Link to original article: https://www.dropbox.com/s/x2z14gw8017ezbg/for%20reddit.pdf https://www.dropbox.com/s/x2z14gw8017ezbg/for%20reddit.pdf (Via reddit: ht

Link to original article: https://www.dropbox.com/s/x2z14gw8017ezbg/for%20reddit.pdf https://www.dropbox.com/s/x2z14gw8017ezbg/for%20reddit.pdf (Via reddit: http://en.reddit.com/r/Scholar/comments/1kc8mw/request_linear_reconstruction_of_perceived_images/ http://en.reddit.com/r/Scholar/comments/1kc8mw/request_linea...) The Gallant lab at UC Berkeley did something somewhat similar about 2 years ago. See here: https://www.youtube.com/watch?v=KMA23JJ1M1o https://www.youtube.com/watch?v=KMA23JJ1M1o From what I understand, both reconstructions involve setting up models of brain activity for vision, learning the parameters by machine learning from patients, and then using Bayesian inference to determine what is being seen. While incredibly cool, we are still a long way from reading thoughts, and even longer if we're not allowed to learn the parameters for that subject first. Right now, we can only kinda reconstruct what someone is seeing, but that's really not much better than a camera.