[anonytrary]

I'm highly skeptical. I mean, a hash function that has four output states also maps anything to one of those four states. That doesn't mean it's some next-level classifier.

The problem here is EEG. EEG bandwidth is not enough to capture that much information. There is far too much noise introduced by the skull and muscles. It's most likely physically impossible to do something like this with EEG.

What's likely happening here is that there's some large scale oscillations that are sufficiently unique to discern the images from each other. This does not mean they are reproducing the images. I am highly skeptical of the methods used here -- they are almost certainly flawed.

I, too, once had dreams of conquering the planet with EEG when I was a grad student. I quickly learned that physics makes this infeasible. Anyone who is serious about BMIs are studying invasive BMIs and how to make them as safe as possible. Going inside the brain is unavoidable, I'm afraid.

[OnlineGladiator]

About a decade ago when I was still in school, I did some work in brain machine interfaces as well as a friend. I made an EEG from scratch, worked on the DSP and amplifications to make it all work, and also had access to a much more expensive state-of-the-art machine. While I didn't work directly on the project with my friend, at the time they came to the conclusion that non-invasive neural processing (so something topical like an EEG, no surgical implants) could process about 1 bit per second of useful information - the noise to signal ratio was about 1000:1. When most people read the raw data from an EEG they don't realize they can't even see the actual data - they're seeing eye movements, facial muscle twitches, and other noise artifacts that overwhelm the actual signal. I'm guessing the technology has improved a lot since then (I'm in another field now), but it's hard to imagine it gaining however many orders of magnitude in resolution necessary for this to be viable.

[henearkr]

Exactly. I have always been wondering how could the brain waves measurements not be overwhelmed by facial muscle signals.

[vanderZwan]

If you have multiple different points where you measure, which all have this overlapping signal problems but at different strengths, couldn't you hypothetically build up a model that "solves" these different weights and untangles the signals?

[dr_zoidberg]

Yes, but... At the end, you're still reconstructing pieces of information from something that was almost destroyed. Picture it this way: there are amazing deconvolution algorithms that can "undo" all sortf of noise and lack of focuse -- but the end result, however good to the original "bad" data isn't nearly as good as a well taken image to begin with.

Disclaimer: I work in image processing, so the example may be a bit obvious to me.

[vanderZwan]

Isn't what I described more like reconstructing a picture from many copies that were each destroyed in a unique fashion?

[dr_zoidberg]

Yes, that'd be a better analogy. My point was that, even if you had the best reconstruction in the world, having to reconstruct from a degraded source is worse than working from a good source to begin with.

[tudorw]

There is at least one 'affordable' fNIRS device coming to market that looks promising, https://foc.us/fnirs-sensor/ There's a paper somewhere on using machine learning to help identify signal, this one is specifically about pain, https://www.nature.com/articles/s41598-019-42098-w Say for example you were making an insurance claim for neuropathic pain, this kind of information could be very important.

[jacquesm]

Instead, it will be repurposed for lie detectors and 'terrorist mindset detectors' in airports.

[anonytrary]

This is amazing. A not-hotdog for pain would definitely be useful!

[dannyw]

Example of how it may be overfit: Waterfalls are loud, audio regions of our brain may activate in response to waterfalls. Classifier reads that to predict waterfalls.

[anonytrary]

Great example! Similar thing probably holds for moving main limbs. A good EEG should be able to pick up when you think about moving your hand or leg. I doubt a good EEG could distinguish more than a few dozen patterns. Most experiments have trouble with even a handful of patterns. Still could be useful, but just very limited.

[londons_explore]

fMRI seems to avoid many of the bandwidth issues EEG has, at least from a theoretical if not practical position.

With enough receive antennas and processing power, you can get almost unbounded 3D resolution.

[buboard]

i wonder if one day fMRIs become a home fixture like washing machines