EEG Foundation Models: Progresses, Benchmarking, and Open Problems
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.
697d56216676f933227061d1
Dingkun Liu
claimed_verified
2026-02-02T16:58:04.035Z
6889e312c7792882e9128b29
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/kVizdw-Yfu0rDrf3W6CmW.png
Dingkun Liu
DiMaria0817
user
DiMaria0817
697d56216676f933227061d2
Yuheng Chen
697d56216676f933227061d3
Zhu Chen
697d56216676f933227061d4
Zhenyao Cui
697d56216676f933227061d5
Yaozhi Wen
697d56216676f933227061d6
Jiayu An
697d56216676f933227061d7
Jingwei Luo
697d56216676f933227061d8
Dongrui Wu
EEG Foundation Models: Progresses, Benchmarking, and Open Problems
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.
EEG foundation models show mixed performance compared to specialist models, with larger models not always providing better generalization under current data regimes.
electroencephalography brain-computer interfaces foundation models pre-training objectives self-supervised pre-training cross-subject generalization few-shot learning linear probing model scale downstream performance