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arXiv Machine Learning By Mingyang Jiang, Yamin Li, Daniel Moyer, Fan Ma, Hua Xu, Catie Chang

Dataset-Dependent Effects of Cross-Depth Aggregation and Soft-Routed Experts in EEG Foundation Model Fine-Tuning

Read the original on arXiv Machine Learning →

The study investigates whether adding specialized modules—cross‑depth Attention Residuals (AttnRes) and soft‑routed expert banks—to a fully fine‑tuned EEG foundation model improves performance on various EEG decoding tasks. Across four datasets (FACED, ISRUC, SEED‑V, PhysioNet‑MI), AttnRes alone boosts mean balanced accuracy on three datasets, while the full model with experts only benefits two. However, these gains come with significant computational overhead, and the effects are dataset‑dependent rather than consistently positive.

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 Machine Learning.

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