[Eeglablist] BINICA in Windows
Brian Harvey
brian.harvey at biogen.com
Fri Jan 17 15:01:18 PST 2025
I use CUDAICA on windows regularly and can confirm it supports the extended infomax option. The windows binary and dependencies/installation info can be found at the following link
https://urldefense.com/v3/__https://github.com/CloudyDory/cudaica_win__;!!Mih3wA!HpIclkdtxBKyfcUF2TGn8KlwndQrE8Ik4hLRVxtZ2hHJFRbYfqHHGiF7F1bf5Ez2ChB6ReOUqw2VwDaaYQGYME7ud5Zs$
I have only used it Nvidia RTX GPUs and the more GPU memory the better.
Brian
[https://urldefense.com/v3/__https://opengraph.githubassets.com/891e34f57e6a2228602bb8c7426313c52c1d545b761354a3ae6668c5e89346e3/CloudyDory/cudaica_win__;!!Mih3wA!HpIclkdtxBKyfcUF2TGn8KlwndQrE8Ik4hLRVxtZ2hHJFRbYfqHHGiF7F1bf5Ez2ChB6ReOUqw2VwDaaYQGYMA4_A1D3$ ]<https://urldefense.com/v3/__https://github.com/CloudyDory/cudaica_win__;!!Mih3wA!HpIclkdtxBKyfcUF2TGn8KlwndQrE8Ik4hLRVxtZ2hHJFRbYfqHHGiF7F1bf5Ez2ChB6ReOUqw2VwDaaYQGYME7ud5Zs$ >
CloudyDory/cudaica_win: CUDAICA on Windows - GitHub<https://urldefense.com/v3/__https://github.com/CloudyDory/cudaica_win__;!!Mih3wA!HpIclkdtxBKyfcUF2TGn8KlwndQrE8Ik4hLRVxtZ2hHJFRbYfqHHGiF7F1bf5Ez2ChB6ReOUqw2VwDaaYQGYME7ud5Zs$ >
CUDAICA on Windows. Contribute to CloudyDory/cudaica_win development by creating an account on GitHub.
github.com
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From: eeglablist <eeglablist-bounces at sccn.ucsd.edu> on behalf of Richards, John via eeglablist <eeglablist at sccn.ucsd.edu>
Sent: Friday, January 17, 2025 4:32 PM
To: smakeig at gmail.com <smakeig at gmail.com>; Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
Cc: eeglablist at sccn.ucsd.edu <eeglablist at sccn.ucsd.edu>
Subject: Re: [Eeglablist] BINICA in Windows
EXTERNAL SENDER
CUDAICA says it is an implementation of infomax. I have used it a lot, its definitely faster. A lot faster. I've been doing it on linux rather than windows, so don't know how it would work on windows.
John
***********************************************
John E. Richards
Carolina Distinguished Professor
Department of Psychology
University of South Carolina
Columbia, SC 29208
Dept Phone: 803 777 2079
Fax: 803 777 9558
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-----Original Message-----
From: eeglablist <eeglablist-bounces at sccn.ucsd.edu> On Behalf Of Scott Makeig via eeglablist
Sent: Friday, January 17, 2025 4:14 PM
To: Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
Cc: eeglablist at sccn.ucsd.edu
Subject: Re: [Eeglablist] BINICA in Windows
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) 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://urldefense.com/v3/__https://sccn/__;!!Mih3wA!FWi1S3uCcm0BlaMIEHb68DijfV6RgrGuoc14YagU7FDEbZXzrSHVcqevL9P2T1ZZ_zXp4WuBONTwzakyuQkZVANQzw86Xw$
> .ucsd.edu%2Fwiki%2FMakoto%27s_useful_EEGLAB_code%23By_using_CUDAICA_.2
> 805.2F09.2F2022_updated%3A_Thank_you_Ugo.2C_Yunhui.2C_John.2C_and_Erni
> e.21.29&data=05%7C02%7CRICHARDS%40mailbox.sc.edu%7Cb8ba4f408ee34a854d6
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> 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 <
> >
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