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Deep neural networks have been developed drawing inspiration from the brainvisual pathway, implementing an end-to-end approach: from image data to videoobject classes. However building an fMRI decoder with the typical structure ofConvolutional Neural Network (CNN), i.e. learning multiple level ofrepresentations, seems impractical due to lack of brain data. As a possiblesolution, this work presents the first hybrid fMRI and deep features decodingapproach: collected fMRI and deep learnt representations of video objectclasses are linked together by means of Kernel Canonical Correlation Analysis.In decoding, this allows exploiting the discriminatory power of CNN by relatingthe fMRI representation to the last layer of CNN (fc7). We show theeffectiveness of embedding fMRI data onto a subspace related to deep featuresin distinguishing semantic visual categories based solely on brain imagingdata.

Deep driven fMRI decoding of visual categories