Electroencephalography (EEG) foundation models have recently emerged as a promising paradigm for brain-computer interfaces (BCIs), aiming to learn transferable neural representations from large-scale heterogeneous recordings. Despite rapid progresses, there lacks fair and comprehensive comparisons of existing EEG foundation models, due to inconsistent pre-training objectives, preprocessing choices, and downstream evaluation protocols. This paper fills this gap. We first review 50 representative models and organize their design choices into a unified taxonomic framework including data standardization, model architectures, and self-supervised pre-training strategies. We then evaluate 12 open-source foundation models and competitive specialist baselines across 13 EEG datasets spanning nine BCI paradigms. Emphasizing real-world deployments, we consider both cross-subject generalization under a leave-one-subject-out protocol and rapid calibration under a within-subject few-shot setting. We further compare full-parameter fine-tuning with linear probing to assess the transferability of pre-trained representations, and examine the relationship between model scale and downstream performance. Our results indicate that: 1) linear probing is frequently insufficient; 2) specialist models trained from scratch remain competitive across many tasks; and, 3) larger foundation models do not necessarily yield better generalization performance under current data regimes and training practices.

翻译：脑电图（EEG）基础模型近年来已成为脑机接口（BCI）领域一个有前景的范式，其目标是从大规模异构记录中学习可迁移的神经表征。尽管进展迅速，但由于预训练目标、预处理选择以及下游评估协议的不一致，目前仍缺乏对现有EEG基础模型公平且全面的比较。本文填补了这一空白。我们首先回顾了50个代表性模型，并将其设计选择组织成一个统一的分类框架，包括数据标准化、模型架构和自监督预训练策略。随后，我们在涵盖九种BCI范式的13个EEG数据集上，评估了12个开源基础模型以及具有竞争力的专业基线模型。着眼于现实世界部署，我们同时考虑了留一受试者协议下的跨受试者泛化能力，以及受试者内少样本设置下的快速校准能力。我们进一步比较了全参数微调与线性探测，以评估预训练表征的可迁移性，并检验了模型规模与下游性能之间的关系。我们的结果表明：1）线性探测通常是不够的；2）从头开始训练的专业模型在许多任务上仍然具有竞争力；3）在当前的数据规模和训练实践下，更大的基础模型并不一定能带来更好的泛化性能。