It is meaningful to decode the semantic information
from functional magnetic resonance imaging (fMRI) brain signals
evoked by natural images. Semantic decoding can be viewed
as a classification problem. Since a natural image may contain
many semantic information of different objects, the single label
classification model is not appropriate to cope with semantic
decoding problem, which motivates the multi-label classification
model. However, most multi-label models always treat each label
equally. Actually, if dataset is associated with a large number of
semantic labels, it will be difficult to get an accurate prediction
of semantic label when the label appears with a low frequency
in this dataset. So we should increase the relative importance
degree to the labels that associate with little instances. In order
to improve multi-label prediction performance, in this paper, we
firstly propose a multinomial label distribution to estimate the
importance degree of each associated label for an instance by
using conditional probability, and then establish a deep neural
network (DNN) based model which contains both multinomial
label distribution and label co-occurrence information to realize
the multi-label classification of semantic information in fMRI
brain signals. Experiments on three fMRI recording datasets
demonstrate that our approach performs better than the stateof-the-art methods on semantic information prediction.