B[FM]${}^2$: Brain Foundation Model via Flow Matching with SplitUNet

EEG foundation models can learn generalizable representations from large-scale EEG corpora to enable single-backbone transfer across diverse clinical and brain-computer interface tasks. Existing models typically discretize the continuous multi-channel EEG waveform into patches or codebook tokens and train a transformer with masked self-supervision. Recognizing that this discretization fragments continuous brain rhythms and obscures fine-grained temporal dynamics, we present B[FM]${}^2$ (Brain Foundation Model via Flow Matching), which drops discretization and pretrains directly on the raw signal using continuous-time flow matching without patches, tokenization, or masking. However, multi-channel EEG signals pose an architectural challenge for flow matching: time is densely sampled and highly autocorrelated (thousands of timepoints), while the electrode axis is short (tens of channels) at distinct scalp positions. To address this time-electrode asymmetry, we introduce SplitUNet, a velocity network that factorizes each block into separate 1D temporal and 1D electrode convolutions and downsamples only along time, preserving electrode topology throughout the hierarchy. B[FM]${}^2$ sets a new state of the art on $7$ of $9$ standard downstream EEG classification tasks, using a pretraining budget of only $36,895$ segments ($\approx 307$ h), a fraction ($\approx 3.3$%) of that required by existing EEG foundation models. It also produces synthetic EEGs that two board-certified neurologists cannot distinguish from real EEGs (Cohen's $\kappa =-0.096$).