eugenehp/eegdino

EEG-DINO (safetensors, Burn-compatible)

Converted weights for the EEG-DINO foundation model, packaged as safetensors for use with the eegdino Rust inference crate.

Derived from: 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 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.

Usage with Rust

# Cargo.toml
[dependencies]
eegdino = "0.1"
burn = { version = "0.20", features = ["ndarray"] }

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

# 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:

@article{jiang2024eegdino,
    title={Learning EEG Foundation Models via Hierarchical Self-Distillation},
    author={Jiang, Yuqi and others},
    year={2024}
}

Links

• Original model: eegdino/EEG-DINO
• Original code: github.com/miraclefish/EEG-DINO
• Rust crate: crates.io/crates/eegdino
• Rust repo: github.com/eugenehp/eegdino-rs

License

MIT (conversion and crate). Original model weights are subject to the EEG-DINO license.