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README.md

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+---
+license: bsd-3-clause
+library_name: braindecode
+pipeline_tag: feature-extraction
+tags:
+  - eeg
+  - biosignal
+  - pytorch
+  - neuroscience
+  - eeg-dino
+  - foundation-model
+  - self-supervised
+  - self-distillation
+  - braindecode
+  - model_hub_mixin
+  - pytorch_model_hub_mixin
+---
+
+# 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](https://huggingface.co/spaces/braindecode/OpenEEGBench) effort.
+
+## Quick start
+
+```bash
+pip install braindecode[hub]
+```
+
+```python
+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](https://github.com/braindecode/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](https://huggingface.co/spaces/braindecode/model-explorer).
+
+## 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*.

config.json

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+{
+  "n_outputs": 2,
+  "n_chans": 19,
+  "chs_info": null,
+  "n_times": 1000,
+  "input_window_seconds": null,
+  "sfreq": null,
+  "patch_size": 200,
+  "n_layer": 16,
+  "nhead": 16,
+  "dim_feedforward": 1024,
+  "channels_kernel_stride_padding_norm": [
+    [
+      64,
+      49,
+      25,
+      24,
+      [
+        8,
+        64
+      ]
+    ],
+    [
+      128,
+      3,
+      1,
+      1,
+      [
+        8,
+        128
+      ]
+    ],
+    [
+      64,
+      3,
+      1,
+      1,
+      [
+        8,
+        64
+      ]
+    ]
+  ],
+  "num_channels": 19,
+  "n_global_tokens": 1,
+  "global_token_layer": 1,
+  "activation": "torch.nn.modules.activation.GELU",
+  "drop_prob": 0.1,
+  "return_features": false,
+  "return_encoder_output": false,
+  "braindecode_version": "1.6.1dev0"
+}

model.safetensors

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+oid sha256:9454036a1825cfe980c716a10152f5c15417f3827b7992030bc4ca94536a5dfc
+size 135048840

pytorch_model.bin

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+size 135109751