Empirical evaluation of GMM, GAN, and VAE models for synthesizing diverse task-dependent fMRI images shows they can augment classifiers with performance gains complementary to the choice of predictive model.
Deep driven fMRI decoding of visual categories
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abstract
Deep neural networks have been developed drawing inspiration from the brain visual pathway, implementing an end-to-end approach: from image data to video object classes. However building an fMRI decoder with the typical structure of Convolutional Neural Network (CNN), i.e. learning multiple level of representations, seems impractical due to lack of brain data. As a possible solution, this work presents the first hybrid fMRI and deep features decoding approach: collected fMRI and deep learnt representations of video object classes are linked together by means of Kernel Canonical Correlation Analysis. In decoding, this allows exploiting the discriminatory power of CNN by relating the fMRI representation to the last layer of CNN (fc7). We show the effectiveness of embedding fMRI data onto a subspace related to deep features in distinguishing semantic visual categories based solely on brain imaging data.
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cs.CV 1years
2019 1verdicts
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FMRI data augmentation via synthesis
Empirical evaluation of GMM, GAN, and VAE models for synthesizing diverse task-dependent fMRI images shows they can augment classifiers with performance gains complementary to the choice of predictive model.