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arXiv AI By Yunzhen Zhang, Ruoxi Piao, Hasan Onur Keles, Mustafa Misir

EEG-AS: Instance-Level Foundation Model Selection for EEG Foundation Models via Behavior Reconstruction

Read the original on arXiv AI →

EEG-AS is an instance-level algorithm selection framework designed for EEG foundation models. It characterizes each EEG instance using latent embeddings, handcrafted neurophysiological features, and an anchor foundation model, then learns to reconstruct the behaviors of other foundation models from privileged prediction tokens. During inference, EEG-AS estimates these behaviors without running the full model portfolio, enabling efficient selection among seven EEG foundation models and significantly reducing the performance gap between the single best solver and the oracle upper bound across seven public EEG benchmarks.

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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