
Ask HN: ML, EEG, metal 3D printer, evolved antennas = high resolution BCI? - TomMarius
Hi, what do you think about the combination of these technologies?<p>EEG has a problem breaching the skull. Evolved antennas would be used to pick up better signal on various frequencies that are interesting. Because these need to be very small and ideally integrated into a head cap, we would use 3D printing to create these, which should also open room for even crazier (and more efficient) kind of fractal designs (fractal antennas are what phones use AFAIK). Then we would use GAN to clean up and another GAN to understand the signal. Because of (supposedly, that&#x27;s what my question is mainly about) significantly better signal, it should have much better performance.<p>I&#x27;m not an expert in any of these. What do you think?
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fundamental
I would not trust experiments using GAN on EEG to provide higher
resolution/'quality' signals. You're not going to have a reasonable method to
collect the data needed to train such a network, nor to be able to generalize
the network. It will hallucinate patterns and given the dubious statistical
quality of many comp-neuro papers I would be even more skeptical of many
results.

If you're interested in applying some of these ML techniques I'd consider
looking towards ECoG or LFP array recordings where sensor noise and sensor
failure could in part be approached by techniques like this. For long term LFP
arrays sensor failure is expected and models do not need to necessarily
generalize to other individuals (and there is enough data to train on).

Overall you're not going to create signal when there was none in the first
place.

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TomMarius
I don't have the clear data but I know people that work on obtaining them and
making them open (from local universities). I would like to use this method
together with other methods, so even poorer performance than other methods
could help - provide certainty in some uncertain edge cases, for example

The big question is whether the signal detectable on the outside of skull is
closely correlated with what's going on inside

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fundamental
As an ex-researcher in this domain (still doing research, but elsewhere), the
data does not currently exist. I believe you are grossly underestimating the
amount of efforts, subjects, time, funds, and bureaucracy needed to obtain
this data.

~~~
TomMarius
You are probably assuming I'm in the USA. I'm in Central Europe and this data
is produced by local university (that has many great hospitals attached to
it), open. I have friends who work on the project, they will start releasing
data next year. They're working on the project for past 5 years.

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p1esk
Perhaps you should first learn what a GAN is, because what you wrote makes no
sense.

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TomMarius
I wrote the question precisely to learn more about these topics. Why do you
think GAN doesn't make sense in that context? There are many papers about
using GANs to clean radio signal, not this one specifically but as an example
of a GAN/radio paper:
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6263619/](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6263619/)

~~~
p1esk
In the paper you linked to a GAN is used to generate fake data. This fake data
can help when training a classifier, just like any other data augmentation
method.

I could not find any papers about using GANs to clean up radio signals. It
could probably work if you have large datasets of both clean and noisy
signals, but in this context you most likely don’t. And even if you did what
makes you think denoising with GANs would work better than traditional
methods?

Just because it’s a hot buzzword doesn’t mean you should use it.

~~~
TomMarius
Why are you condescending me? I studied applications of GAN nets for dozens of
hours (still an amateur beginner of course). I didn't even know it was such a
buzzword when I got started as I don't follow twitter etc.

Here is a paper that talks about exactly what I am proposing, using GANs,
minus evolved antennas:
[https://arxiv.org/abs/1806.01875](https://arxiv.org/abs/1806.01875) (it is no
secret that this paper has inspired me to write this question - my thoughts
were about improving it with evolved antennas).

About the clean data requirement, it is common practice to record as much as
possible once doctors open somebody's skull because such opportunity is
scarce. So people are working on it.

~~~
p1esk
I apologize, for some reason I was in confrontational mood.

This paper is also about using GANs to generate fake data.

Here’s the thing - to generate high quality fake data you need to have a lot
of real data, but if you do then you don’t really need fake data.

Re denoising with GANs: do you have a lot of clean data (eg from inside the
skull) that is equivalent to the noisy data (from outside the skull)? Let’s
assume you do. Do you have any example of someone using GANs to clean those
signals? Or any radio signals? What makes you think it would work better than
any other existing denoising methods?

