What would a world look like where all thoughts are public?
* a LOT more weird porns
* we do not need passwords
* eventually, human will be more empathetic
* new educational system
Natural image reconstruction from brain waves
11–20 of 41 posts
Re: Natural image reconstruction from brain waves
#12Re: Natural image reconstruction from brain waves
#13I'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.
Re: Natural image reconstruction from brain waves
#14The end results are much, much better than I thought they would be. Luckily, I think it would be easy to fool the training by thinking about a totally different image to the baseline one. Idk if that would stand up to rubber hose cryptanalysis, but there’s got to be a way that can.
The end results show an overfit model. It's not predicting that specific input out of an option space of everything; it's essentially predicting that mode (out of the 4) and probably capturing things like "if brain's audio regions are active, it's a waterfall, because waterfalls are loud and trigger that".
Re: Natural image reconstruction from brain waves
#15I'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 happen…
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.
Re: Natural image reconstruction from brain waves
#16What would a world look like where all thoughts are public?
My first thought: https://en.wikipedia.org/wiki/Kallocain
Re: Natural image reconstruction from brain waves
#17I'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 happen…
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.
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.
Re: Natural image reconstruction from brain waves
#18I'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 happen…
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.
Re: Natural image reconstruction from brain waves
#19What would a world look like where all thoughts are public?
* a LOT more weird porns * we do not need passwords * eventually, human will be more empathetic * new educational system
> * we do not need passwords
Probably the opposite. All passwords are machine generated/stored. Everyone uses an HSM.
Re: Natural image reconstruction from brain waves
#20I'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 happen…
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.