eegdino-medium-pretrained

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

For the full architecture description, parameter table, and references, see the rendered docstring at 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.