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arXiv Computation and Language By Ruidong Ma, Jisheng Dang, Ronghao Lin, Bimei Wang, Jingwen Zhao, Wenjun Ma, Wei-Shi Zheng, Meng Wang, Qi Tian, Tat-Seng Chua

EEG-HealthLLM: Hierarchical Adaptation of Large Language Models for Unified Heterogeneous EEG Health Assessment

Read the original on arXiv Computation and Language →

EEG-HealthLLM introduces a hierarchical adaptation framework that unifies heterogeneous EEG health assessments using large language models. Task‑specific EEG adapters preserve unique spatio‑temporal dynamics, while a shared LoRA module in the LLM backbone captures common high‑level assessment semantics. The new EEG‑HealthBench benchmark reorganizes five EEG datasets into five health‑assessment tasks, and experiments show EEG‑HealthLLM achieves the highest average balanced accuracy and macro‑F1, improving performance while reducing adapter size by 80%.

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 Computation and Language.

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