HiERA: Hierarchical Domain-aware Reusable Adaptation for Continual Personalization of EEG Foundation Models

EEG foundation models (EFMs) generalize well across EEG analysis and brain-computer interface (BCI) tasks, yet continually personalizing EFMs to a growing stream of subjects remains costly: full fine-tuning is computationally expensive, while per-subject LoRA adapters cause storage to grow rapidly with the number of subjects. We observe that shallow EFM layers encode more domain-level variation shared across subjects, whereas deeper layers increasingly capture subject-specific characteristics. Building on this observation, we propose **HiERA**, a hierarchical adaptation framework that employs a domain-shared Tensor Ring transformation in shallow layers and a compact, reusable adapter repository in deeper layers. New subjects can reuse existing adapters when appropriate, avoiding unnecessary parameter growth. Experiments on multiple public EEG datasets and EFMs show that HiERA maintains competitive personalization performance while substantially reducing trainable and stored parameters compared with other PEFT baselines. Code will be released upon acceptance.