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README.md

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+---
+language: en
+license: mit
+tags:
+  - eeg
+  - foundation-model
+  - BCI
+  - neuroscience
+  - burn
+  - rust
+  - safetensors
+base_model: eegdino/EEG-DINO
+library_name: burn
+pipeline_tag: feature-extraction
+---
+
+# EEG-DINO (safetensors, Burn-compatible)
+
+Converted weights for the [EEG-DINO](https://github.com/miraclefish/EEG-DINO) foundation model, packaged as safetensors for use with the [`eegdino`](https://crates.io/crates/eegdino) Rust inference crate.
+
+**Derived from:** [eegdino/EEG-DINO](https://huggingface.co/eegdino/EEG-DINO)
+
+## Files
+
+| File | Model | Params | d_model | Heads | Layers | Size |
+|------|-------|--------|---------|-------|--------|------|
+| `eeg_dino_small.safetensors` | Small | 4.6 M | 200 | 8 | 12 | 17 MB |
+| `eeg_dino_medium.safetensors` | Medium | 33 M | 512 | 16 | 16 | 129 MB |
+| `eeg_dino_large.safetensors` | Large | 201 M | 1 024 | 16 | 24 | 770 MB |
+
+## What changed from the original weights
+
+The original PyTorch `.pt` checkpoints from [eegdino/EEG-DINO](https://huggingface.co/eegdino/EEG-DINO) were converted with the following transformations:
+
+1. **Filtered** --- only student encoder weights are kept; teacher, projector, and loss tensors are discarded
+2. **Renamed** --- `module.student.` prefix stripped; Sequential indices mapped to descriptive names (e.g. `proj_in.0` → `proj_in.conv1`)
+3. **Transposed** --- linear weight matrices converted from PyTorch `[out, in]` to Burn `[in, out]` layout
+4. **Format** --- saved as float32 safetensors (no bf16)
+
+The conversion script is at [`scripts/convert_weights.py`](https://github.com/eugenehp/eegdino-rs/blob/main/scripts/convert_weights.py).
+
+## Usage with Rust
+
+```toml
+# Cargo.toml
+[dependencies]
+eegdino = "0.1"
+burn = { version = "0.20", features = ["ndarray"] }
+```
+
+```rust
+use eegdino_rs::prelude::*;
+use burn::backend::NdArray;
+
+type B = NdArray;
+
+let encoder = EegDinoEncoder::<B>::builder()
+    .weights("eeg_dino_small.safetensors")
+    .device(Default::default())
+    .build()
+    .unwrap();
+
+// 19 channels, 10 seconds @ 200 Hz
+let signal = vec![0.0f32; 19 * 2000];
+let result = encoder.encode_raw(&signal, 1, 19, 2000).unwrap();
+// result.shape == [1, 191, 200]
+```
+
+## Download
+
+```bash
+# All weights
+hf download eugenehp/eegdino --local-dir weights
+
+# Single model
+hf download eugenehp/eegdino eeg_dino_small.safetensors --local-dir weights
+```
+
+## Numerical parity
+
+These converted weights produce outputs with NRMSE < 1e-6 compared to the original PyTorch model on identical inputs:
+
+| Model | Max abs error | NRMSE |
+|-------|---------------|-------|
+| Small | 8.5e-7 | 5.5e-7 |
+| Medium | 2.1e-6 | 8.8e-7 |
+| Large | 4.8e-6 | 5.9e-7 |
+
+## Citation
+
+If you use these weights, please cite the original EEG-DINO paper:
+
+```bibtex
+@article{jiang2024eegdino,
+    title={Learning EEG Foundation Models via Hierarchical Self-Distillation},
+    author={Jiang, Yuqi and others},
+    year={2024}
+}
+```
+
+## Links
+
+- **Original model:** [eegdino/EEG-DINO](https://huggingface.co/eegdino/EEG-DINO)
+- **Original code:** [github.com/miraclefish/EEG-DINO](https://github.com/miraclefish/EEG-DINO)
+- **Rust crate:** [crates.io/crates/eegdino](https://crates.io/crates/eegdino)
+- **Rust repo:** [github.com/eugenehp/eegdino-rs](https://github.com/eugenehp/eegdino-rs)
+
+## License
+
+MIT (conversion and crate). Original model weights are subject to the [EEG-DINO license](https://huggingface.co/eegdino/EEG-DINO).

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