File size: 2,725 Bytes
857f1e3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | ---
library_name: braindecode
license: mit
tags:
- braindecode
- eeg
- motor-imagery
- foundation-model
---
# MIRepNet
Braindecode-format re-host of the official MIRepNet checkpoint released by
Liu et al. The checkpoint can be loaded directly through Braindecode's standard
Hugging Face integration:
```python
from braindecode.models import MIRepNet
model = MIRepNet.from_pretrained("braindecode/mirepnet-pretrained")
# Fine-tuning for another task replaces the released three-class head.
model = MIRepNet.from_pretrained(
"braindecode/mirepnet-pretrained",
n_outputs=4,
)
```
## Released configuration
- 45 channels in the order stored in `mirepnet_channels.json`
- 1,000 samples at 250 Hz
- 256-dimensional embedding
- 6 Transformer blocks with 8 heads
- 3-output supervised pretraining head
The paper's 8--30 Hz filtering, resampling, channel-template preparation, and
Euclidean alignment are preprocessing steps and are not performed by the model.
## Provenance and conversion
- Official code: https://github.com/staraink/MIRepNet at revision
`edb80d7605f75ba8b72b417a124cc9db07385f72`
- Official checkpoint: https://huggingface.co/starself/MIRepNet at revision
`9bac0439c0d3e9ffdb40ca675d61a51b439a446e`
- Source file: `MIRepNet.pth`, SHA-256
`432288958007e344a5a84a9ffe9d0e5e5c0cb616aef86c85522375a3f4da9aaf`
All 109 downstream tensors were converted. The 34 pretraining-only tensors
(`mask_token`, `decoder.*`, and the upstream `embedding.chan_embed.weight`,
which is not used by the released forward pass) were intentionally omitted.
Against the official implementation, maximum absolute error was `2.38e-7` for
pooled features and `1.19e-7` for logits. The conversion is reproducible with
`convert_mirepnet_checkpoint.py`.
The source code and checkpoint are distributed under the MIT License. The
original copyright notice is preserved in `LICENSE`.
## Limitations
The official repository does not document the semantic ordering of the three
pretraining-head outputs. Replace the head with `n_outputs=...` and fine-tune it
for downstream use unless that label mapping has been independently verified.
Dataset licenses are separate from the checkpoint's MIT license.
## Citation
```bibtex
@article{LIU2026115966,
title = {MIRepNet: A pipeline and pre-trained model for EEG-based motor imagery classification},
journal = {Knowledge-Based Systems},
volume = {343},
pages = {115966},
year = {2026},
issn = {0950-7051},
doi = {10.1016/j.knosys.2026.115966},
url = {https://www.sciencedirect.com/science/article/pii/S0950705126006921},
author = {Dingkun Liu and Zhu Chen and Jingwei Luo and Shijie Lian and Yuheng Chen and Shaojie Hou and Xiaolian Zhu and Dongrui Wu}
}
```
|