NeuroWeave: Frequency-Factorized Multi-Slot Discrete Targets for Masked EEG Pre-Training

Discrete masked EEG pre-training depends on both target structure and target capacity. A single broadband code must compress heterogeneous rhythms into one discrete decision; increasing its vocabulary does not create independently representable signal components. We introduce \model, which factorizes each one-second channel patch into delta, theta, and alpha bands and represents each band with multiple discrete slots. Independent tokenizers generate frozen targets, and a shared spatiotemporal Transformer predicts them from masked broadband input. We evaluate target design in stages, separating tokenizer capacity, masked reconstruction, target-band selection, and downstream transfer. Increasing slot count improves tokenizer fidelity more than enlarging a single-slot codebook, while masked reconstruction reveals finite, frequency-dependent useful capacity. Under matched controls, frequency-factorized targets improve low-frequency reconstruction over an equally sized broadband VQ target. Downstream ablations select delta, theta, and alpha, whereas beta and gamma provide no consistent benefit. Across six datasets, the resulting three-band model surpasses single-slot, broadband VQ, and waveform-regression controls on every reported metric and achieves state-of-the-art performance on the reported benchmarks. Code will be released.