  Self-supervised learning has emerged as a highly effective approach in the
fields of natural language processing and computer vision. It is also
applicable to brain signals such as electroencephalography (EEG) data, given
the abundance of available unlabeled data that exist in a wide spectrum of
real-world medical applications ranging from seizure detection to wave
analysis. The existing works leveraging self-supervised learning on EEG
modeling mainly focus on pretraining upon each individual dataset corresponding
to a single downstream task, which cannot leverage the power of abundant data,
and they may derive sub-optimal solutions with a lack of generalization.
Moreover, these methods rely on end-to-end model learning which is not easy for
humans to understand. In this paper, we present a novel EEG foundation model,
namely EEGFormer, pretrained on large-scale compound EEG data. The pretrained
model cannot only learn universal representations on EEG signals with adaptable
performance on various downstream tasks but also provide interpretable outcomes
of the useful patterns within the data. To validate the effectiveness of our
model, we extensively evaluate it on various downstream tasks and assess the
performance under different transfer settings. Furthermore, we demonstrate how
the learned model exhibits transferable anomaly detection performance and
provides valuable interpretability of the acquired patterns via self-supervised
learning.
