FlatClip: A Geometry-Aware Surface-Level Baseline for fMRI Representation Learning
Converting brain scans into flat maps to decode brain activity with simpler models
Researchers created FlatClip, a method that flattens the brain's curved surface into a 2D map and feeds it to an off-the-shelf image recognition model to decode fMRI brain activity. The approach sits between inefficient coarse methods and complex voxel-level models, performing competitively on standard benchmarks while requiring no brain-imaging-specific training.
Most brain-imaging AI models either sacrifice detail for simplicity or demand expensive specialized training. FlatClip shows that reusing existing image-recognition tools on geometry-aware brain maps could lower the barrier to building practical brain decoders, potentially accelerating research into reading brain activity for applications like brain-computer interfaces or clinical diagnosis.