Journal Article

Linear reconstruction of perceived images from human brain activity

Barth, Markus, Schoenmakers, Sanne, Heskes, Tom, van Gerven, Marcel

2013

Overview

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. •We propose a linear Gaussian framework for perceived image reconstruction.•We reconstructed handwritten characters from rapid event related fMRI.•Reconstructions are of high quality, even for previously unseen characters.•The framework is proposed as a baseline with which to compare other approaches.

• Publisher: Elsevier Inc,Elsevier Limited
• Subject: Brain - physiology / Brain mapping / Brain Mapping - methods / fMRI analysis / Humans / Image Processing, Computer-Assisted - methods / Image reconstruction / Linear Models / Linear regression / Magnetic Resonance Imaging / Models, Neurological / Multivariate analysis / Neurosciences / Regression analysis / Regularization / Regularization methods / Visual Perception - physiology