Linear reconstruction of perceived images from human brain activity
until further notice
Number of pages
SourceNeuroImage, 83, (2013), pp. 951-961
Article / Letter to editor
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SW OZ DCC AI
PI Group MR Techniques in Brain Function
Subject150 000 MR Techniques in Brain Function; Cognitive artificial intelligence; DI-BCB_DCC_Theme 4: Brain Networks and Neuronal Communication; Data Science
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.
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