SpecBraM: What Should an EEG Foundation Model Predict? Masked Band-Power Prediction versus Waveform Reconstruction

Peng Xie, Yequan Bie, Jianda Mao, Kani Chen (Hong Kong University of Science and Technology)

arXiv 2610.07484· 2026
EEGFoundation Model

Summary

A controlled study of the masked-modeling target. Same 12-layer criss-cross transformer, data and step budget; the only change is what masked patches predict. Instead of reconstructing the raw waveform (CBraMod, REVE) or VQ codes (LaBraM), SpecBraM predicts log energy in five fixed frequency bands per masked channel-time patch, dropping phase that can't be inferred from neighbours.

Reported benchmarks

Sleep staging

hmc · Bal. accuracy ↑

#1 of 1
0.7425

HMC sleep staging, EEG, frozen features + strict linear probe, mean ± sd over 3 pretrain seeds (±0.0045); full fine-tune 0.7669

Sleep staging

isruc-s1 · Bal. accuracy ↑

#1 of 1
0.7916

ISRUC subgroup I, EEG, frozen features + strict linear probe, mean ± sd over 3 pretrain seeds (±0.0081); full fine-tune 0.8107

Updated: Oct 7, 2026