Bayesian reconstruction of perceptual experiences from human brain activity

Jack Gallant, Thomas Naselaris, Ryan Prenger, Kendrick Kay, Dustin Stansbury, Michael Oliver, An Vu, Shinji Nishimoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationFoundations of Augmented Cognition
Subtitle of host publicationNeuroergonomics and Operational Neuroscience - 5th International Conference, FAC 2009, Held as Part of HCI International 2009, Proceedings
Pages390-393
Number of pages4
DOIs
StatePublished - Dec 1 2009
Event5th International Conference on Foundations of Augmented Cognition, FAC 2009, Held as Part of HCI International 2009 - San Diego, CA, United States
Duration: Jul 19 2009Jul 24 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5638 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other5th International Conference on Foundations of Augmented Cognition, FAC 2009, Held as Part of HCI International 2009
CountryUnited States
CitySan Diego, CA
Period7/19/097/24/09

Keywords

  • Bayesian
  • Brain reading
  • Brain-computer interface
  • Brain-machine interface
  • Vision

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