Neural decoding is still a challenge and hot topic in neurocomputing science.
Recently, many studies have shown that brain network patterns containing rich
spatial and temporal structure information, which represents the activation
information of brain under external stimuli. %Therefore, the research of
decoding stimuli from brain network received extensive more attention. The
traditional method extracts brain network features directly from the common
machine learning method, then puts these features into the classifier, and
realizes to decode external stimuli. However, this method cannot effectively
extract the multi-dimensional structural information, which is hidden in the
brain network. The tensor researchers show that the tensor decomposition model
can fully mine unique spatio-temporal structure characteristics in
multi-dimensional structure data. This research proposed a stimulus constrained
tensor brain model(STN)which involves the tensor decomposition idea and
stimulus category constraint information. The model was verified on the real
neuroimaging data sets (MEG and fMRI). The experimental results show that the
STN model achieves more than 11.06% and 18.46% on accuracy matrix compared with
others methods on two modal data sets. These results imply the superiority of
extracting discriminative characteristics about STN model, especially for
decoding object stimuli with semantic information.

提出了一种刺激约束的张量脑模型，将张量分解模型与刺激类别约束信息相结合，用于解码带有语义信息的目标刺激，实验结果表明该模型相对于其他方法提高了 11.06％到 18.46％的准确性。