
Deep learning with convolutional neural networks for brain mapping from EEG - kensai
https://arxiv.org/abs/1703.05051
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curiousgal
As a novice research-assistant I really appreciate the fact they included a
_Software implementation and hardware_ section in the appendix.

They even wrote "We will make the code to reproduce these results publicly
available". That should be the norm. Kudos.

~~~
choxi
> We will make the code to reproduce these results publicly available

I'm excited about this trend, it makes it easier for everyday software
engineers to tinker around with cutting edge tools.

I think of innovation as a pipeline starting from scientific insights, to
engineering advances, to improvements in the consumer experience. If research
communities continue to borrow more ideas from open source culture, it could
really improve the speed of that pipeline.

~~~
nojvek
Imagine every CS paper coming out with executable code. That would definitely
put humanity in an accelerated path to fictional future.

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bmc7505
They reported an accuracy of baseline FBCSP and ConvNets on the BCIC dataset
around ~68%-70%, which is reminiscent of early WER for speech recognition
(prior to the advent of specialized speech architectures). However, when you
consider the datasets (BCIC 2a and 2b) are only using 22 scalp electrodes (for
2a) and 3 (for 2b) at 250 Hz, that's pretty amazing. That's analogous to using
something like 8-bit audio for speech recognition.

Clearly there is lots of room for improvement here, both in neural net
architectures, measurement technology, and preprocessing. One technique the
authors didn't explore is source localization [1], which typically requires
much higher density (somewhere on the order of 128-256 active electrodes), but
offers much higher spatial resolution (up to ~5mm isotropic). Given that most
of the EEG datasets are recorded on less than 32 channels, I wonder how much
more signal can be detected from such low resolution methods, or if we're
approaching the theoretical maximum channel capacity of 32 scalp electrodes.

It's like Feynman's analogy of measuring the height of corks in a swimming
pool to determine the physics of an object that hit its surface. The more
corks you have, the more information about the source (location, velocity,
trajectory, geometry) you can reconstruct.

[1]:
[http://www.scholarpedia.org/article/Source_localization#Elec...](http://www.scholarpedia.org/article/Source_localization#Electroencephalography_.28EEG.29)

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cool_shit
I don't see why this research couldn't be applied to empirical data from an
arbitrary system of coupled oscillators.

I am interested in EEG and fNIRs, but there are drawbacks. Naively, people
will try and study this data without first doing a necessary back-of-the-
napkin.

EEG and fNIRs have hard physical limitations. EEG is limited to pick up only
large-scale EM field activity, where the higher resolution perturbations and
effects are averaged out. This is because of an increased measurement distance
and noise that is acquired through the skull and other intermediary tissue.
This is unacceptable since the current scientific consensus is high frequency
phase and activity is fairly important for information coding.

On the bright side, a lot of information might also be encoded in larger scale
synchronized oscillation that happens in the brain (the stuff that EEG picks
up on). This space is obviously lower in dimension.

The only work-around for this hard information limit is to explore invasive
BCI technology (e.g. tetrodes connected to your neurons).

Relatively speaking, this isn't difficult for scientific laboratory research
because:

    
    
      1. We don't care about invasive surgery on rats.
    
      2. We don't care how comfortable rats are.
    
      3. We don't care how mobile rats are.
    

On the other hand, for commercial purposes, it is not feasible to stick a
wired-tetrode array into a human brain yet. We can't afford to lose a human.
Engineering on the invasive BCI frontier is incredibly primitive right now.

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rosser
There is something so deliciously meta about this. FMRI scans might also be a
very interesting place to play with CNNs.

~~~
nullc
FMRI has very poor temporal resolution and moderately poor spatial
resolution-- it's like trying to figure out the workings of your computer by
using a meat thermometer.

~~~
return0
Plus an fMRI machine is not easy to fit on your desk or carry with you.

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jacquesm
A portable fMRI machine... that's the stuff of dreams. For sure that would
change the world for the better but I really don't see how you would even
begin to tackle that.

~~~
return0
I suppose with a few years of weightlifting, it can become portable ...

~~~
chrisfosterelli
I suppose, if you can also weightlift a magnetically & RF shielded room with
you :P

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kensai
I really enjoyed the elegant combination of Theano/Lasagne [1] and Wyrm [2].

[1] [https://github.com/Lasagne/Lasagne](https://github.com/Lasagne/Lasagne)
[2] [https://github.com/bbci/wyrm](https://github.com/bbci/wyrm)

