腦電圖基礎模型：進展、基準測試與開放性問題

EEG Foundation Models: Progresses, Benchmarking, and Open Problems

January 25, 2026

作者: Dingkun Liu, Yuheng Chen, Zhu Chen, Zhenyao Cui, Yaozhi Wen, Jiayu An, Jingwei Luo, Dongrui Wu

cs.AI

摘要

近期，脑电图（EEG）基础模型已成为脑机接口（BCI）领域极具前景的研究范式，其目标是从大规模异构记录数据中学习可迁移的神经表征。尽管发展迅速，但由于预训练目标、预处理方法及下游评估协议的不一致，现有EEG基础模型尚缺乏公平全面的比较研究。本文旨在填补这一空白。我们首先回顾了50个代表性模型，并将其设计选择归纳为统一分类框架，涵盖数据标准化、模型架构和自监督预训练策略三大维度。随后，我们在涵盖九类BCI范式的13个EEG数据集上，系统评估了12个开源基础模型及具有竞争力的专业基线模型。为强调实际部署需求，我们同时考察了留一被试协议下的跨被试泛化能力，以及被试内少样本场景下的快速校准性能。通过对比全参数微调与线性探测两种迁移方式，我们评估了预训练表征的可迁移性，并探究了模型规模与下游性能的关联。实验结果表明：1）线性探测常不足以充分挖掘模型潜力；2）从头训练的专业模型在多数任务中仍具竞争力；3）在当前数据规模与训练范式下，扩大基础模型规模未必能提升泛化性能。

English

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.