A Linear Gaussian Framework for Decoding of Perceived Images

With the advent of sophisticated acquisition and analysis techniques, decoding the contents of someone´s experience has become a reality. We propose a simple linear Gaussian framework where decoding relies on the inversion of properly regularized encoding models. We show that this approach yields state-of-the-art decoding performance on an fMRI dataset.