这篇论文给BCI个性化提供了一个通用API，不用每个模型另起炉灶，在5种骨干和4个数据集上都跑通了，效率还比微调高。
Nimbus Personalizer提出了一种与骨干无关的API，可在EEGNet、Shallow、Deep、Conformer、ATCNet五种经典骨干以及REVE基础编码器上统一运行，无需为每种架构设计新的个性化堆栈。在四个MI数据集的18个实验单元中，该API通过廉价默认头部恢复了大部分微调精度，适应时间降低多个数量级。校准仅当数据干净时有效，在12/18个单元中成立。所有结果基于受试者自助法，无确认性检验，适用范围由配套工作提供决策逻辑。
Universal BCI Personalization: One API for Frozen EEG Trunks and Foundation Models
Frozen EEG encoders proliferate; per-model fine-tune defaults do not scale. We present Nimbus Personalizer: one contract encode to Bayesian head to BrainState (optional affine mid-tier) that sits on heterogeneous frozen trunks without a new personalization stack per architecture. Thesis (systems): the contribution is the trunk-agnostic API - not LDA-on-embeddings as an ML novelty - so OEMs integrate once and swap trunks. Evidence: the same surface runs on five classical trunks EEGNet, Shallow, Deep, Conformer, ATCNet x four MI datasets (18 cells) and on a foundation encoder (REVE) under the same Personalizer. Where embedding capacity exists, the head is a cheap default mid-point versus warm-start fine-tune or PEFT, costing orders of magnitude less adaptation wall time while recovering much of the fine-tune accuracy gain; calibration-only-when-clean holds in 12/18 cells. Head gains are supporting evidence that the API is useful where capacity exists. Subject-level confidence intervals identify the clearest dataset and span zero elsewhere. All results are exploratory (subject-level bootstrap, no confirmatory tests); the decision logic for when to escalate adaptation is addressed in our companion work on the control layer.