发表机构
The Hong Kong University of Science and Technology（香港科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过对比群体模型与交换适配器，在三个运动想象数据集上评估了三个冻结脑电基础模型的个人适配，发现个性化增益真实存在但受群体训练预算影响，且廉价获取不可靠，强调需多参照系评估。
AI 中文摘要
基础模型越来越多地被适配到个体用户，但表面上的个性化增益可能仅仅反映了更强的群体模型。这一区分对于脑机接口至关重要，因为每个新用户都必须进行校准。我们在来自三个运动想象数据集的235名留出受试者中，评估了三个冻结的脑电基础模型（CBraMod、REVE和LaBraM）的个人适配，将每个受试者的适配器与群体模型以及适配到其他受试者的适配器进行比较。使用所有前半部分会话标签，个人适配器在平均平衡准确率上比群体模型提高了1.5-5.4个百分点，并在所有九种模型-数据集组合中优于交换适配器2.3-7.3个百分点。该收益的大小取决于群体训练：当使用原始预算的四倍时，中位增益仍为正值（1.0-2.0个百分点），但每个模型的增益都更小，且没有群体模型达到确认的平台期。廉价地获取该收益并不可靠：少标签校准仅在其中一个数据集上始终非负，且在CBraMod中，无标签上下文和元学习初始化均未优于匹配的对照。因此，个性化应同时针对群体参考和交换参数，在不同群体训练预算下进行评估。
英文摘要
Foundation models are increasingly adapted to individual users, but an apparent personalization gain can simply reflect a stronger population model. This distinction matters for brain-computer interfaces, where every new user must be calibrated. We evaluated personal adaptation of three frozen EEG foundation models (CBraMod, REVE and LaBraM) in 235 held-out subjects from three motor-imagery datasets, comparing each subject's adapter with the population model and with adapters fitted to other subjects. Using all first-half session labels, personal adapters improved mean balanced accuracy over the population model by 1.5-5.4 percentage points and outperformed exchanged adapters by 2.3-7.3 points in all nine model-dataset combinations. The size of this benefit depended on population training: with four times the original budget, median gains remained positive (1.0-2.0 points) but were smaller for every model, and no population model reached a confirmed plateau. Acquiring the benefit cheaply was unreliable: few-label calibration was consistently non-negative on only one dataset, and in CBraMod neither unlabeled context nor meta-learned initialization outperformed matched controls. Personalization should therefore be evaluated against both a population reference and exchanged parameters, across population-training budgets.
Comments59 pages, 19 figures (6 main, 13 supplementary), 33 tables (3 main, 30 supplementary). Includes supplementary information and source data. Code: https://github.com/gaivrt/eeg-personal-population-benefit
