EEG-DINO Medium — Self-Distillation EEG Foundation Model
EEG-DINO-Medium encoder (~33M parameters) pretrained with DINO-v2 hierarchical self-distillation (Wang et al., MICCAI 2025).
This is the eegdino-medium-pretrained checkpoint for braindecode.models.EEGDINO,
curated and re-uploaded as part of the
OpenEEG-Bench effort.
Quick start
pip install braindecode[hub]
from braindecode.models import EEGDINO
model = EEGDINO.from_pretrained(
"braindecode/eegdino-medium-pretrained",
n_outputs=2, # set to your downstream task
n_chans=19,
sfreq=200,
)
from_pretrained reads both the architecture configuration (config.json)
and the weights (model.safetensors or pytorch_model.bin) and returns a
ready-to-fine-tune nn.Module.
Model details
| Architecture | braindecode.models.EEGDINO |
| Expected channels | 19 |
| Expected sampling frequency | 200 Hz |
| Library | braindecode ≥ 1.5 |
| Loaded via | huggingface_hub.PyTorchModelHubMixin (free with braindecode[hub]) |
For the full architecture description, parameter table, and references, see the rendered docstring at https://braindecode.org/stable/generated/braindecode.models.EEGDINO.html or in the interactive Model Explorer Space.
Training data
Temple University Hospital EEG Corpus (TUEG), 19 common 10-20 channels resampled to 200 Hz (>9000 hours), following CBraMod's preprocessing. Pretrained by hierarchical self-distillation.
Intended use
Larger EEG-DINO encoder for feature extraction or fine-tuning; the architecture is restored from config.json on load. The classification head is re-initialized on load.
Limitations
- Channel layout matters. Performance degrades when the input montage
differs from the pretraining montage. Use the
Interpolated*variant (where available) or resample channels with MNE before fine-tuning. - Sampling rate matters. Resample your data to 200 Hz before inference; the positional / patch embeddings assume this rate.
- Inherited license restrictions. Downstream weights derived from this checkpoint inherit the license of the original training corpus (some braindecode pretraining corpora are CC-BY-NC). Verify the upstream dataset licence before commercial use.
Citation
If you use this checkpoint, please cite both the original architecture paper and braindecode.
@inproceedings{wang2025eegdino,
title = {{EEG-DINO}: Learning {EEG} Foundation Models via Hierarchical
Self-Distillation},
author = {Wang, Xujia and Liu, Xuhui and Liu, Xi and Si, Qian and Xu,
Zhaoliang and Li, Yang and Zhen, Xiantong},
booktitle = {Medical Image Computing and Computer Assisted Intervention (MICCAI)},
year = {2025},
}
@article{aristimunha2025braindecode,
title = {Braindecode: a deep learning library for raw electrophysiological data},
author = {Aristimunha, Bruno and others},
journal = {Zenodo},
year = {2025},
doi = {10.5281/zenodo.17699192},
}
License
BSD-3-Clause for the model code (matching braindecode). The pretraining data may impose additional restrictions — see Limitations.