EEG-DINO: Learning EEG Foundation Models via Hierarchical Self-Distillation

Electroencephalography (EEG) provides a non-invasive window into the brain’s electrical activity, playing an essential role in various brain–computer interface (BCI) and healthcare applications. In this paper, we propose EEG-DINO, a novel foundation model for EEG encoding based on a hierarchical self-distillation framework. By multi-view semantic alignment, the model is able to extract multi-level semantic features from EEG data, which captures a wide range of semantic information, increasing the robustness against noise and variances inherent in complex EEG signals. Moreover, acknowledging the unique heterogeneous spatial-temporal dependencies in EEG signals, we design a channel-aware sampling mechanism and a decoupled positional coding scheme. They independently address spatial and temporal dimensions, enabling the model to capture the intricate structural characteristics of EEG signals. We pre-train EEG-DINO on a large-scale EEG corpus spanning over 9000 hours, which consistently achieves state-of-the-art performance on multiple downstream tasks. These results demonstrate the great effectiveness of our self-distillation framework for EEG encoding.