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The exploration of brain activity and its decoding from fMRI data has been alongstanding pursuit, driven by its potential applications in brain-computerinterfaces, medical diagnostics, and virtual reality. Previous approaches haveprimarily focused on individual subject analysis, highlighting the need for amore universal and adaptable framework, which is the core motivation behind ourwork. In this work, we propose fMRI-PTE, an innovative auto-encoder approachfor fMRI pre-training, with a focus on addressing the challenges of varyingfMRI data dimensions due to individual brain differences. Our approach involvestransforming fMRI signals into unified 2D representations, ensuring consistencyin dimensions and preserving distinct brain activity patterns. We introduce anovel learning strategy tailored for pre-training 2D fMRI images, enhancing thequality of reconstruction. fMRI-PTE's adaptability with image generatorsenables the generation of well-represented fMRI features, facilitating variousdownstream tasks, including within-subject and cross-subject brain activitydecoding. Our contributions encompass introducing fMRI-PTE, innovative datatransformation, efficient training, a novel learning strategy, and theuniversal applicability of our approach. Extensive experiments validate andsupport our claims, offering a promising foundation for further research inthis domain.

fMRI-PTE: A Large-scale fMRI Pretrained Transformer Encoder for Multi-Subject Brain Activity Decoding