  As a technology to read brain states from measurable brain activities, brain
decoding are widely applied in industries and medical sciences. In spite of
high demands in these applications for a universal decoder that can be applied
to all individuals simultaneously, large variation in brain activities across
individuals has limited the scope of many studies to the development of
individual-specific decoders. In this study, we used deep neural network (DNN),
a nonlinear hierarchical model, to construct a subject-transfer decoder. Our
decoder is the first successful DNN-based subject-transfer decoder. When
applied to a large-scale functional magnetic resonance imaging (fMRI) database,
our DNN-based decoder achieved higher decoding accuracy than other baseline
methods, including support vector machine (SVM). In order to analyze the
knowledge acquired by this decoder, we applied principal sensitivity analysis
(PSA) to the decoder and visualized the discriminative features that are common
to all subjects in the dataset. Our PSA successfully visualized the
subject-independent features contributing to the subject-transferability of the
trained decoder.
