  The study of decoding visual neural information faces challenges in
generalizing single-subject decoding models to multiple subjects, due to
individual differences. Moreover, the limited availability of data from a
single subject has a constraining impact on model performance. Although prior
multi-subject decoding methods have made significant progress, they still
suffer from several limitations, including difficulty in extracting global
neural response features, linear scaling of model parameters with the number of
subjects, and inadequate characterization of the relationship between neural
responses of different subjects to various stimuli. To overcome these
limitations, we propose a CLIP-guided Multi-sUbject visual neural information
SEmantic Decoding (CLIP-MUSED) method. Our method consists of a
Transformer-based feature extractor to effectively model global neural
representations. It also incorporates learnable subject-specific tokens that
facilitates the aggregation of multi-subject data without a linear increase of
parameters. Additionally, we employ representational similarity analysis (RSA)
to guide token representation learning based on the topological relationship of
visual stimuli in the representation space of CLIP, enabling full
characterization of the relationship between neural responses of different
subjects under different stimuli. Finally, token representations are used for
multi-subject semantic decoding. Our proposed method outperforms single-subject
decoding methods and achieves state-of-the-art performance among the existing
multi-subject methods on two fMRI datasets. Visualization results provide
insights into the effectiveness of our proposed method. Code is available at
https://github.com/CLIP-MUSED/CLIP-MUSED.
