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arXiv AI By Kieren Yu, Ziyang Liu, Chang Huang, Jintai Chen, Kaishun Wu

Neural State Prediction: Obstructing Shortcut Learning in EEG Foundation Models

Read the original on arXiv AI →

Neural State Prediction (NSP) is a latent‑predictive framework designed to curb shortcut learning in EEG foundation models. By using a target encoder updated with an exponential moving average, identity residualization, and topology‑separated context, NSP constrains both the prediction target and the available context. Trained on 2.2 million EEG segments, NSP outperforms baselines on 14 datasets in the EEG‑FM‑Bench, achieving 63.94 % macro balanced accuracy.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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