  Due to the lack of paired samples and the low signal-to-noise ratio of
functional MRI (fMRI) signals, reconstructing perceived natural images or
decoding their semantic contents from fMRI data are challenging tasks. In this
work, we propose, for the first time, a task-agnostic fMRI-based brain decoding
model, BrainCLIP, which leverages CLIP's cross-modal generalization ability to
bridge the modality gap between brain activity, image, and text. Our
experiments demonstrate that CLIP can act as a pivot for generic brain decoding
tasks, including zero-shot visual categories decoding, fMRI-image/text
matching, and fMRI-to-image generation. Specifically, BrainCLIP aims to train a
mapping network that transforms fMRI patterns into a well-aligned CLIP
embedding space by combining visual and textual supervision. Our experiments
show that this combination can boost the decoding model's performance on
certain tasks like fMRI-text matching and fMRI-to-image generation. On the
zero-shot visual category decoding task, BrainCLIP achieves significantly
better performance than BraVL, a recently proposed multi-modal method
specifically designed for this task. BrainCLIP can also reconstruct visual
stimuli with high semantic fidelity and establishes a new state-of-the-art for
fMRI-based natural image reconstruction in terms of high-level semantic
features.
