Generic decoding of seen and imagined objects using hierarchical visual features

Abstract

Machine learning algorithms can decode objects that people see or imagine from their brain activity. Here the authors present a predictive decoder combined with deep neural network representations that generalizes beyond the training set and correctly identifies novel objects that it has never been trained on.