[Eeglablist] Installing CUDAICA on Windows 10 (2021 update)
mmiyakoshi at ucsd.edu
Thu Nov 18 12:41:59 PST 2021
Apart from the value of the study, I don't like the side effect Fiorenzo's
PCA paper caused: It made non-engineers superstitious about the use of PCA
(and now you push this fear campaign.)
In the Artoni paper in question, at the very first line of the result
section he reported that there were only 8+/-2.5 PCs were left to obtain
95% of variance out of 71 scalp electrodes. You can easily imagine what
happens when you reject 63/71 PCs before ICA. In this sense, the conclusion
of this study is 'duh' to me (is it not?)
'Applying PCA before ICA is suboptimal' is a qualitative statement. But
what if you reject only one or two PCs just to make the data full-ranked?
These questions can be answered only by performing numerical and simulation
studies. We should educate people to reject qualitative statements and
instead think quantitatively.
It's not about CUDAICA per se, but if you care the obtimality of the ICA
results, I recommend you compare results from channel rejection against
results from PCA dimension reduction so that you obtain 70 ICs in both
results. Compare the results side by side and determine both visually and
quantitatively using ICLabel. Most likely, they do not show much
difference. Then you feel better to go with PCA because so that you don't
need to lose scalp electrodes.
On Wed, Nov 17, 2021 at 1:22 PM Scott Makeig <smakeig at gmail.com> wrote:
> Makoto seems to forget the result of Fiorenzo Artoni that applying PCA
> before ICA is suboptimal - better to reduce the number of channels and run
> ICA decomposition full-rank. Or, if you are more ambitious / exacting, run
> multiple ICA decompositions on different channel subsets (for example,
> random sets of 70 channels picked from 128) and then apply clustering to
> the resulting independent component (IC) maps - I haven't seen that
> approach applied yet ...
> Artoni, F., Delorme, A. and Makeig, S., 2018. Applying dimension
> reduction to EEG data by Principal Component Analysis reduces the quality
> of its subsequent Independent Component decomposition
> . *NeuroImage*, *175*, pp.176-187.
> Fiorenzo's RELICA plug-in does something related - applying (full-rank)
> ICA decomposition to randomly selected subsets of data points, followed by
> component clustering. Zeynep Akalin Acar has recently demonstrated that
> using RELICA component cluster scalp map means *and* variances can
> increase the accuracy of high-resolution source location estimation.
> Acar, Z.A. and Makeig, S., 2020, October. Improved cortical source
> localization of ICA-derived EEG components using a source scalp projection
> noise model
> In *2020 IEEE 20th International Conference on Bioinformatics and
> Bioengineering (BIBE)* (pp. 543-547). IEEE.
> On Wed, Nov 17, 2021 at 3:24 PM Makoto Miyakoshi via eeglablist <
> eeglablist at sccn.ucsd.edu> wrote:
>> Dear John,
>> I found it interesting that in your case runica()'s processing time
>> linearly increased (63 -> 168 min) as the input data length increased (8
>> 25 min), but that for CUDAICA did not (2.3 -> 2.8 min).
>> If you have 126 ch, you want to have 126^2*30 = 476280 data points as a
>> minimum (from SCCN's never-verified rule of thumb). But you have 275*474=
>> 130350 datapoints, which seems suboptimal to ensure a good learning.
>> Perhaps you want to apply dimension reduction using PCA to obtain 70 ICs,
>> so that the same rule of thumb predicts 70^2*30 = 147000 datapoints for
>> learning, which is much closer.
>> Do you want to know more detail about this optimization?
>> In fact, without running a simulation you can't theoretically determine
>> what number is a good number. This is why I wrote this simulator as an
>> EEGLAB plugin. Try it out to 'feel' how much deviation/violation from the
>> 'rule of thumb' can negatively impact the decomposition.
>> On Tue, Nov 16, 2021 at 2:59 PM Richards, John <RICHARDS at mailbox.sc.edu>
>> > I was curious about the speed differences for my applications. I have
>> > tested this before, did not write down my results.
>> > I ran an EEGlab file, 126 channels * 275 samples * 474 trials, about 8
>> > of EEG data. This is done on a linux node in a a linux cluster,
>> > Xeon(R) CPU E5-2680 v4 @ 2.40GHz node, with 256G memory, 28 cores. The
>> > runica appears to be working on 12 cores. The gpu was a dual Tesla
>> > P100-PCIE-16G. The cudaica ran on one GPU.
>> > runica version took 63 min. cudaica version took 2 min 15 s;
>> > runica appeared to be running on multiple CPUs, ~ 12 CPUs.
>> > I concatenated the data for 1422 trials, about 25 min
>> > Cudica took 2 min 50s|
>> > runica took 2.8 hours, 12 CPUs
>> > Most of our runs with infants take 8 to 10 min, some of our adults runs
>> > are 25 min.
>> > I understand from the earlier conversation the binica might be able to
>> > match these results? I'm not going to do a full test, but this
>> > me to stick with cudaica for now.
>> > John
>> > -----Original Message-----
>> > From: Richards, John
>> > Sent: Thursday, November 11, 2021 1:15 AM
>> > To: Makoto Miyakoshi <mmiyakoshi at ucsd.edu>; ugob at siu.edu;
>> > eeglablist at sccn.ucsd.edu
>> > Subject: RE: [Eeglablist] Installing CUDAICA on Windows 10 (2021 update)
>> > Re CUDAICA. I was able to install it, i don't remember it being that
>> > difficult. I had to mess around with the CUDA version.
>> > I have found it "blazing" fast compared to runica. I have not timed it.
>> > We have 10-15 min sessions with EGI 128, 250 hz, do the Prep pipeline to
>> > get avg ref, and then CUDAICA. It takes < 5 min to do the Prep, and <
>> > min to do the CUDAICA; cf 45 min to 60 min with runica. I may not be
>> > the most recent runica. BTW, we have fairly powerful computers; we
>> use 48
>> > cores for the Prep pipeline which is a vast speedup, and V100's with
>> > or 32gb. Definitely not bargain chips. We use the 48core computers
>> > the runica, but it does not appear to profit from the multiple CPUs.
>> > Prep pipeline also is very slow on single CPUs, but very fast on the 48
>> > machines.
>> > I would be glad to share more details if anyone is interested.
>> > 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
>> > Email: richards-john at sc.edu
>> > *************************************************
>> > -----Original Message-----
>> > From: eeglablist <eeglablist-bounces at sccn.ucsd.edu> On Behalf Of Makoto
>> > Miyakoshi via eeglablist
>> > Sent: Thursday, November 11, 2021 1:02 AM
>> > To: EEGLAB List <eeglablist at sccn.ucsd.edu>; ugob at siu.edu
>> > Subject: [Eeglablist] Installing CUDAICA on Windows 10 (2021 update)
>> > Dear list members,
>> > I summarized the steps to install cudaica() which uses GPU computation
>> > calculate infomax ICA (Raimondo et al., 2012). The result from the speed
>> > comparison between runica() and cudaica() was not as dramatic as x25
>> > reported by the original paper, probably because Tjerk's smart hack
>> > already gave x4-5 speed up to runica(). Still, using a relatively cheap
>> > GTX1660 (the pre-COVID price range is $250), I confirmed x4-5 speed up
>> > compared with runica(). The detailed instruction can be found in the
>> > following link.
>> > WARNING: The installation was difficult.
>> > Makoto
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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
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