[Eeglablist] BINICA in Windows

Fri Jan 17 13:23:18 PST 2025

> Does CUDAICA implement Extended Infomax?  I do not recall ...

Supported.
https://urldefense.com/v3/__https://github.com/CloudyDory/cudaica_win/blob/02aaf9c36c0b12346bccdc25eec195565634abdd/EEGLAB_Plugin/CudaICA1.1/cudaica.m*L17__;Iw!!Mih3wA!BgEkgL2xStRSA4D5IziCMnGoNkSx52ZRQsUMBYj223kQIUwrapgHxpSzz2SZyhXEhXg23A22vnQbFGMhzuvkRC2egEM$ 

On Fri, Jan 17, 2025 at 4:13 PM Scott Makeig <smakeig at gmail.com> wrote:

> But recall that using RUNICA/BINICA  in Extended Infomax mode (the
> default) is advantageous, as simple Infomax should fail to locate
> independent components whose probability distribution function (pdf) is
> subGaussian (e.g., pure line noise).  For most accuracy, we showed in 2012
> (Delorme et al., 2012) that AMICA is the most powerful ICA approach because
> it fits its component pdf model to each component as it learns.  Recently
> its author, Jason Palmer, has made available an AMICA version on the UCSD
> supercomputer that can be used for free via the Neuroscience Gateway (
> nsgportal.org
> <https://urldefense.com/v3/__http://nsgportal.org__;!!Mih3wA!GiYJXGOyag-vo57-Sop7UhOx9D9iyFTL0CXwtvYsC3zx5u6gh9k2VNPPWTmlDp1BH0tRU6q4-QwS3LzgEw$>)
> and is many times faster through proper parallel computing configuration.
> This is particularly important when using the 2nd major advantage of AMICA,
> its ability to find multiple ICA models operating on different subsets of
> the input data -- a sensitive measure of non-stationarity in the ICA source
> solution (see SH Hsu et al., 2018, 2022).
>
> Does CUDAICA implement Extended Infomax?  I do not recall ...
>
> Scott
>
> On Fri, Jan 17, 2025 at 2:06 PM Makoto Miyakoshi via eeglablist <
> eeglablist at sccn.ucsd.edu> wrote:
>
>> Hi Andrew,
>>
>> If you really need speed, try CUDAICA. It can be 15-25 times faster,
>> depending on your environment.
>>
>> https://sccn.ucsd.edu/wiki/Makoto's_useful_EEGLAB_code#By_using_CUDAICA_.2805.2F09.2F2022_updated:_Thank_you_Ugo.2C_Yunhui.2C_John.2C_and_Ernie.21.29
>>
>> Makoto
>>
>> On Thu, Jan 16, 2025 at 1:24 PM Andrew Corcoran via eeglablist <
>> eeglablist at sccn.ucsd.edu> wrote:
>>
>> > Hi list
>> >
>> > I'm rerunning some old preprocessing scripts and having some trouble
>> > getting the binica implementation of runica to work with my version of
>> > EEGlab (2024.2.1). The script runs fine with an archived version of
>> EEGlab
>> > from 2021 so I assume I have not set up the binary correctly.
>> >
>> > I have tried copying over the binica.exe file from my archived version
>> and
>> > updated icadefs.m to ensure the ICABINARY path is correct. Attempting to
>> > run binica via pop_runica produces the following error:
>> >
>> > Output argument "wts" (and possibly others) not assigned a value in the
>> > execution with "binica" function.
>> > Error in pop_runica (line 532)
>> >             [EEG.icaweights,EEG.icasphere] = binica( tmpdata, 'lrate',
>> > 0.001, g.options{:} );
>> >
>> > I would like to continue using binica given its speed advantages but am
>> > concerned that reproducibility of my analyses (by my future self and
>> > possibly others) will be hampered if binica is difficult to set up...
>> > Indeed,  I struggled to find a convenient download (for Windows) -- the
>> > github code seems to require compilation, which I don't recall doing
>> > previously. Is there a more straightforward alternative ?
>> >
>> > Thanks
>> > Andrew
>> >
>> >
>> > --
>> > Dr. Andrew W. Corcoran
>> > Post-doctoral Research Fellow
>> > Monash Centre for Consciousness & Contemplative Studies
>> > <
>> >
>> https://urldefense.com/v3/__http://www.monash.edu/consciousness-contemplative-studies__;!!Mih3wA!BKRCpLqNy1TXMHXE5WoaBYdNGJDn4TNyNuWKRMmybAZkKwVupR5uFEfh4l47c_3-RGLdiMeKTF0s9RLbs3O638jvtg3z3ui7kV8$
>> > >
>> >
>> > Rm 422, 29 Ancora Imparo Way
>> > Clayton, VIC 3800, Australia
>> > +61 3 9905 4266
>> >
>> > Website
>> > <
>> >
>> https://urldefense.com/v3/__https://www.monash.edu/arts/cognition-and-philosophy-lab/people/andrew-corcoran__;!!Mih3wA!BKRCpLqNy1TXMHXE5WoaBYdNGJDn4TNyNuWKRMmybAZkKwVupR5uFEfh4l47c_3-RGLdiMeKTF0s9RLbs3O638jvtg3zjTfBFD0$
>> > >
>> > | Google Scholar
>> > <
>> >
>> https://urldefense.com/v3/__https://scholar.google.com/citations?user=yxKSfdsAAAAJ&hl=en__;!!Mih3wA!BKRCpLqNy1TXMHXE5WoaBYdNGJDn4TNyNuWKRMmybAZkKwVupR5uFEfh4l47c_3-RGLdiMeKTF0s9RLbs3O638jvtg3zvt-Dcdc$
>> > > |
>> > ResearchGate <
>> >
>> https://urldefense.com/v3/__https://www.researchgate.net/profile/Andrew-Corcoran-5__;!!Mih3wA!BKRCpLqNy1TXMHXE5WoaBYdNGJDn4TNyNuWKRMmybAZkKwVupR5uFEfh4l47c_3-RGLdiMeKTF0s9RLbs3O638jvtg3z1eQ4DBY$
>> > > |
>> > ORCID <
>> >
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>> > > | Twitter
>> > <
>> >
>> https://urldefense.com/v3/__https://twitter.com/mr_corcorana__;!!Mih3wA!BKRCpLqNy1TXMHXE5WoaBYdNGJDn4TNyNuWKRMmybAZkKwVupR5uFEfh4l47c_3-RGLdiMeKTF0s9RLbs3O638jvtg3zbVXNVaM$
>> > >
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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 92093-0559, http://sccn.ucsd.edu/~scott
>