  The exploration of brain activity and its decoding from fMRI data has been a
longstanding pursuit, driven by its potential applications in brain-computer
interfaces, medical diagnostics, and virtual reality. Previous approaches have
primarily focused on individual subject analysis, highlighting the need for a
more universal and adaptable framework, which is the core motivation behind our
work. In this work, we propose fMRI-PTE, an innovative auto-encoder approach
for fMRI pre-training, with a focus on addressing the challenges of varying
fMRI data dimensions due to individual brain differences. Our approach involves
transforming fMRI signals into unified 2D representations, ensuring consistency
in dimensions and preserving distinct brain activity patterns. We introduce a
novel learning strategy tailored for pre-training 2D fMRI images, enhancing the
quality of reconstruction. fMRI-PTE's adaptability with image generators
enables the generation of well-represented fMRI features, facilitating various
downstream tasks, including within-subject and cross-subject brain activity
decoding. Our contributions encompass introducing fMRI-PTE, innovative data
transformation, efficient training, a novel learning strategy, and the
universal applicability of our approach. Extensive experiments validate and
support our claims, offering a promising foundation for further research in
this domain.
