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.

bibtex

@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.