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.

Video: https://youtu.be/nf-P3b2AnZw

Watch how it has preconceived notions of these scenes. It frequently fails to reconstruct the correct scene from video, and it also turns completely blank input into one of the scenes it was trained on.

Currently eyewitness criminal sketches are still drawn by artist so they are naturally low fidelity.

That will change once you can generate a photo of a face (like https://thispersondoesnotexist.com/) based on your brain waves.

This will be disastrous on so many levels. The eyewitness might not have a good sample of a minority race. The GAN dataset itself might also only be trained on celebrity faces so it doesn't know how to generate anything else (e.g., a teen).

But it will be deceptively high resolution so police will rely on it.

If you have a generic face your life is fucked.

We have 24 subjects listening to 12 songs in random order, with 128 channel EEG sampling at 1000hz. We can then label all these data points with the musical features at the time the data is collected.

We don't have a public repo yet, but we are sharing data.