Work

EEG-FM-Compass: progress, benchmarking and future directions for EEG foundation models

Year: 2026

Type: article

Abstract: ABSTRACT Electroencephalography (EEG) foundation models (FMs) have recently emerged as a promising paradigm for brain–computer interfaces, aiming to learn transferable neural representations from large-scale heterogeneous recordings. Despite rapid progress, a fair and comprehensive comparison of existing EEG FMs is still lacking, owing to inconsistent pre-training objectives, preprocessing choices and downstream evaluation protocols. To fill this gap, we present EEG-FM-Compass. We first review 55 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 FMs and competitive specialist baselines across 13 EEG datasets spanning 9 brain–computer interface 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 (i) linear probing is frequently insufficient; (ii) specialist models trained from scratch remain competitive across many tasks; and (iii) larger FMs do not necessarily yield better generalization performance under current data regimes and training practices.

Source: National Science Review

DOI: 10.1093/nsr/nwag466