Fair point. Side-channel attacks show how much signal you can pull from noise. But fMRI is a different kind of beast. It’s slow, indirect, and coarse. You’re not measuring neural activity directly, just blood flow changes that lag by a few seconds.
The paper [0] doesn’t pretend otherwise. It trains a model (PAM) to learn which brain regions carry useful info for reconstructing images, and applies this to both fMRI data from humans and intracranial recordings from macaques. The two signal types are handled separately.
If you want an analogy, it’s less like tapping power lines and more like trying to figure out which YouTube video someone is watching by measuring heat on the back of their laptop every few seconds. There’s a pattern in there, but pulling it out takes work.
The paper [0] doesn’t pretend otherwise. It trains a model (PAM) to learn which brain regions carry useful info for reconstructing images, and applies this to both fMRI data from humans and intracranial recordings from macaques. The two signal types are handled separately.
If you want an analogy, it’s less like tapping power lines and more like trying to figure out which YouTube video someone is watching by measuring heat on the back of their laptop every few seconds. There’s a pattern in there, but pulling it out takes work.
[0] https://www.biorxiv.org/content/10.1101/2024.06.04.596589v2....