Aperiodic and Low-Frequency Spectral Bias in Reconstruction based EEG Foundation Models

ArXi:2605.26434v1 Announce Type: cross EEG foundation models, pre-trained on large-scale unlabelled EEG data, have emerged as a promising direction towards learning generalizable EEG representations. Despite showing positive results in data-rich regimes, they often fail to outperform significantly smaller supervised models in low-resource settings compared to fully supervised models.