  Among the most impressive recent applications of neural decoding is the
visual representation decoding, where the category of an object that a subject
either sees or imagines is inferred by observing his/her brain activity. Even
though there is an increasing interest in the aforementioned visual
representation decoding task, there is no extensive study of the effect of
using different machine learning models on the decoding accuracy. In this paper
we provide an extensive evaluation of several machine learning models, along
with different similarity metrics, for the aforementioned task, drawing many
interesting conclusions. That way, this paper a) paves the way for developing
more advanced and accurate methods and b) provides an extensive and easily
reproducible baseline for the aforementioned decoding task.

Neural and Evolutionary Computing;Machine Learning;Neurons and Cognition