
Neurovis: Visualizing brain signals in 3D in real-time - breck
http://www.neuropro.ch/neurovis
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BucketSort
The consumer grade EEG's you would use for this, like Open BCI, place
rudimentary sensors on the scalp. There just isn't enough information from
these types of sensors to give you a real look at what signals are occurring
in the brain. This is a fundamentally flawed premise and is nothing more than
a toy. Real brain research must be done with FMRIs or much more accurate EEG's
than consumers have access to, and institutions with such advanced tech
already have vis software.

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arca_vorago
What is the most advanced open source solution to the brain computer interface
issue? Afaik they had gotten pretty good at decoding even the low sensor
density data into actionable input, but all solutions suffer from
input/compute lag.

So that said, FMRI's won't work for realtime will it due to how long scans
take? So EEG's seem to be the only choice. Perhaps to make up for accuracy you
have to increase sensor density enough and you should get a system thats
fairly accurate. My guess is that most modern attempts have poor sensor
density.

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EmlynC
There are many open-source solutions for BCI projects like BCI2000
([http://www.schalklab.org/research/bci2000](http://www.schalklab.org/research/bci2000)),
OpenVibe ([http://openvibe.inria.fr/](http://openvibe.inria.fr/)) and EEGLab
([https://sccn.ucsd.edu/eeglab/index.php](https://sccn.ucsd.edu/eeglab/index.php)).
That's based on the kinds of tools that our customers use. Most of these,
aren't as pretty as tool like Neurovis, but in reality most of the information
that we make use of to control prosthetics or signal intention involve looking
at the temporal-frequency relationships between and within broad regions of
the brain. There isn't a lot that you gain from just looking at the brain
light up like this for BCI — the main use for a visualisation like this, is as
the docs say, for diagnosis and determination of epilepsy since in epilepsy
the activity you'd see is much higher than usual.

EEG has better temporal resolution than FMRI; you are measuring the electrical
activity rather than vascular changes, the former changes more rapidly than
the other. EEG, however, is just the surface activity of the brain, so you
don't get information about 'deeper' (physically) brain processes; this is
where FMRI is invaluable. EEG is also limited to the size of the electrodes
and how many electrodes you can physically place in one location. 256
electrodes on an EEG cap is about the limit you can get to.

Electrocorticography (ECog) involves implanting electrodes on the dura, this
can substantially improve the density of electrodes in a given area, however
this doesn't measure deep brain activity and we have no way of leaving the
electrode grid in place for long periods of time without risking infection.
For BCI, we've been able to classify more classes of data using ECog than EEG
— research by Kyousuke Kamada and Gerwin Schalk are informative. It's a very
promising area if we can work out how to implant the electrodes, seal the
skull and telemeter data out.

Magnetoencephalography (MEG) can help with measuring deep brain activity, but
there are other tradeoffs to consider. Essentially, the point where we are now
is combining multiple techniques to get the best temporal, spatial and
frequential compromises.

Thus in answer to your question; no FMRI is not great for realtime responses,
measuring the electrical activity has better temporal properties so EEG and
ECog work better here. Sensor density is part of the problem, but once you
solve density you then need to consider how you'll deal with deep brain
measurements.

Background; I own a company that distributes BCI equipment for g.tec medical
engineering in the UK. We've been operating in this space for 8 years. I have
a PhD in Pharmacology and a speciality in electrophysiology.

~~~
arca_vorago
Thank you so much for taking the time to answer my questions. Looks like I
have some reading up to do.

The reason I ask about this is because my long term thinking is that while
motion controlled virtual reality is fun and going to be good for exercise
games and training, I think human-brain-computer interface represents the most
promising of the interaction methods (nothing says you can't be hooked up and
still typing, using a mouse, or motioncontrolling either).

You mentioned the medical desire of seeing deeper brain waves, but I just want
to control a computer, so given the current state of the EEG's are they useful
yet as a practical operating system control input?

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hacker_9
The video looks really cool, even if it just shows how fast the bloodflow in
the brain moves about. I didn't realise it was that quick before, so that was
interesting. But their technology paragraph makes no sense:

 _" Unity takes advantage of the computational power of the graphical
processing unit (GPU) thus greatly improving processing speed for real-time
analytics and data visualisation."_

And so does any other 3d application in the last 40 years. It's why we have
GPUs. Unity does nothing special, and in fact doesn't even give you access to
the full power of the GPU (like CUDA does).

 _" Streaming data from local files, the cloud or connected headsets improves
memory usage and computational performance."_

Streaming data from the internet improves performance? That makes no sense.

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IanCal
> Streaming data from the internet improves performance? That makes no sense.

The streaming part improves performance over loading the whole data in
advance. I think that's what they mean.

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quicon
This is as useful as plotting traffic data from NY into a map of SF.

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dang
This comment could be useful if you'd explain why, but as it stands, it's just
a shallow dismissal of the kind we're hoping to avoid here. Please don't post
like that.

If, however, you'd like to neutrally explain the point (neutrally = without
snark or spin), so we could all learn something, that would be great.

[https://news.ycombinator.com/newsguidelines.html](https://news.ycombinator.com/newsguidelines.html)

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frereubu
This quote - "facilitating intuitive interpretation of complex phenomena" \-
makes me very suspicious. Inference in neuroimaging is complex and you need to
take a great deal of care when interpreting data. There isn't really such a
thing as a good outcome from "intuitive" interpretation of neuroimaging data.

For example, there was a story in 2011 about some research that said the
brain's "love centre" was being activated by a video of a vibrating and
ringing iPhone. They were talking about the insular cortex, but the insular
cortex is also involved in vomiting.

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lend000
This is very cool -- does anyone have experience with the company or product?
This looks like something I want for my home lab.

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rdruxn
Cool, but practically useless.

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ngcc_hk
Interesting but I think Heart is more ok. Brain? Imagine you put a eeg and ecg
in an alphago neural network and looked at the heat map. Any use? That is not
even close to the 100 billion plus Neuton we have. Wonder. But the graphic
especially the heart is interesting.

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epmaybe
Isn't alphago a dqn? If so, how would you generate any eeg or ecg heat maps?
Or rather, how would you generate a state action space given that data?

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yoz-y
The only new thing with these visualisations is that they are nicer than what
is currently done. And in nicer I mean that they have more polygons and
translucency. Most tools working with EEG do have extremely similar
visualisations bundled with them as well.

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kruhft
Better than the Emotiv locked down software I hope? Worst part of the Emotiv
experience is simply registering to be able to use their software...

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abhyudaypratap
NeuroVIS is just amazing.

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make3
Is it? It looks like something you could code up in a few weeks.. just take a
random brain model, make it semi translucent, then map the data inside of the
model to a continuous 3d smoothed grid, and voila

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SubiculumCode
I'm not an eeg guy, but do fMRI. The little video on the site did not give me
an indication of what makes it notable or useful. Eeg researchers create heat
maps all the time over electrode points. They also look at timeseries on those
electrode points.

Edit: I guess tht focus is on presenting the data as it is collected. Not sure
if that is novel or not, but that is more specific than I thought at first.

