title: Linear reconstruction of perceived images from human brain activity.
abstract: With the advent of sophisticated acquisition and analysis techniques, decoding the contents of someone's experience has become a reality. We propose a straightforward linear Gaussian approach, where decoding relies on the inversion of properly regularized encoding models, which can still be solved analytically. In order to test our approach we acquired functional magnetic resonance imaging data under a rapid event-related design in which subjects were presented with handwritten characters. Our approach is shown to yield state-of-the-art reconstructions of perceived characters as estimated from BOLD responses. This even holds for previously unseen characters. We propose that this framework serves as a baseline with which to compare more sophisticated models for which analytical inversion is infeasible.
authors.affiliation: Radboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands. Electronic address: s.schoenmakers@donders.ru.nl.
authors.forename: Sanne
authors.initials: S
authors.lastname: Schoenmakers
authors.forename: Markus
authors.initials: M
authors.lastname: Barth
authors.forename: Tom
authors.initials: T
authors.lastname: Heskes
authors.forename: Marcel
authors.initials: M
authors.lastname: van Gerven
mesh_terms: D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016014:Linear Models; D008279:Magnetic Resonance Imaging; D008959:Models, Neurological; D014796:Visual Perception / Q000502:physiology*
keywords: Image reconstruction; Linear regression; Regularization; fMRI analysis