  The dispute of how the human brain represents conceptual knowledge has been
argued in many scientific fields. Brain imaging studies have shown that the
spatial patterns of neural activation in the brain are correlated with thinking
about different semantic categories of words (for example, tools, animals, and
buildings) or when viewing the related pictures. In this paper, we present a
computational model that learns to predict the neural activation captured in
functional magnetic resonance imaging (fMRI) data of test words. Unlike the
models with hand-crafted features that have been used in the literature, in
this paper we propose a novel approach wherein decoding models are built with
features extracted from popular linguistic encodings of Word2Vec, GloVe,
Meta-Embeddings in conjunction with the empirical fMRI data associated with
viewing several dozen concrete nouns. We compared these models with several
other models that use word features extracted from FastText, Randomly-generated
features, Mitchell's 25 features [1]. The experimental results show that the
predicted fMRI images using Meta-Embeddings meet the state-of-the-art
performance. Although models with features from GloVe and Word2Vec predict fMRI
images similar to the state-of-the-art model, model with features from
Meta-Embeddings predicts significantly better. The proposed scheme that uses
popular linguistic encoding offers a simple and easy approach for semantic
decoding from fMRI experiments.
