Real-Time Brain-to-Image Reconstructions

Already gone down this road with lower tech. In the 50s - 70s it was in vogue to monitor blood flow to the genitals while subjecting the subject to erotic imagery of various kinds to see what turned them on. [1]

It was then realized that arousal is also correlated with pupil dilation, which meant that a much less invasive test was possible. Which of course led to the Canadian government (and probably other governments) using it in the 1960s to detect possible homosexuals in the public service. [2]

The scientific flaws and human rights violations of such a test are too many to even begin to list in a short post like this. Still, the inconvenient and disturbing thing is: it does work. Sort of. Kind of. Not good enough to be confident about the result with an individual, but a properly run protocol has better than chance odds at determining if a man is gay or straight, in terms of aligning with their self-reports about what arouses them.

Whether someone is actually erotically aroused by something based on a physical response is obviously controversial. For example, a repeatable finding is that self-reported-heterosexual men with intensely homophobic views are more aroused by homosexual male imagery than the average heterosexual male. Commonly reported in the news and media as: homophobic men are secretly gay. And I guess that's one possible interpretation. But someone about to to fly into a fit of rage is also aroused. Sometimes literally -- sexual arousal during flight-or-fight and disgust responses is a thing. So it's tricky to interpret what that result means, if anything.

No doubt we will fall into similar traps again, with the brain scanning version of it.

[1] https://en.wikipedia.org/wiki/Penile_plethysmography

[2] https://en.wikipedia.org/wiki/Fruit_machine_(homosexuality_t...

You use something like an EEG and show someone a series of images to establish a baseline of “familiar” and “unfamiliar.” Then you show them photographs of the crime scene that haven’t been made public to test for familiarity.

I question its accuracy but your scenario already seems possible.

Edit: for siblings comments about journalists - “identifying” a source could probably be “accomplished” using the above technique already, no need for AI. Show photos of random people mixed in with the photo of the suspected leak in your gov. department.

Projects that are at the intersection of life sciences and machine learning are rarely interesting along both axes, it's always novel on one side and the other side is just there to make it seem cool. For example you'll find a lot of ML scientists investigating medical issues but lacking the basic medical knowledge to actually make an impact, similarly you'll see doctors using neural networks where a linear regression would do. In both cases the paper seems novel to the people in the field it's being published in.

The classifier I was talking about is implicit, if you look at their proposed diagram they want to use fMRI embeddings, they serve as a continuous equivalent. You put a sufficiently large model on top of any embeddings and it will learn to reconstruct their modes. The embeddings are trained across subjects so it's not specific to a single patient but they hope to fine-tune it per patient using latent space alignment.

If you look at the papers in their recommended reading list, it's apparent that only low-frequency signals are learned, for example for face reconstruction the model can learn hair color, sex and beard/no-beard, but the actual face doesn't look anything like that of the person being looked at. Similarly for other benchmarks, the model can tell it's looking at a plane, but not much beyond that. This is cool from a technological standpoint but I recall papers doing the same thing at least 7 years ago. The novelty of their approach will be the architecture that comes after the embeddings, which can be interesting but won't add any more information to the fMRI scans.

Not any time soon: https://duckduckgo.com/?q=fMRI+device&ia=images&iax=images

Or whether you're thinking thoughts the government doesn't like.

The only reason the reconstructions are much better than before is because they use the latest generation models. Those models have internal models of the classes which allow them to fill in the high-frequency details in the reconstruction. The only information they get from the fMRI is the same low-frequency signal that previous papers already had, and indeed the only things the reconstructions get right are low-frequency: class of object/scene, broad position of object, broad shape of object.

fMRI scans are aggregates of brain information, they act like low-pass filters over the brain state. You can put as big a model on top as you want it won't make it more truthful a reconstruction.

I think, as you say, that detecting as many classes as possible is already a pretty good goal, developing new embeddings and techniques to see how much juice we can squeeze out of the scans. I like the arxiv preprint you posted in particular since it does just that and evaluates accuracy (although the way it does it is flawed since it uses an image classifier on the reconstruction which presents the same problems). What I don't like is the misrepresentation of what's going on when people put those large generative models on top of this kind of data.