通用BCI个性化：面向冻结EEG骨干和基础模型的单一API
Universal BCI Personalization: One API for Frozen EEG Trunks and Foundation Models
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.
This report is categorized under AITOP's "Papers" desk. Key entities and technical topics under continuous tracking include Nimbus Personalizer, brain-computer interfaces, EEG, personalization.
Source attribution: curated from arXiv cs.LG (@Sergey Musienko).
