@inproceedings{b12d9046b6484fad84ae2e9bd2681625,
title = "Bayesian reconstruction of perceptual experiences from human brain activity",
abstract = "A method for decoding the subjective contents of perceptual systems in the human brain would have broad practical utility for communication and as a brain-machine interface. Previous approaches to this problem in vision have used linear classifiers to solve specific problems, but these approaches were not general enough to solve complex problems such as reconstructing subjective perceptual states. We have developed a new approach to these problems based on quantitative encoding models that explicitly describe how visual stimuli are (nonlinearly) transformed into brain activity. We then invert these encoding models in order to decode activity evoked by novel images or movies, providing reconstructions with unprecedented fidelity. Here we briefly review these results and the potential uses of perceptual decoding devices.",
keywords = "Bayesian, Brain reading, Brain-computer interface, Brain-machine interface, Vision",
author = "Jack Gallant and Thomas Naselaris and Ryan Prenger and Kendrick Kay and Dustin Stansbury and Michael Oliver and An Vu and Shinji Nishimoto",
year = "2009",
doi = "10.1007/978-3-642-02812-0_46",
language = "English (US)",
isbn = "364202811X",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "390--393",
booktitle = "Foundations of Augmented Cognition",
note = "5th International Conference on Foundations of Augmented Cognition, FAC 2009, Held as Part of HCI International 2009 ; Conference date: 19-07-2009 Through 24-07-2009",
}