be expected?  Were the ICs strikingly non-dipolar (high
resid. var.)?

>> Might you try computing the mutual information reduction (MIR) in the
data by single-model ICA vs multimodel ICA (after breaking the data into
the different model ranges).

>> Does multimodel AMICA decomposition effect stronger MIR than single
model? If not, then there is likely to be no meaningful changes in
source-space configuration revealed by multi-model decomposition.



*- One should expect that most ICs should be shared by all models (e.g.,
ICs accounting for eyeblink artifact? etc.).*

It’s true that some ICs are common, particularly horizontal eye movements,
but the majority of the ICs are not shared across models within an
individual run, at least in our data.

 >> Again, without saying what differences in which kinds of IC maps, it is
hard to say anything...

>
>
> *- Perhaps the dimension of your recording (10-12 channels) was
> insufficient to create a stable decomposition -- more independent sources
> than channels...*
>
> I find this explanation more likely and related to my earlier point. When
> we do an initial ICA decomposition assuming stationarity, then select only
> brain ICs, re-project them, followed by subsequent AMICA, the final
> solutions we get are very stable. All runs on the same data produce almost
> identical AMICA solutions. This pipeline likely reduced the dimensionality
> of the data (less number of independent components) to then allow AMICA to
> converge. That’s my interpretation. What do you think of this pipeline?
>
>> It doesn't sound surprising, since ICA has already 'honed in' on stable
IC processes. Thus, unlikely that multi-model ICA would highlight
interesting differences e.g. between conditions. (p.s. conditions = what
here?)

>
>
> * -  What ASR processes did you employ? Data correction?*
>
> I used the *Reject data using Clean RawData and ASR* option in EEGLab
>
>> I'll leave suggestions on this to others.  -Scott

-- 
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