Here’s what happens when micro-electrode arrays are used in a monkey brain to distinguish the response of single neurons to visual stimuli. A neuron fires orders of magnitude more quickly in response to synthetic, evolved patterns than to natural video. One neuron seems to recognize another monkey that wears a red tag; another seems to recognize a human caretaker who wears a blue surgical mask.
These results, too, are fascinating, to me at least.
Indeed! How could they produce such reconstruction with only a limited EEG electrodes? I suspect overfitting as an explanation.
From the article:
Researchers from Russian corporation Neurobotics and the Moscow Institute of Physics and Technology have found a way to visualize a person’s brain activity as actual images mimicking what they observe in real time. This will enable new post-stroke rehabilitation devices controlled by brain signals.
Since the device needs to be trained on each subject, the “post-stroke rehabilitation” will only work on people having already undergone the training before their stroke, right?
but unfortunately we didn’t get there. It remains to be seen if we keep the investigation up now after the hackathon.
I think the criticism here underlines the correct figure of merit: the approach can’t be evaluated simply by how well the generated images approach the original images, but rather how covariant they are. A causal measure of how much info is taken from the EEG.