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

arXiv:2605.26434v1 Announce Type: new Abstract: 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. We provide a mechanistic account of this shortcoming, attributing it to a fundamental mismatch between reconstruction-based pretext tasks and the idiosyncratic spectral structure of EEG signals, which dec

Source: arXiv cs.LG — read the full report at the original publisher.

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